517 lines
156 KiB
XML
517 lines
156 KiB
XML
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<channel>
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<title>Hugging Face Daily Papers</title>
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<link>https://huggingface.co/papers</link>
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<description>Daily research papers curated by the Hugging Face community.</description>
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<docs>http://www.rssboard.org/rss-specification</docs>
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<generator>python-feedgen</generator>
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<language>en</language>
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<lastBuildDate>Mon, 05 May 2025 00:11:45 +0000</lastBuildDate>
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<item>
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<title>DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency</title>
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<link>https://arxiv.org/abs/2504.12080</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.12080.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mengshi Qi, Pengfei Zhu, Xiangtai Li, Xiaoyang Bi, Lu Qi, Huadong Ma, Ming-Hsuan Yang</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Given a single labeled example, in-context segmentation aims to segment corresponding objects. This setting, known as one-shot segmentation in few-shot learning, explores the segmentation model's generalization ability and has been applied to various vision tasks, including scene understanding and image/video editing. While recent Segment Anything Models have achieved state-of-the-art results in interactive segmentation, these approaches are not directly applicable to in-context segmentation. In this work, we propose the Dual Consistency SAM (DC-SAM) method based on prompt-tuning to adapt SAM and SAM2 for in-context segmentation of both images and videos. Our key insights are to enhance the features of the SAM's prompt encoder in segmentation by providing high-quality visual prompts. When generating a mask prior, we fuse the SAM features to better align the prompt encoder. Then, we design a cycle-consistent cross-attention on fused features and initial visual prompts. Next, a dual-branch design is provided by using the discriminative positive and negative prompts in the prompt encoder. Furthermore, we design a simple mask-tube training strategy to adopt our proposed dual consistency method into the mask tube. Although the proposed DC-SAM is primarily designed for images, it can be seamlessly extended to the video domain with the support of SAM2. Given the absence of in-context segmentation in the video domain, we manually curate and construct the first benchmark from existing video segmentation datasets, named In-Context Video Object Segmentation (IC-VOS), to better assess the in-context capability of the model. Extensive experiments demonstrate that our method achieves 55.5 (+1.4) mIoU on COCO-20i, 73.0 (+1.1) mIoU on PASCAL-5i, and a J&F score of 71.52 on the proposed IC-VOS benchmark. Our source code and benchmark are available at https://github.com/zaplm/DC-SAM.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.12080</guid>
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<pubDate>Wed, 16 Apr 2025 13:41:59 +0000</pubDate>
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</item>
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<item>
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<title>Towards Understanding Camera Motions in Any Video</title>
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<link>https://arxiv.org/abs/2504.15376</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.15376.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhiqiu Lin, Siyuan Cen, Daniel Jiang, Jay Karhade, Hewei Wang, Chancharik Mitra, Tiffany Ling, Yuhan Huang, Sifan Liu, Mingyu Chen, Rushikesh Zawar, Xue Bai, Yilun Du, Chuang Gan, Deva Ramanan</p><p><b>Upvotes:</b> 150</p><p><b>Summary:</b> We introduce CameraBench, a large-scale dataset and benchmark designed to assess and improve camera motion understanding. CameraBench consists of ~3,000 diverse internet videos, annotated by experts through a rigorous multi-stage quality control process. One of our contributions is a taxonomy of camera motion primitives, designed in collaboration with cinematographers. We find, for example, that some motions like "follow" (or tracking) require understanding scene content like moving subjects. We conduct a large-scale human study to quantify human annotation performance, revealing that domain expertise and tutorial-based training can significantly enhance accuracy. For example, a novice may confuse zoom-in (a change of intrinsics) with translating forward (a change of extrinsics), but can be trained to differentiate the two. Using CameraBench, we evaluate Structure-from-Motion (SfM) and Video-Language Models (VLMs), finding that SfM models struggle to capture semantic primitives that depend on scene content, while VLMs struggle to capture geometric primitives that require precise estimation of trajectories. We then fine-tune a generative VLM on CameraBench to achieve the best of both worlds and showcase its applications, including motion-augmented captioning, video question answering, and video-text retrieval. We hope our taxonomy, benchmark, and tutorials will drive future efforts towards the ultimate goal of understanding camera motions in any video.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.15376</guid>
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<pubDate>Mon, 21 Apr 2025 18:34:57 +0000</pubDate>
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</item>
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<item>
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<title>DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models</title>
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<link>https://arxiv.org/abs/2504.15716</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.15716.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jie Zhu, Qian Chen, Huaixia Dou, Junhui Li, Lifan Guo, Feng Chen, Chi Zhang</p><p><b>Upvotes:</b> 9</p><p><b>Summary:</b> Effective reasoning remains a core challenge for large language models (LLMs) in the financial domain, where tasks often require domain-specific knowledge, precise numerical calculations, and strict adherence to compliance rules. We propose DianJin-R1, a reasoning-enhanced framework designed to address these challenges through reasoning-augmented supervision and reinforcement learning. Central to our approach is DianJin-R1-Data, a high-quality dataset constructed from CFLUE, FinQA, and a proprietary compliance corpus (Chinese Compliance Check, CCC), combining diverse financial reasoning scenarios with verified annotations. Our models, DianJin-R1-7B and DianJin-R1-32B, are fine-tuned from Qwen2.5-7B-Instruct and Qwen2.5-32B-Instruct using a structured format that generates both reasoning steps and final answers. To further refine reasoning quality, we apply Group Relative Policy Optimization (GRPO), a reinforcement learning method that incorporates dual reward signals: one encouraging structured outputs and another rewarding answer correctness. We evaluate our models on five benchmarks: three financial datasets (CFLUE, FinQA, and CCC) and two general reasoning benchmarks (MATH-500 and GPQA-Diamond). Experimental results show that DianJin-R1 models consistently outperform their non-reasoning counterparts, especially on complex financial tasks. Moreover, on the real-world CCC dataset, our single-call reasoning models match or even surpass the performance of multi-agent systems that require significantly more computational cost. These findings demonstrate the effectiveness of DianJin-R1 in enhancing financial reasoning through structured supervision and reward-aligned learning, offering a scalable and practical solution for real-world applications.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.15716</guid>
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<pubDate>Tue, 22 Apr 2025 09:01:04 +0000</pubDate>
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<title>TrustGeoGen: Scalable and Formal-Verified Data Engine for Trustworthy Multi-modal Geometric Problem Solving</title>
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<link>https://arxiv.org/abs/2504.15780</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.15780.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Daocheng Fu, Zijun Chen, Renqiu Xia, Qi Liu, Yuan Feng, Hongbin Zhou, Renrui Zhang, Shiyang Feng, Peng Gao, Junchi Yan, Botian Shi, Bo Zhang, Yu Qiao</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Mathematical geometric problem solving (GPS) often requires effective integration of multimodal information and verifiable logical coherence. Despite the fast development of large language models in general problem solving, it remains unresolved regarding with both methodology and benchmarks, especially given the fact that exiting synthetic GPS benchmarks are often not self-verified and contain noise and self-contradicted information due to the illusion of LLMs. In this paper, we propose a scalable data engine called TrustGeoGen for problem generation, with formal verification to provide a principled benchmark, which we believe lays the foundation for the further development of methods for GPS. The engine synthesizes geometric data through four key innovations: 1) multimodal-aligned generation of diagrams, textual descriptions, and stepwise solutions; 2) formal verification ensuring rule-compliant reasoning paths; 3) a bootstrapping mechanism enabling complexity escalation via recursive state generation and 4) our devised GeoExplore series algorithms simultaneously produce multi-solution variants and self-reflective backtracking traces. By formal logical verification, TrustGeoGen produces GeoTrust-200K dataset with guaranteed modality integrity, along with GeoTrust-test testset. Experiments reveal the state-of-the-art models achieve only 49.17\% accuracy on GeoTrust-test, demonstrating its evaluation stringency. Crucially, models trained on GeoTrust achieve OOD generalization on GeoQA, significantly reducing logical inconsistencies relative to pseudo-label annotated by OpenAI-o1. Our code is available at https://github.com/Alpha-Innovator/TrustGeoGen</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.15780</guid>
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<pubDate>Tue, 22 Apr 2025 10:45:23 +0000</pubDate>
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<title>Certified Mitigation of Worst-Case LLM Copyright Infringement</title>
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<link>https://arxiv.org/abs/2504.16046</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.16046.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jingyu Zhang, Jiacan Yu, Marc Marone, Benjamin Van Durme, Daniel Khashabi</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> The exposure of large language models (LLMs) to copyrighted material during pre-training raises concerns about unintentional copyright infringement post deployment. This has driven the development of "copyright takedown" methods, post-training approaches aimed at preventing models from generating content substantially similar to copyrighted ones. While current mitigation approaches are somewhat effective for average-case risks, we demonstrate that they overlook worst-case copyright risks exhibits by the existence of long, verbatim quotes from copyrighted sources. We propose BloomScrub, a remarkably simple yet highly effective inference-time approach that provides certified copyright takedown. Our method repeatedly interleaves quote detection with rewriting techniques to transform potentially infringing segments. By leveraging efficient data sketches (Bloom filters), our approach enables scalable copyright screening even for large-scale real-world corpora. When quotes beyond a length threshold cannot be removed, the system can abstain from responding, offering certified risk reduction. Experimental results show that BloomScrub reduces infringement risk, preserves utility, and accommodates different levels of enforcement stringency with adaptive abstention. Our results suggest that lightweight, inference-time methods can be surprisingly effective for copyright prevention.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.16046</guid>
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<pubDate>Tue, 22 Apr 2025 17:16:53 +0000</pubDate>
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<title>MMInference: Accelerating Pre-filling for Long-Context VLMs via Modality-Aware Permutation Sparse Attention</title>
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<link>https://arxiv.org/abs/2504.16083</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.16083.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yucheng Li, Huiqiang Jiang, Chengruidong Zhang, Qianhui Wu, Xufang Luo, Surin Ahn, Amir H. Abdi, Dongsheng Li, Jianfeng Gao, Yuqing Yang, Lili Qiu</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> The integration of long-context capabilities with visual understanding unlocks unprecedented potential for Vision Language Models (VLMs). However, the quadratic attention complexity during the pre-filling phase remains a significant obstacle to real-world deployment. To overcome this limitation, we introduce MMInference (Multimodality Million tokens Inference), a dynamic sparse attention method that accelerates the prefilling stage for long-context multi-modal inputs. First, our analysis reveals that the temporal and spatial locality of video input leads to a unique sparse pattern, the Grid pattern. Simultaneously, VLMs exhibit markedly different sparse distributions across different modalities. We introduce a permutation-based method to leverage the unique Grid pattern and handle modality boundary issues. By offline search the optimal sparse patterns for each head, MMInference constructs the sparse distribution dynamically based on the input. We also provide optimized GPU kernels for efficient sparse computations. Notably, MMInference integrates seamlessly into existing VLM pipelines without any model modifications or fine-tuning. Experiments on multi-modal benchmarks-including Video QA, Captioning, VisionNIAH, and Mixed-Modality NIAH-with state-of-the-art long-context VLMs (LongVila, LlavaVideo, VideoChat-Flash, Qwen2.5-VL) show that MMInference accelerates the pre-filling stage by up to 8.3x at 1M tokens while maintaining accuracy. Our code is available at https://aka.ms/MMInference.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.16083</guid>
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<pubDate>Tue, 22 Apr 2025 17:59:51 +0000</pubDate>
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<title>Learning Explainable Dense Reward Shapes via Bayesian Optimization</title>
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<link>https://arxiv.org/abs/2504.16272</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.16272.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ryan Koo, Ian Yang, Vipul Raheja, Mingyi Hong, Kwang-Sung Jun, Dongyeop Kang</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Current reinforcement learning from human feedback (RLHF) pipelines for large language model (LLM) alignment typically assign scalar rewards to sequences, using the final token as a surrogate indicator for the quality of the entire sequence. However, this leads to sparse feedback and suboptimal token-level credit assignment. In this work, we frame reward shaping as an optimization problem focused on token-level credit assignment. We propose a reward-shaping function leveraging explainability methods such as SHAP and LIME to estimate per-token rewards from the reward model. To learn parameters of this shaping function, we employ a bilevel optimization framework that integrates Bayesian Optimization and policy training to handle noise from the token reward estimates. Our experiments show that achieving a better balance of token-level reward attribution leads to performance improvements over baselines on downstream tasks and finds an optimal policy faster during training. Furthermore, we show theoretically that explainability methods that are feature additive attribution functions maintain the optimal policy as the original reward.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.16272</guid>
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<pubDate>Tue, 22 Apr 2025 21:09:33 +0000</pubDate>
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<title>Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark</title>
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<link>https://arxiv.org/abs/2504.16427</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.16427.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hanlei Zhang, Zhuohang Li, Yeshuang Zhu, Hua Xu, Peiwu Wang, Haige Zhu, Jie Zhou, Jinchao Zhang</p><p><b>Upvotes:</b> 17</p><p><b>Summary:</b> Multimodal language analysis is a rapidly evolving field that leverages multiple modalities to enhance the understanding of high-level semantics underlying human conversational utterances. Despite its significance, little research has investigated the capability of multimodal large language models (MLLMs) to comprehend cognitive-level semantics. In this paper, we introduce MMLA, a comprehensive benchmark specifically designed to address this gap. MMLA comprises over 61K multimodal utterances drawn from both staged and real-world scenarios, covering six core dimensions of multimodal semantics: intent, emotion, dialogue act, sentiment, speaking style, and communication behavior. We evaluate eight mainstream branches of LLMs and MLLMs using three methods: zero-shot inference, supervised fine-tuning, and instruction tuning. Extensive experiments reveal that even fine-tuned models achieve only about 60%~70% accuracy, underscoring the limitations of current MLLMs in understanding complex human language. We believe that MMLA will serve as a solid foundation for exploring the potential of large language models in multimodal language analysis and provide valuable resources to advance this field. The datasets and code are open-sourced at https://github.com/thuiar/MMLA.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.16427</guid>
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<pubDate>Wed, 23 Apr 2025 05:25:13 +0000</pubDate>
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<title>Subject-driven Video Generation via Disentangled Identity and Motion</title>
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<link>https://arxiv.org/abs/2504.17816</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.17816.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Daneul Kim, Jingxu Zhang, Wonjoon Jin, Sunghyun Cho, Qi Dai, Jaesik Park, Chong Luo</p><p><b>Upvotes:</b> 11</p><p><b>Summary:</b> We propose to train a subject-driven customized video generation model through decoupling the subject-specific learning from temporal dynamics in zero-shot without additional tuning. A traditional method for video customization that is tuning-free often relies on large, annotated video datasets, which are computationally expensive and require extensive annotation. In contrast to the previous approach, we introduce the use of an image customization dataset directly on training video customization models, factorizing the video customization into two folds: (1) identity injection through image customization dataset and (2) temporal modeling preservation with a small set of unannotated videos through the image-to-video training method. Additionally, we employ random image token dropping with randomized image initialization during image-to-video fine-tuning to mitigate the copy-and-paste issue. To further enhance learning, we introduce stochastic switching during joint optimization of subject-specific and temporal features, mitigating catastrophic forgetting. Our method achieves strong subject consistency and scalability, outperforming existing video customization models in zero-shot settings, demonstrating the effectiveness of our framework.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.17816</guid>
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<pubDate>Wed, 23 Apr 2025 06:48:31 +0000</pubDate>
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<title>Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning</title>
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<link>https://arxiv.org/abs/2504.16656</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.16656.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chris, Yichen Wei, Yi Peng, Xiaokun Wang, Weijie Qiu, Wei Shen, Tianyidan Xie, Jiangbo Pei, Jianhao Zhang, Yunzhuo Hao, Xuchen Song, Yang Liu, Yahui Zhou</p><p><b>Upvotes:</b> 53</p><p><b>Summary:</b> We present Skywork R1V2, a next-generation multimodal reasoning model and a major leap forward from its predecessor, Skywork R1V. At its core, R1V2 introduces a hybrid reinforcement learning paradigm that harmonizes reward-model guidance with rule-based strategies, thereby addressing the long-standing challenge of balancing sophisticated reasoning capabilities with broad generalization. To further enhance training efficiency, we propose the Selective Sample Buffer (SSB) mechanism, which effectively counters the ``Vanishing Advantages'' dilemma inherent in Group Relative Policy Optimization (GRPO) by prioritizing high-value samples throughout the optimization process. Notably, we observe that excessive reinforcement signals can induce visual hallucinations--a phenomenon we systematically monitor and mitigate through calibrated reward thresholds throughout the training process. Empirical results affirm the exceptional capability of R1V2, with benchmark-leading performances such as 62.6 on OlympiadBench, 79.0 on AIME2024, 63.6 on LiveCodeBench, and 74.0 on MMMU. These results underscore R1V2's superiority over existing open-source models and demonstrate significant progress in closing the performance gap with premier proprietary systems, including Gemini 2.5 and OpenAI o4-mini. The Skywork R1V2 model weights have been publicly released to promote openness and reproducibility https://huggingface.co/Skywork/Skywork-R1V2-38B.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.16656</guid>
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<pubDate>Wed, 23 Apr 2025 12:24:10 +0000</pubDate>
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<title>VideoVista-CulturalLingo: 360^circ Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension</title>
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<link>https://arxiv.org/abs/2504.17821</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.17821.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xinyu Chen, Yunxin Li, Haoyuan Shi, Baotian Hu, Wenhan Luo, Yaowei Wang, Min Zhang</p><p><b>Upvotes:</b> 21</p><p><b>Summary:</b> Assessing the video comprehension capabilities of multimodal AI systems can effectively measure their understanding and reasoning abilities. Most video evaluation benchmarks are limited to a single language, typically English, and predominantly feature videos rooted in Western cultural contexts. In this paper, we present VideoVista-CulturalLingo, the first video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. Our work differs from existing benchmarks in the following ways: 1) Cultural diversity, incorporating cultures from China, North America, and Europe; 2) Multi-linguistics, with questions presented in Chinese and English-two of the most widely spoken languages; and 3) Broad domain, featuring videos sourced from hundreds of human-created domains. VideoVista-CulturalLingo contains 1,389 videos and 3,134 QA pairs, and we have evaluated 24 recent open-source or proprietary video large models. From the experiment results, we observe that: 1) Existing models perform worse on Chinese-centric questions than Western-centric ones, particularly those related to Chinese history; 2) Current open-source models still exhibit limitations in temporal understanding, especially in the Event Localization task, achieving a maximum score of only 45.2%; 3) Mainstream models demonstrate strong performance in general scientific questions, while open-source models demonstrate weak performance in mathematics.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.17821</guid>
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<pubDate>Wed, 23 Apr 2025 13:47:30 +0000</pubDate>
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<title>Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation</title>
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<link>https://arxiv.org/abs/2504.17025</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.17025.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Luca Moroni, Giovanni Puccetti, Pere-Lluis Huguet Cabot, Andrei Stefan Bejgu, Edoardo Barba, Alessio Miaschi, Felice Dell'Orletta, Andrea Esuli, Roberto Navigli</p><p><b>Upvotes:</b> 16</p><p><b>Summary:</b> The number of pretrained Large Language Models (LLMs) is increasing steadily, though the majority are designed predominantly for the English language. While state-of-the-art LLMs can handle other languages, due to language contamination or some degree of multilingual pretraining data, they are not optimized for non-English languages, leading to inefficient encoding (high token "fertility") and slower inference speed. In this work, we thoroughly compare a variety of vocabulary adaptation techniques for optimizing English LLMs for the Italian language, and put forward Semantic Alignment Vocabulary Adaptation (SAVA), a novel method that leverages neural mapping for vocabulary substitution. SAVA achieves competitive performance across multiple downstream tasks, enhancing grounded alignment strategies. We adapt two LLMs: Mistral-7b-v0.1, reducing token fertility by 25\%, and Llama-3.1-8B, optimizing the vocabulary and reducing the number of parameters by 1 billion. We show that, following the adaptation of the vocabulary, these models can recover their performance with a relatively limited stage of continual training on the target language. Finally, we test the capabilities of the adapted models on various multi-choice and generative tasks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.17025</guid>
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<pubDate>Wed, 23 Apr 2025 18:12:27 +0000</pubDate>
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<title>Group Downsampling with Equivariant Anti-aliasing</title>
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<link>https://arxiv.org/abs/2504.17258</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.17258.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Md Ashiqur Rahman, Raymond A. Yeh</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Downsampling layers are crucial building blocks in CNN architectures, which help to increase the receptive field for learning high-level features and reduce the amount of memory/computation in the model. In this work, we study the generalization of the uniform downsampling layer for group equivariant architectures, e.g., G-CNNs. That is, we aim to downsample signals (feature maps) on general finite groups with anti-aliasing. This involves the following: (a) Given a finite group and a downsampling rate, we present an algorithm to form a suitable choice of subgroup. (b) Given a group and a subgroup, we study the notion of bandlimited-ness and propose how to perform anti-aliasing. Notably, our method generalizes the notion of downsampling based on classical sampling theory. When the signal is on a cyclic group, i.e., periodic, our method recovers the standard downsampling of an ideal low-pass filter followed by a subsampling operation. Finally, we conducted experiments on image classification tasks demonstrating that the proposed downsampling operation improves accuracy, better preserves equivariance, and reduces model size when incorporated into G-equivariant networks</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.17258</guid>
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<pubDate>Thu, 24 Apr 2025 05:29:51 +0000</pubDate>
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<title>Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency</title>
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<link>https://arxiv.org/abs/2504.18589</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18589.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhikai Wang, Jiashuo Sun, Wenqi Zhang, Zhiqiang Hu, Xin Li, Fan Wang, Deli Zhao</p><p><b>Upvotes:</b> 9</p><p><b>Summary:</b> Recent advancements in Large Vision-Language Models (LVLMs) have significantly enhanced their ability to integrate visual and linguistic information, achieving near-human proficiency in tasks like object recognition, captioning, and visual question answering. However, current benchmarks typically focus on knowledge-centric evaluations that assess domain-specific expertise, often neglecting the core ability to reason about fundamental mathematical elements and visual concepts. We identify a gap in evaluating elementary-level math problems, which rely on explicit visual dependencies-requiring models to discern, integrate, and reason across multiple images while incorporating commonsense knowledge, all of which are crucial for advancing toward broader AGI capabilities. To address this gap, we introduce VCBENCH, a comprehensive benchmark for multimodal mathematical reasoning with explicit visual dependencies. VCBENCH includes 1,720 problems across six cognitive domains, featuring 6,697 images (averaging 3.9 per question) to ensure multi-image reasoning. We evaluate 26 state-of-the-art LVLMs on VCBENCH, revealing substantial performance disparities, with even the top models unable to exceed 50% accuracy. Our findings highlight the ongoing challenges in visual-mathematical integration and suggest avenues for future LVLM advancements.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18589</guid>
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<pubDate>Thu, 24 Apr 2025 06:16:38 +0000</pubDate>
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<title>The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs</title>
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<link>https://arxiv.org/abs/2504.17768</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.17768.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Piotr Nawrot, Robert Li, Renjie Huang, Sebastian Ruder, Kelly Marchisio, Edoardo M. Ponti</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> Sparse attention offers a promising strategy to extend long-context capabilities in Transformer LLMs, yet its viability, its efficiency-accuracy trade-offs, and systematic scaling studies remain unexplored. To address this gap, we perform a careful comparison of training-free sparse attention methods at varying model scales, sequence lengths, and sparsity levels on a diverse collection of long-sequence tasks-including novel ones that rely on natural language while remaining controllable and easy to evaluate. Based on our experiments, we report a series of key findings: 1) an isoFLOPS analysis reveals that for very long sequences, larger and highly sparse models are preferable to smaller and dense ones. 2) The level of sparsity attainable while statistically guaranteeing accuracy preservation is higher during decoding than prefilling, and correlates with model size in the former. 3) There is no clear strategy that performs best across tasks and phases, with different units of sparsification or budget adaptivity needed for different scenarios. Even moderate sparsity levels often result in significant performance degradation on at least one task, highlighting that sparse attention is not a universal solution. 4) We introduce and validate novel scaling laws specifically tailored for sparse attention, providing evidence that our findings are likely to hold true beyond our range of experiments. Through these insights, we demonstrate that sparse attention is a key tool to enhance the capabilities of Transformer LLMs for processing longer sequences, but requires careful evaluation of trade-offs for performance-sensitive applications.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.17768</guid>
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<pubDate>Thu, 24 Apr 2025 17:39:25 +0000</pubDate>
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<title>CaRL: Learning Scalable Planning Policies with Simple Rewards</title>
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<link>https://arxiv.org/abs/2504.17838</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.17838.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Bernhard Jaeger, Daniel Dauner, Jens Beißwenger, Simon Gerstenecker, Kashyap Chitta, Andreas Geiger</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> We investigate reinforcement learning (RL) for privileged planning in autonomous driving. State-of-the-art approaches for this task are rule-based, but these methods do not scale to the long tail. RL, on the other hand, is scalable and does not suffer from compounding errors like imitation learning. Contemporary RL approaches for driving use complex shaped rewards that sum multiple individual rewards, \eg~progress, position, or orientation rewards. We show that PPO fails to optimize a popular version of these rewards when the mini-batch size is increased, which limits the scalability of these approaches. Instead, we propose a new reward design based primarily on optimizing a single intuitive reward term: route completion. Infractions are penalized by terminating the episode or multiplicatively reducing route completion. We find that PPO scales well with higher mini-batch sizes when trained with our simple reward, even improving performance. Training with large mini-batch sizes enables efficient scaling via distributed data parallelism. We scale PPO to 300M samples in CARLA and 500M samples in nuPlan with a single 8-GPU node. The resulting model achieves 64 DS on the CARLA longest6 v2 benchmark, outperforming other RL methods with more complex rewards by a large margin. Requiring only minimal adaptations from its use in CARLA, the same method is the best learning-based approach on nuPlan. It scores 91.3 in non-reactive and 90.6 in reactive traffic on the Val14 benchmark while being an order of magnitude faster than prior work.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.17838</guid>
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<pubDate>Thu, 24 Apr 2025 17:56:01 +0000</pubDate>
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<title>RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning</title>
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<link>https://arxiv.org/abs/2504.20073</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20073.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, Eli Gottlieb, Monica Lam, Yiping Lu, Kyunghyun Cho, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi, Manling Li</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Training large language models (LLMs) as interactive agents presents unique challenges including long-horizon decision making and interacting with stochastic environment feedback. While reinforcement learning (RL) has enabled progress in static tasks, multi-turn agent RL training remains underexplored. We propose StarPO (State-Thinking-Actions-Reward Policy Optimization), a general framework for trajectory-level agent RL, and introduce RAGEN, a modular system for training and evaluating LLM agents. Our study on three stylized environments reveals three core findings. First, our agent RL training shows a recurring mode of Echo Trap where reward variance cliffs and gradient spikes; we address this with StarPO-S, a stabilized variant with trajectory filtering, critic incorporation, and decoupled clipping. Second, we find the shaping of RL rollouts would benefit from diverse initial states, medium interaction granularity and more frequent sampling. Third, we show that without fine-grained, reasoning-aware reward signals, agent reasoning hardly emerge through multi-turn RL and they may show shallow strategies or hallucinated thoughts. Code and environments are available at https://github.com/RAGEN-AI/RAGEN.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20073</guid>
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<pubDate>Thu, 24 Apr 2025 17:57:08 +0000</pubDate>
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<title>Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation</title>
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<link>https://arxiv.org/abs/2504.18087</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18087.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Weipeng Tan, Chuming Lin, Chengming Xu, FeiFan Xu, Xiaobin Hu, Xiaozhong Ji, Junwei Zhu, Chengjie Wang, Yanwei Fu</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Recent advances in Talking Head Generation (THG) have achieved impressive lip synchronization and visual quality through diffusion models; yet existing methods struggle to generate emotionally expressive portraits while preserving speaker identity. We identify three critical limitations in current emotional talking head generation: insufficient utilization of audio's inherent emotional cues, identity leakage in emotion representations, and isolated learning of emotion correlations. To address these challenges, we propose a novel framework dubbed as DICE-Talk, following the idea of disentangling identity with emotion, and then cooperating emotions with similar characteristics. First, we develop a disentangled emotion embedder that jointly models audio-visual emotional cues through cross-modal attention, representing emotions as identity-agnostic Gaussian distributions. Second, we introduce a correlation-enhanced emotion conditioning module with learnable Emotion Banks that explicitly capture inter-emotion relationships through vector quantization and attention-based feature aggregation. Third, we design an emotion discrimination objective that enforces affective consistency during the diffusion process through latent-space classification. Extensive experiments on MEAD and HDTF datasets demonstrate our method's superiority, outperforming state-of-the-art approaches in emotion accuracy while maintaining competitive lip-sync performance. Qualitative results and user studies further confirm our method's ability to generate identity-preserving portraits with rich, correlated emotional expressions that naturally adapt to unseen identities.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18087</guid>
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<pubDate>Fri, 25 Apr 2025 05:28:21 +0000</pubDate>
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<title>Even Small Reasoners Should Quote Their Sources: Introducing the Pleias-RAG Model Family</title>
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<link>https://arxiv.org/abs/2504.18225</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18225.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Pierre-Carl Langlais, Pavel Chizhov, Mattia Nee, Carlos Rosas Hinostroza, Matthieu Delsart, Irène Girard, Othman Hicheur, Anastasia Stasenko, Ivan P. Yamshchikov</p><p><b>Upvotes:</b> 11</p><p><b>Summary:</b> We introduce a new generation of small reasoning models for RAG, search, and source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-trained on a large synthetic dataset emulating the retrieval of a wide variety of multilingual open sources from the Common Corpus. They provide native support for citation and grounding with literal quotes and reintegrate multiple features associated with RAG workflows, such as query routing, query reformulation, and source reranking. Pleias-RAG-350m and Pleias-RAG-1B outperform SLMs below 4 billion parameters on standardized RAG benchmarks (HotPotQA, 2wiki) and are competitive with popular larger models, including Qwen-2.5-7B, Llama-3.1-8B, and Gemma-3-4B. They are the only SLMs to date maintaining consistent RAG performance across leading European languages and ensuring systematic reference grounding for statements. Due to their size and ease of deployment on constrained infrastructure and higher factuality by design, the models unlock a range of new use cases for generative AI.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18225</guid>
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<pubDate>Fri, 25 Apr 2025 10:17:04 +0000</pubDate>
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<title>BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs</title>
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<link>https://arxiv.org/abs/2504.18415</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18415.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hongyu Wang, Shuming Ma, Furu Wei</p><p><b>Upvotes:</b> 41</p><p><b>Summary:</b> Efficient deployment of 1-bit Large Language Models (LLMs) is hindered by activation outliers, which complicate quantization to low bit-widths. We introduce BitNet v2, a novel framework enabling native 4-bit activation quantization for 1-bit LLMs. To tackle outliers in attention and feed-forward network activations, we propose H-BitLinear, a module applying an online Hadamard transformation prior to activation quantization. This transformation smooths sharp activation distributions into more Gaussian-like forms, suitable for low-bit representation. Experiments show BitNet v2 trained from scratch with 8-bit activations matches BitNet b1.58 performance. Crucially, BitNet v2 achieves minimal performance degradation when trained with native 4-bit activations, significantly reducing memory footprint and computational cost for batched inference.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18415</guid>
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<pubDate>Fri, 25 Apr 2025 15:17:52 +0000</pubDate>
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<title>Kimi-Audio Technical Report</title>
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<link>https://arxiv.org/abs/2504.18425</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18425.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> KimiTeam, Ding Ding, Zeqian Ju, Yichong Leng, Songxiang Liu, Tong Liu, Zeyu Shang, Kai Shen, Wei Song, Xu Tan, Heyi Tang, Zhengtao Wang, Chu Wei, Yifei Xin, Xinran Xu, Jianwei Yu, Yutao Zhang, Xinyu Zhou, Y. Charles, Jun Chen, Yanru Chen, Yulun Du, Weiran He, Zhenxing Hu, Guokun Lai, Qingcheng Li, Yangyang Liu, Weidong Sun, Jianzhou Wang, Yuzhi Wang, Yuefeng Wu, Yuxin Wu, Dongchao Yang, Hao Yang, Ying Yang, Zhilin Yang, Aoxiong Yin, Ruibin Yuan, Yutong Zhang, Zaida Zhou</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> We present Kimi-Audio, an open-source audio foundation model that excels in audio understanding, generation, and conversation. We detail the practices in building Kimi-Audio, including model architecture, data curation, training recipe, inference deployment, and evaluation. Specifically, we leverage a 12.5Hz audio tokenizer, design a novel LLM-based architecture with continuous features as input and discrete tokens as output, and develop a chunk-wise streaming detokenizer based on flow matching. We curate a pre-training dataset that consists of more than 13 million hours of audio data covering a wide range of modalities including speech, sound, and music, and build a pipeline to construct high-quality and diverse post-training data. Initialized from a pre-trained LLM, Kimi-Audio is continual pre-trained on both audio and text data with several carefully designed tasks, and then fine-tuned to support a diverse of audio-related tasks. Extensive evaluation shows that Kimi-Audio achieves state-of-the-art performance on a range of audio benchmarks including speech recognition, audio understanding, audio question answering, and speech conversation. We release the codes, model checkpoints, as well as the evaluation toolkits in https://github.com/MoonshotAI/Kimi-Audio.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18425</guid>
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<pubDate>Fri, 25 Apr 2025 15:31:46 +0000</pubDate>
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<title>Spatial Speech Translation: Translating Across Space With Binaural Hearables</title>
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<link>https://arxiv.org/abs/2504.18715</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18715.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tuochao Chen, Qirui Wang, Runlin He, Shyam Gollakota</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Imagine being in a crowded space where people speak a different language and having hearables that transform the auditory space into your native language, while preserving the spatial cues for all speakers. We introduce spatial speech translation, a novel concept for hearables that translate speakers in the wearer's environment, while maintaining the direction and unique voice characteristics of each speaker in the binaural output. To achieve this, we tackle several technical challenges spanning blind source separation, localization, real-time expressive translation, and binaural rendering to preserve the speaker directions in the translated audio, while achieving real-time inference on the Apple M2 silicon. Our proof-of-concept evaluation with a prototype binaural headset shows that, unlike existing models, which fail in the presence of interference, we achieve a BLEU score of up to 22.01 when translating between languages, despite strong interference from other speakers in the environment. User studies further confirm the system's effectiveness in spatially rendering the translated speech in previously unseen real-world reverberant environments. Taking a step back, this work marks the first step towards integrating spatial perception into speech translation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18715</guid>
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<pubDate>Fri, 25 Apr 2025 21:58:56 +0000</pubDate>
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<title>A Review of 3D Object Detection with Vision-Language Models</title>
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<link>https://arxiv.org/abs/2504.18738</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18738.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ranjan Sapkota, Konstantinos I Roumeliotis, Rahul Harsha Cheppally, Marco Flores Calero, Manoj Karkee</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> This review provides a systematic analysis of comprehensive survey of 3D object detection with vision-language models(VLMs) , a rapidly advancing area at the intersection of 3D vision and multimodal AI. By examining over 100 research papers, we provide the first systematic analysis dedicated to 3D object detection with vision-language models. We begin by outlining the unique challenges of 3D object detection with vision-language models, emphasizing differences from 2D detection in spatial reasoning and data complexity. Traditional approaches using point clouds and voxel grids are compared to modern vision-language frameworks like CLIP and 3D LLMs, which enable open-vocabulary detection and zero-shot generalization. We review key architectures, pretraining strategies, and prompt engineering methods that align textual and 3D features for effective 3D object detection with vision-language models. Visualization examples and evaluation benchmarks are discussed to illustrate performance and behavior. Finally, we highlight current challenges, such as limited 3D-language datasets and computational demands, and propose future research directions to advance 3D object detection with vision-language models. >Object Detection, Vision-Language Models, Agents, VLMs, LLMs, AI</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18738</guid>
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<pubDate>Fri, 25 Apr 2025 23:27:26 +0000</pubDate>
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<title>RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning</title>
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<link>https://arxiv.org/abs/2504.18904</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18904.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haoran Geng, Feishi Wang, Songlin Wei, Yuyang Li, Bangjun Wang, Boshi An, Charlie Tianyue Cheng, Haozhe Lou, Peihao Li, Yen-Jen Wang, Yutong Liang, Dylan Goetting, Chaoyi Xu, Haozhe Chen, Yuxi Qian, Yiran Geng, Jiageng Mao, Weikang Wan, Mingtong Zhang, Jiangran Lyu, Siheng Zhao, Jiazhao Zhang, Jialiang Zhang, Chengyang Zhao, Haoran Lu, Yufei Ding, Ran Gong, Yuran Wang, Yuxuan Kuang, Ruihai Wu, Baoxiong Jia, Carlo Sferrazza, Hao Dong, Siyuan Huang, Yue Wang, Jitendra Malik, Pieter Abbeel</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Data scaling and standardized evaluation benchmarks have driven significant advances in natural language processing and computer vision. However, robotics faces unique challenges in scaling data and establishing evaluation protocols. Collecting real-world data is resource-intensive and inefficient, while benchmarking in real-world scenarios remains highly complex. Synthetic data and simulation offer promising alternatives, yet existing efforts often fall short in data quality, diversity, and benchmark standardization. To address these challenges, we introduce RoboVerse, a comprehensive framework comprising a simulation platform, a synthetic dataset, and unified benchmarks. Our simulation platform supports multiple simulators and robotic embodiments, enabling seamless transitions between different environments. The synthetic dataset, featuring high-fidelity physics and photorealistic rendering, is constructed through multiple approaches. Additionally, we propose unified benchmarks for imitation learning and reinforcement learning, enabling evaluation across different levels of generalization. At the core of the simulation platform is MetaSim, an infrastructure that abstracts diverse simulation environments into a universal interface. It restructures existing simulation environments into a simulator-agnostic configuration system, as well as an API aligning different simulator functionalities, such as launching simulation environments, loading assets with initial states, stepping the physics engine, etc. This abstraction ensures interoperability and extensibility. Comprehensive experiments demonstrate that RoboVerse enhances the performance of imitation learning, reinforcement learning, world model learning, and sim-to-real transfer. These results validate the reliability of our dataset and benchmarks, establishing RoboVerse as a robust solution for advancing robot learning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18904</guid>
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<pubDate>Sat, 26 Apr 2025 12:31:04 +0000</pubDate>
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<title>Clinical knowledge in LLMs does not translate to human interactions</title>
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<link>https://arxiv.org/abs/2504.18919</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18919.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Andrew M. Bean, Rebecca Payne, Guy Parsons, Hannah Rose Kirk, Juan Ciro, Rafael Mosquera, Sara Hincapié Monsalve, Aruna S. Ekanayaka, Lionel Tarassenko, Luc Rocher, Adam Mahdi</p><p><b>Upvotes:</b> 23</p><p><b>Summary:</b> Global healthcare providers are exploring use of large language models (LLMs) to provide medical advice to the public. LLMs now achieve nearly perfect scores on medical licensing exams, but this does not necessarily translate to accurate performance in real-world settings. We tested if LLMs can assist members of the public in identifying underlying conditions and choosing a course of action (disposition) in ten medical scenarios in a controlled study with 1,298 participants. Participants were randomly assigned to receive assistance from an LLM (GPT-4o, Llama 3, Command R+) or a source of their choice (control). Tested alone, LLMs complete the scenarios accurately, correctly identifying conditions in 94.9% of cases and disposition in 56.3% on average. However, participants using the same LLMs identified relevant conditions in less than 34.5% of cases and disposition in less than 44.2%, both no better than the control group. We identify user interactions as a challenge to the deployment of LLMs for medical advice. Standard benchmarks for medical knowledge and simulated patient interactions do not predict the failures we find with human participants. Moving forward, we recommend systematic human user testing to evaluate interactive capabilities prior to public deployments in healthcare.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18919</guid>
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<pubDate>Sat, 26 Apr 2025 13:32:49 +0000</pubDate>
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<title>LawFlow : Collecting and Simulating Lawyers' Thought Processes</title>
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<link>https://arxiv.org/abs/2504.18942</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18942.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Debarati Das, Khanh Chi Le, Ritik Sachin Parkar, Karin De Langis, Brendan Madson, Chad M. Berryman, Robin M. Willis, Daniel H. Moses, Brett McDonnell, Daniel Schwarcz, Dongyeop Kang</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Legal practitioners, particularly those early in their careers, face complex, high-stakes tasks that require adaptive, context-sensitive reasoning. While AI holds promise in supporting legal work, current datasets and models are narrowly focused on isolated subtasks and fail to capture the end-to-end decision-making required in real-world practice. To address this gap, we introduce LawFlow, a dataset of complete end-to-end legal workflows collected from trained law students, grounded in real-world business entity formation scenarios. Unlike prior datasets focused on input-output pairs or linear chains of thought, LawFlow captures dynamic, modular, and iterative reasoning processes that reflect the ambiguity, revision, and client-adaptive strategies of legal practice. Using LawFlow, we compare human and LLM-generated workflows, revealing systematic differences in structure, reasoning flexibility, and plan execution. Human workflows tend to be modular and adaptive, while LLM workflows are more sequential, exhaustive, and less sensitive to downstream implications. Our findings also suggest that legal professionals prefer AI to carry out supportive roles, such as brainstorming, identifying blind spots, and surfacing alternatives, rather than executing complex workflows end-to-end. Building on these findings, we propose a set of design suggestions, rooted in empirical observations, that align AI assistance with human goals of clarity, completeness, creativity, and efficiency, through hybrid planning, adaptive execution, and decision-point support. Our results highlight both the current limitations of LLMs in supporting complex legal workflows and opportunities for developing more collaborative, reasoning-aware legal AI systems. All data and code are available on our project page (https://minnesotanlp.github.io/LawFlow-website/).</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18942</guid>
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<pubDate>Sat, 26 Apr 2025 15:01:55 +0000</pubDate>
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<title>MediAug: Exploring Visual Augmentation in Medical Imaging</title>
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<link>https://arxiv.org/abs/2504.18983</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.18983.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xuyin Qi, Zeyu Zhang, Canxuan Gang, Hao Zhang, Lei Zhang, Zhiwei Zhang, Yang Zhao</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Data augmentation is essential in medical imaging for improving classification accuracy, lesion detection, and organ segmentation under limited data conditions. However, two significant challenges remain. First, a pronounced domain gap between natural photographs and medical images can distort critical disease features. Second, augmentation studies in medical imaging are fragmented and limited to single tasks or architectures, leaving the benefits of advanced mix-based strategies unclear. To address these challenges, we propose a unified evaluation framework with six mix-based augmentation methods integrated with both convolutional and transformer backbones on brain tumour MRI and eye disease fundus datasets. Our contributions are threefold. (1) We introduce MediAug, a comprehensive and reproducible benchmark for advanced data augmentation in medical imaging. (2) We systematically evaluate MixUp, YOCO, CropMix, CutMix, AugMix, and SnapMix with ResNet-50 and ViT-B backbones. (3) We demonstrate through extensive experiments that MixUp yields the greatest improvement on the brain tumor classification task for ResNet-50 with 79.19% accuracy and SnapMix yields the greatest improvement for ViT-B with 99.44% accuracy, and that YOCO yields the greatest improvement on the eye disease classification task for ResNet-50 with 91.60% accuracy and CutMix yields the greatest improvement for ViT-B with 97.94% accuracy. Code will be available at https://github.com/AIGeeksGroup/MediAug.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.18983</guid>
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<pubDate>Sat, 26 Apr 2025 17:56:56 +0000</pubDate>
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<title>Selecting Optimal Candidate Profiles in Adversarial Environments Using Conjoint Analysis and Machine Learning</title>
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<link>https://arxiv.org/abs/2504.19043</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19043.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Connor T. Jerzak, Priyanshi Chandra, Rishi Hazra</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Conjoint analysis, an application of factorial experimental design, is a popular tool in social science research for studying multidimensional preferences. In such experiments in the political analysis context, respondents are asked to choose between two hypothetical political candidates with randomly selected features, which can include partisanship, policy positions, gender and race. We consider the problem of identifying optimal candidate profiles. Because the number of unique feature combinations far exceeds the total number of observations in a typical conjoint experiment, it is impossible to determine the optimal profile exactly. To address this identification challenge, we derive an optimal stochastic intervention that represents a probability distribution of various attributes aimed at achieving the most favorable average outcome. We first consider an environment where one political party optimizes their candidate selection. We then move to the more realistic case where two political parties optimize their own candidate selection simultaneously and in opposition to each other. We apply the proposed methodology to an existing candidate choice conjoint experiment concerning vote choice for US president. We find that, in contrast to the non-adversarial approach, expected outcomes in the adversarial regime fall within range of historical electoral outcomes, with optimal strategies suggested by the method more likely to match the actual observed candidates compared to strategies derived from a non-adversarial approach. These findings indicate that incorporating adversarial dynamics into conjoint analysis may yield unique insight into social science data from experiments.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19043</guid>
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<pubDate>Sat, 26 Apr 2025 22:35:58 +0000</pubDate>
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<title>Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions</title>
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<link>https://arxiv.org/abs/2504.19056</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19056.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mohammad Mahdi Abootorabi, Omid Ghahroodi, Pardis Sadat Zahraei, Hossein Behzadasl, Alireza Mirrokni, Mobina Salimipanah, Arash Rasouli, Bahar Behzadipour, Sara Azarnoush, Benyamin Maleki, Erfan Sadraiye, Kiarash Kiani Feriz, Mahdi Teymouri Nahad, Ali Moghadasi, Abolfazl Eshagh Abianeh, Nizi Nazar, Hamid R. Rabiee, Mahdieh Soleymani Baghshah, Meisam Ahmadi, Ehsaneddin Asgari</p><p><b>Upvotes:</b> 12</p><p><b>Summary:</b> Generative AI is reshaping art, gaming, and most notably animation. Recent breakthroughs in foundation and diffusion models have reduced the time and cost of producing animated content. Characters are central animation components, involving motion, emotions, gestures, and facial expressions. The pace and breadth of advances in recent months make it difficult to maintain a coherent view of the field, motivating the need for an integrative review. Unlike earlier overviews that treat avatars, gestures, or facial animation in isolation, this survey offers a single, comprehensive perspective on all the main generative AI applications for character animation. We begin by examining the state-of-the-art in facial animation, expression rendering, image synthesis, avatar creation, gesture modeling, motion synthesis, object generation, and texture synthesis. We highlight leading research, practical deployments, commonly used datasets, and emerging trends for each area. To support newcomers, we also provide a comprehensive background section that introduces foundational models and evaluation metrics, equipping readers with the knowledge needed to enter the field. We discuss open challenges and map future research directions, providing a roadmap to advance AI-driven character-animation technologies. This survey is intended as a resource for researchers and developers entering the field of generative AI animation or adjacent fields. Resources are available at: https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19056</guid>
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<pubDate>Sun, 27 Apr 2025 00:09:31 +0000</pubDate>
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<title>Versatile Framework for Song Generation with Prompt-based Control</title>
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<link>https://arxiv.org/abs/2504.19062</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19062.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Song generation focuses on producing controllable high-quality songs based on various prompts. However, existing methods struggle to generate vocals and accompaniments with prompt-based control and proper alignment. Additionally, they fall short in supporting various tasks. To address these challenges, we introduce VersBand, a multi-task song generation framework for synthesizing high-quality, aligned songs with prompt-based control. VersBand comprises these primary models: 1) VocalBand, a decoupled model, leverages the flow-matching method for generating singing styles, pitches, and mel-spectrograms, allowing fast, high-quality vocal generation with style control. 2) AccompBand, a flow-based transformer model, incorporates the Band-MOE, selecting suitable experts for enhanced quality, alignment, and control. This model allows for generating controllable, high-quality accompaniments aligned with vocals. 3) Two generation models, LyricBand for lyrics and MelodyBand for melodies, contribute to the comprehensive multi-task song generation system, allowing for extensive control based on multiple prompts. Experimental results demonstrate that VersBand performs better over baseline models across multiple song generation tasks using objective and subjective metrics. Audio samples are available at https://VersBand.github.io.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19062</guid>
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<pubDate>Sun, 27 Apr 2025 01:00:06 +0000</pubDate>
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<title>CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges</title>
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<link>https://arxiv.org/abs/2504.19093</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19093.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yu Li, Qizhi Pei, Mengyuan Sun, Honglin Lin, Chenlin Ming, Xin Gao, Jiang Wu, Conghui He, Lijun Wu</p><p><b>Upvotes:</b> 16</p><p><b>Summary:</b> Large language models (LLMs) have demonstrated remarkable capabilities, especially the recent advancements in reasoning, such as o1 and o3, pushing the boundaries of AI. Despite these impressive achievements in mathematics and coding, the reasoning abilities of LLMs in domains requiring cryptographic expertise remain underexplored. In this paper, we introduce CipherBank, a comprehensive benchmark designed to evaluate the reasoning capabilities of LLMs in cryptographic decryption tasks. CipherBank comprises 2,358 meticulously crafted problems, covering 262 unique plaintexts across 5 domains and 14 subdomains, with a focus on privacy-sensitive and real-world scenarios that necessitate encryption. From a cryptographic perspective, CipherBank incorporates 3 major categories of encryption methods, spanning 9 distinct algorithms, ranging from classical ciphers to custom cryptographic techniques. We evaluate state-of-the-art LLMs on CipherBank, e.g., GPT-4o, DeepSeek-V3, and cutting-edge reasoning-focused models such as o1 and DeepSeek-R1. Our results reveal significant gaps in reasoning abilities not only between general-purpose chat LLMs and reasoning-focused LLMs but also in the performance of current reasoning-focused models when applied to classical cryptographic decryption tasks, highlighting the challenges these models face in understanding and manipulating encrypted data. Through detailed analysis and error investigations, we provide several key observations that shed light on the limitations and potential improvement areas for LLMs in cryptographic reasoning. These findings underscore the need for continuous advancements in LLM reasoning capabilities.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19093</guid>
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<pubDate>Sun, 27 Apr 2025 03:41:17 +0000</pubDate>
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<title>ChiseLLM: Unleashing the Power of Reasoning LLMs for Chisel Agile Hardware Development</title>
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<link>https://arxiv.org/abs/2504.19144</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19144.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Bowei Wang, Jiaran Gao, Yelai Feng, Renzhi Chen, Shanshan Li, Lei Wang</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> The growing demand for Domain-Specific Architecture (DSA) has driven the development of Agile Hardware Development Methodology (AHDM). Hardware Construction Language (HCL) like Chisel offers high-level abstraction features, making it an ideal language for HCL-Based AHDM. While Large Language Models (LLMs) excel in code generation tasks, they still face challenges with Chisel generation, particularly regarding syntax correctness and design variability. Recent reasoning models have significantly enhanced code generation capabilities through test-time scaling techniques. However, we found that reasoning models without domain adaptation cannot bring substantial benefits to Chisel code generation tasks. This paper presents ChiseLLM, a solution comprising data processing and transformation, prompt-guided reasoning trace synthesis, and domain-adapted model training. We constructed high-quality datasets from public RTL code resources and guided the model to adopt structured thinking patterns through prompt enhancement methods. Experiments demonstrate that our ChiseLLM-7B and ChiseLLM-32B models improved syntax correctness by 18.85% and 26.32% respectively over base models, while increasing variability design ability by 47.58% compared to baseline reasoning models. Our datasets and models are publicly available, providing high-performance, cost-effective models for HCL-Based AHDM, and offering an effective baseline for future research. Github repository: https://github.com/observerw/ChiseLLM</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19144</guid>
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<pubDate>Sun, 27 Apr 2025 07:56:49 +0000</pubDate>
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<title>SPC: Evolving Self-Play Critic via Adversarial Games for LLM Reasoning</title>
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<link>https://arxiv.org/abs/2504.19162</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19162.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jiaqi Chen, Bang Zhang, Ruotian Ma, Peisong Wang, Xiaodan Liang, Zhaopeng Tu, Xiaolong Li, Kwan-Yee K. Wong</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Evaluating the step-by-step reliability of large language model (LLM) reasoning, such as Chain-of-Thought, remains challenging due to the difficulty and cost of obtaining high-quality step-level supervision. In this paper, we introduce Self-Play Critic (SPC), a novel approach where a critic model evolves its ability to assess reasoning steps through adversarial self-play games, eliminating the need for manual step-level annotation. SPC involves fine-tuning two copies of a base model to play two roles, namely a "sneaky generator" that deliberately produces erroneous steps designed to be difficult to detect, and a "critic" that analyzes the correctness of reasoning steps. These two models engage in an adversarial game in which the generator aims to fool the critic, while the critic model seeks to identify the generator's errors. Using reinforcement learning based on the game outcomes, the models iteratively improve; the winner of each confrontation receives a positive reward and the loser receives a negative reward, driving continuous self-evolution. Experiments on three reasoning process benchmarks (ProcessBench, PRM800K, DeltaBench) demonstrate that our SPC progressively enhances its error detection capabilities (e.g., accuracy increases from 70.8% to 77.7% on ProcessBench) and surpasses strong baselines, including distilled R1 model. Furthermore, applying SPC to guide the test-time search of diverse LLMs significantly improves their mathematical reasoning performance on MATH500 and AIME2024, outperforming state-of-the-art process reward models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19162</guid>
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<pubDate>Sun, 27 Apr 2025 08:45:06 +0000</pubDate>
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<title>LLMs for Engineering: Teaching Models to Design High Powered Rockets</title>
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<link>https://arxiv.org/abs/2504.19394</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19394.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Toby Simonds</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Large Language Models (LLMs) have transformed software engineering, but their application to physical engineering domains remains underexplored. This paper evaluates LLMs' capabilities in high-powered rocketry design through RocketBench, a benchmark connecting LLMs to high-fidelity rocket simulations. We test models on two increasingly complex design tasks: target altitude optimization and precision landing challenges. Our findings reveal that while state-of-the-art LLMs demonstrate strong baseline engineering knowledge, they struggle to iterate on their designs when given simulation results and ultimately plateau below human performance levels. However, when enhanced with reinforcement learning (RL), we show that a 7B parameter model outperforms both SoTA foundation models and human experts. This research demonstrates that RL-trained LLMs can serve as effective tools for complex engineering optimization, potentially transforming engineering domains beyond software development.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19394</guid>
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<pubDate>Sun, 27 Apr 2025 23:59:39 +0000</pubDate>
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<title>ICL CIPHERS: Quantifying "Learning'' in In-Context Learning via Substitution Ciphers</title>
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<link>https://arxiv.org/abs/2504.19395</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19395.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhouxiang Fang, Aayush Mishra, Muhan Gao, Anqi Liu, Daniel Khashabi</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Recent works have suggested that In-Context Learning (ICL) operates in dual modes, i.e. task retrieval (remember learned patterns from pre-training) and task learning (inference-time ``learning'' from demonstrations). However, disentangling these the two modes remains a challenging goal. We introduce ICL CIPHERS, a class of task reformulations based on substitution ciphers borrowed from classic cryptography. In this approach, a subset of tokens in the in-context inputs are substituted with other (irrelevant) tokens, rendering English sentences less comprehensible to human eye. However, by design, there is a latent, fixed pattern to this substitution, making it reversible. This bijective (reversible) cipher ensures that the task remains a well-defined task in some abstract sense, despite the transformations. It is a curious question if LLMs can solve ICL CIPHERS with a BIJECTIVE mapping, which requires deciphering the latent cipher. We show that LLMs are better at solving ICL CIPHERS with BIJECTIVE mappings than the NON-BIJECTIVE (irreversible) baseline, providing a novel approach to quantify ``learning'' in ICL. While this gap is small, it is consistent across the board on four datasets and six models. Finally, we examine LLMs' internal representations and identify evidence in their ability to decode the ciphered inputs.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19395</guid>
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<pubDate>Mon, 28 Apr 2025 00:05:29 +0000</pubDate>
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<title>Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory</title>
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<link>https://arxiv.org/abs/2504.19413</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19413.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, Deshraj Yadav</p><p><b>Upvotes:</b> 10</p><p><b>Summary:</b> Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for maintaining consistency over prolonged multi-session dialogues. We introduce Mem0, a scalable memory-centric architecture that addresses this issue by dynamically extracting, consolidating, and retrieving salient information from ongoing conversations. Building on this foundation, we further propose an enhanced variant that leverages graph-based memory representations to capture complex relational structures among conversational elements. Through comprehensive evaluations on LOCOMO benchmark, we systematically compare our approaches against six baseline categories: (i) established memory-augmented systems, (ii) retrieval-augmented generation (RAG) with varying chunk sizes and k-values, (iii) a full-context approach that processes the entire conversation history, (iv) an open-source memory solution, (v) a proprietary model system, and (vi) a dedicated memory management platform. Empirical results show that our methods consistently outperform all existing memory systems across four question categories: single-hop, temporal, multi-hop, and open-domain. Notably, Mem0 achieves 26% relative improvements in the LLM-as-a-Judge metric over OpenAI, while Mem0 with graph memory achieves around 2% higher overall score than the base configuration. Beyond accuracy gains, we also markedly reduce computational overhead compared to full-context method. In particular, Mem0 attains a 91% lower p95 latency and saves more than 90% token cost, offering a compelling balance between advanced reasoning capabilities and practical deployment constraints. Our findings highlight critical role of structured, persistent memory mechanisms for long-term conversational coherence, paving the way for more reliable and efficient LLM-driven AI agents.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19413</guid>
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<pubDate>Mon, 28 Apr 2025 01:46:35 +0000</pubDate>
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<title>TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering</title>
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<link>https://arxiv.org/abs/2504.20114</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20114.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhonghao Li, Kunpeng Zhang, Jinghuai Ou, Shuliang Liu, Xuming Hu</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require synthesizing information across multiple document chunks. Existing approaches typically rely on iterative LLM-based query rewriting and routing, resulting in high computational costs due to repeated LLM invocations and multi-stage processes. To address these limitations, we propose TreeHop, an embedding-level framework without the need for LLMs in query refinement. TreeHop dynamically updates query embeddings by fusing semantic information from prior queries and retrieved documents, enabling iterative retrieval through embedding-space operations alone. This method replaces the traditional "Retrieve-Rewrite-Vectorize-Retrieve" cycle with a streamlined "Retrieve-Embed-Retrieve" loop, significantly reducing computational overhead. Moreover, a rule-based stop criterion is introduced to further prune redundant retrievals, balancing efficiency and recall rate. Experimental results show that TreeHop rivals advanced RAG methods across three open-domain MHQA datasets, achieving comparable performance with only 5\%-0.4\% of the model parameter size and reducing the query latency by approximately 99\% compared to concurrent approaches. This makes TreeHop a faster and more cost-effective solution for deployment in a range of knowledge-intensive applications. For reproducibility purposes, codes and data are available here: https://github.com/allen-li1231/TreeHop.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20114</guid>
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<pubDate>Mon, 28 Apr 2025 01:56:31 +0000</pubDate>
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<title>Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report</title>
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<link>https://arxiv.org/abs/2504.21039</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21039.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Paul Kassianik, Baturay Saglam, Alexander Chen, Blaine Nelson, Anu Vellore, Massimo Aufiero, Fraser Burch, Dhruv Kedia, Avi Zohary, Sajana Weerawardhena, Aman Priyanshu, Adam Swanda, Amy Chang, Hyrum Anderson, Kojin Oshiba, Omar Santos, Yaron Singer, Amin Karbasi</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> As transformer-based large language models (LLMs) increasingly permeate society, they have revolutionized domains such as software engineering, creative writing, and digital arts. However, their adoption in cybersecurity remains limited due to challenges like scarcity of specialized training data and complexity of representing cybersecurity-specific knowledge. To address these gaps, we present Foundation-Sec-8B, a cybersecurity-focused LLM built on the Llama 3.1 architecture and enhanced through continued pretraining on a carefully curated cybersecurity corpus. We evaluate Foundation-Sec-8B across both established and new cybersecurity benchmarks, showing that it matches Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks. By releasing our model to the public, we aim to accelerate progress and adoption of AI-driven tools in both public and private cybersecurity contexts.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21039</guid>
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<pubDate>Mon, 28 Apr 2025 08:41:12 +0000</pubDate>
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<title>Taming the Titans: A Survey of Efficient LLM Inference Serving</title>
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<link>https://arxiv.org/abs/2504.19720</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19720.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Ranran Zhen, Juntao Li, Yixin Ji, Zhenlin Yang, Tong Liu, Qingrong Xia, Xinyu Duan, Zhefeng Wang, Baoxing Huai, Min Zhang</p><p><b>Upvotes:</b> 9</p><p><b>Summary:</b> Large Language Models (LLMs) for Generative AI have achieved remarkable progress, evolving into sophisticated and versatile tools widely adopted across various domains and applications. However, the substantial memory overhead caused by their vast number of parameters, combined with the high computational demands of the attention mechanism, poses significant challenges in achieving low latency and high throughput for LLM inference services. Recent advancements, driven by groundbreaking research, have significantly accelerated progress in this field. This paper provides a comprehensive survey of these methods, covering fundamental instance-level approaches, in-depth cluster-level strategies, emerging scenario directions, and other miscellaneous but important areas. At the instance level, we review model placement, request scheduling, decoding length prediction, storage management, and the disaggregation paradigm. At the cluster level, we explore GPU cluster deployment, multi-instance load balancing, and cloud service solutions. For emerging scenarios, we organize the discussion around specific tasks, modules, and auxiliary methods. To ensure a holistic overview, we also highlight several niche yet critical areas. Finally, we outline potential research directions to further advance the field of LLM inference serving.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19720</guid>
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<pubDate>Mon, 28 Apr 2025 12:14:02 +0000</pubDate>
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<title>RepText: Rendering Visual Text via Replicating</title>
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<link>https://arxiv.org/abs/2504.19724</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19724.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haofan Wang, Yujia Xu, Yimeng Li, Junchen Li, Chaowei Zhang, Jing Wang, Kejia Yang, Zhibo Chen</p><p><b>Upvotes:</b> 29</p><p><b>Summary:</b> Although contemporary text-to-image generation models have achieved remarkable breakthroughs in producing visually appealing images, their capacity to generate precise and flexible typographic elements, especially non-Latin alphabets, remains constrained. To address these limitations, we start from an naive assumption that text understanding is only a sufficient condition for text rendering, but not a necessary condition. Based on this, we present RepText, which aims to empower pre-trained monolingual text-to-image generation models with the ability to accurately render, or more precisely, replicate, multilingual visual text in user-specified fonts, without the need to really understand them. Specifically, we adopt the setting from ControlNet and additionally integrate language agnostic glyph and position of rendered text to enable generating harmonized visual text, allowing users to customize text content, font and position on their needs. To improve accuracy, a text perceptual loss is employed along with the diffusion loss. Furthermore, to stabilize rendering process, at the inference phase, we directly initialize with noisy glyph latent instead of random initialization, and adopt region masks to restrict the feature injection to only the text region to avoid distortion of the background. We conducted extensive experiments to verify the effectiveness of our RepText relative to existing works, our approach outperforms existing open-source methods and achieves comparable results to native multi-language closed-source models. To be more fair, we also exhaustively discuss its limitations in the end.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19724</guid>
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<pubDate>Mon, 28 Apr 2025 12:19:53 +0000</pubDate>
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<title>LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects</title>
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<link>https://arxiv.org/abs/2504.19838</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19838.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Guangyi Liu, Pengxiang Zhao, Liang Liu, Yaxuan Guo, Han Xiao, Weifeng Lin, Yuxiang Chai, Yue Han, Shuai Ren, Hao Wang, Xiaoyu Liang, Wenhao Wang, Tianze Wu, Linghao Li, Hao Wang, Guanjing Xiong, Yong Liu, Hongsheng Li</p><p><b>Upvotes:</b> 21</p><p><b>Summary:</b> With the rapid rise of large language models (LLMs), phone automation has undergone transformative changes. This paper systematically reviews LLM-driven phone GUI agents, highlighting their evolution from script-based automation to intelligent, adaptive systems. We first contextualize key challenges, (i) limited generality, (ii) high maintenance overhead, and (iii) weak intent comprehension, and show how LLMs address these issues through advanced language understanding, multimodal perception, and robust decision-making. We then propose a taxonomy covering fundamental agent frameworks (single-agent, multi-agent, plan-then-act), modeling approaches (prompt engineering, training-based), and essential datasets and benchmarks. Furthermore, we detail task-specific architectures, supervised fine-tuning, and reinforcement learning strategies that bridge user intent and GUI operations. Finally, we discuss open challenges such as dataset diversity, on-device deployment efficiency, user-centric adaptation, and security concerns, offering forward-looking insights into this rapidly evolving field. By providing a structured overview and identifying pressing research gaps, this paper serves as a definitive reference for researchers and practitioners seeking to harness LLMs in designing scalable, user-friendly phone GUI agents.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19838</guid>
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<pubDate>Mon, 28 Apr 2025 14:39:25 +0000</pubDate>
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<title>NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks</title>
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<link>https://arxiv.org/abs/2504.19854</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.19854.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chia-Yu Hung, Qi Sun, Pengfei Hong, Amir Zadeh, Chuan Li, U-Xuan Tan, Navonil Majumder, Soujanya Poria</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Existing Visual-Language-Action (VLA) models have shown promising performance in zero-shot scenarios, demonstrating impressive task execution and reasoning capabilities. However, a significant challenge arises from the limitations of visual encoding, which can result in failures during tasks such as object grasping. Moreover, these models typically suffer from high computational overhead due to their large sizes, often exceeding 7B parameters. While these models excel in reasoning and task planning, the substantial computational overhead they incur makes them impractical for real-time robotic environments, where speed and efficiency are paramount. To address the limitations of existing VLA models, we propose NORA, a 3B-parameter model designed to reduce computational overhead while maintaining strong task performance. NORA adopts the Qwen-2.5-VL-3B multimodal model as its backbone, leveraging its superior visual-semantic understanding to enhance visual reasoning and action grounding. Additionally, our is trained on 970k real-world robot demonstrations and equipped with the FAST+ tokenizer for efficient action sequence generation. Experimental results demonstrate that NORA outperforms existing large-scale VLA models, achieving better task performance with significantly reduced computational overhead, making it a more practical solution for real-time robotic autonomy.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.19854</guid>
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<pubDate>Mon, 28 Apr 2025 14:47:34 +0000</pubDate>
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<title>Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models</title>
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<link>https://arxiv.org/abs/2504.20157</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20157.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zae Myung Kim, Chanwoo Park, Vipul Raheja, Dongyeop Kang</p><p><b>Upvotes:</b> 33</p><p><b>Summary:</b> Reward-based alignment methods for large language models (LLMs) face two key limitations: vulnerability to reward hacking, where models exploit flaws in the reward signal; and reliance on brittle, labor-intensive prompt engineering when LLMs are used as reward models. We introduce Meta Policy Optimization (MPO), a framework that addresses these challenges by integrating a meta-reward model that dynamically refines the reward model's prompt throughout training. In MPO, the meta-reward model monitors the evolving training context and continuously adjusts the reward model's prompt to maintain high alignment, providing an adaptive reward signal that resists exploitation by the policy. This meta-learning approach promotes a more stable policy optimization, and greatly reduces the need for manual reward prompt design. It yields performance on par with or better than models guided by extensively hand-crafted reward prompts. Furthermore, we show that MPO maintains its effectiveness across diverse tasks, such as question answering and mathematical reasoning, without requiring specialized reward designs. Beyond standard RLAIF, MPO's meta-learning formulation is readily extensible to higher-level alignment frameworks. Overall, this method addresses theoretical and practical challenges in reward-based RL alignment for LLMs, paving the way for more robust and adaptable alignment strategies. The code and models will be publicly shared.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20157</guid>
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<pubDate>Mon, 28 Apr 2025 18:02:35 +0000</pubDate>
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<title>Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs</title>
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<link>https://arxiv.org/abs/2504.20406</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20406.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Paiheng Xu, Gang Wu, Xiang Chen, Tong Yu, Chang Xiao, Franck Dernoncourt, Tianyi Zhou, Wei Ai, Viswanathan Swaminathan</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Scripting interfaces enable users to automate tasks and customize software workflows, but creating scripts traditionally requires programming expertise and familiarity with specific APIs, posing barriers for many users. While Large Language Models (LLMs) can generate code from natural language queries, runtime code generation is severely limited due to unverified code, security risks, longer response times, and higher computational costs. To bridge the gap, we propose an offline simulation framework to curate a software-specific skillset, a collection of verified scripts, by exploiting LLMs and publicly available scripting guides. Our framework comprises two components: (1) task creation, using top-down functionality guidance and bottom-up API synergy exploration to generate helpful tasks; and (2) skill generation with trials, refining and validating scripts based on execution feedback. To efficiently navigate the extensive API landscape, we introduce a Graph Neural Network (GNN)-based link prediction model to capture API synergy, enabling the generation of skills involving underutilized APIs and expanding the skillset's diversity. Experiments with Adobe Illustrator demonstrate that our framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. This is the first attempt to use software scripting interfaces as a testbed for LLM-based systems, highlighting the advantages of leveraging execution feedback in a controlled environment and offering valuable insights into aligning AI capabilities with user needs in specialized software domains.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20406</guid>
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<pubDate>Tue, 29 Apr 2025 04:03:37 +0000</pubDate>
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<title>Reinforcement Learning for Reasoning in Large Language Models with One Training Example</title>
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<link>https://arxiv.org/abs/2504.20571</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20571.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yiping Wang, Qing Yang, Zhiyuan Zeng, Liliang Ren, Lucas Liu, Baolin Peng, Hao Cheng, Xuehai He, Kuan Wang, Jianfeng Gao, Weizhu Chen, Shuohang Wang, Simon Shaolei Du, Yelong Shen</p><p><b>Upvotes:</b> 79</p><p><b>Summary:</b> We show that reinforcement learning with verifiable reward using one training example (1-shot RLVR) is effective in incentivizing the math reasoning capabilities of large language models (LLMs). Applying RLVR to the base model Qwen2.5-Math-1.5B, we identify a single example that elevates model performance on MATH500 from 36.0% to 73.6%, and improves the average performance across six common mathematical reasoning benchmarks from 17.6% to 35.7%. This result matches the performance obtained using the 1.2k DeepScaleR subset (MATH500: 73.6%, average: 35.9%), which includes the aforementioned example. Similar substantial improvements are observed across various models (Qwen2.5-Math-7B, Llama3.2-3B-Instruct, DeepSeek-R1-Distill-Qwen-1.5B), RL algorithms (GRPO and PPO), and different math examples (many of which yield approximately 30% or greater improvement on MATH500 when employed as a single training example). In addition, we identify some interesting phenomena during 1-shot RLVR, including cross-domain generalization, increased frequency of self-reflection, and sustained test performance improvement even after the training accuracy has saturated, a phenomenon we term post-saturation generalization. Moreover, we verify that the effectiveness of 1-shot RLVR primarily arises from the policy gradient loss, distinguishing it from the "grokking" phenomenon. We also show the critical role of promoting exploration (e.g., by adding entropy loss with an appropriate coefficient) in 1-shot RLVR training. As a bonus, we observe that applying entropy loss alone, without any outcome reward, significantly enhances Qwen2.5-Math-1.5B's performance on MATH500 by 27.4%. These findings can inspire future work on RLVR data efficiency and encourage a re-examination of both recent progress and the underlying mechanisms in RLVR. Our code, model, and data are open source at https://github.com/ypwang61/One-Shot-RLVR</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20571</guid>
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<pubDate>Tue, 29 Apr 2025 09:24:30 +0000</pubDate>
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<title>ReasonIR: Training Retrievers for Reasoning Tasks</title>
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<link>https://arxiv.org/abs/2504.20595</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20595.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Rulin Shao, Rui Qiao, Varsha Kishore, Niklas Muennighoff, Xi Victoria Lin, Daniela Rus, Bryan Kian Hsiang Low, Sewon Min, Wen-tau Yih, Pang Wei Koh, Luke Zettlemoyer</p><p><b>Upvotes:</b> 45</p><p><b>Summary:</b> We present ReasonIR-8B, the first retriever specifically trained for general reasoning tasks. Existing retrievers have shown limited gains on reasoning tasks, in part because existing training datasets focus on short factual queries tied to documents that straightforwardly answer them. We develop a synthetic data generation pipeline that, for each document, our pipeline creates a challenging and relevant query, along with a plausibly related but ultimately unhelpful hard negative. By training on a mixture of our synthetic data and existing public data, ReasonIR-8B achieves a new state-of-the-art of 29.9 nDCG@10 without reranker and 36.9 nDCG@10 with reranker on BRIGHT, a widely-used reasoning-intensive information retrieval (IR) benchmark. When applied to RAG tasks, ReasonIR-8B improves MMLU and GPQA performance by 6.4% and 22.6% respectively, relative to the closed-book baseline, outperforming other retrievers and search engines. In addition, ReasonIR-8B uses test-time compute more effectively: on BRIGHT, its performance consistently increases with longer and more information-rich rewritten queries; it continues to outperform other retrievers when combined with an LLM reranker. Our training recipe is general and can be easily extended to future LLMs; to this end, we open-source our code, data, and model.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20595</guid>
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<pubDate>Tue, 29 Apr 2025 09:49:28 +0000</pubDate>
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<title>TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models</title>
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<link>https://arxiv.org/abs/2504.20605</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20605.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mihai Nadas, Laura Diosan, Andrei Piscoran, Andreea Tomescu</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Moral stories are a time-tested vehicle for transmitting values, yet modern NLP lacks a large, structured corpus that couples coherent narratives with explicit ethical lessons. We close this gap with TF1-EN-3M, the first open dataset of three million English-language fables generated exclusively by instruction-tuned models no larger than 8B parameters. Each story follows a six-slot scaffold (character -> trait -> setting -> conflict -> resolution -> moral), produced through a combinatorial prompt engine that guarantees genre fidelity while covering a broad thematic space. A hybrid evaluation pipeline blends (i) a GPT-based critic that scores grammar, creativity, moral clarity, and template adherence with (ii) reference-free diversity and readability metrics. Among ten open-weight candidates, an 8B-parameter Llama-3 variant delivers the best quality-speed trade-off, producing high-scoring fables on a single consumer GPU (<24 GB VRAM) at approximately 13.5 cents per 1,000 fables. We release the dataset, generation code, evaluation scripts, and full metadata under a permissive license, enabling exact reproducibility and cost benchmarking. TF1-EN-3M opens avenues for research in instruction following, narrative intelligence, value alignment, and child-friendly educational AI, demonstrating that large-scale moral storytelling no longer requires proprietary giant models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20605</guid>
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<pubDate>Tue, 29 Apr 2025 10:15:28 +0000</pubDate>
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<title>ISDrama: Immersive Spatial Drama Generation through Multimodal Prompting</title>
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<link>https://arxiv.org/abs/2504.20630</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20630.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Tao Jin, Zhou Zhao</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Multimodal immersive spatial drama generation focuses on creating continuous multi-speaker binaural speech with dramatic prosody based on multimodal prompts, with potential applications in AR, VR, and others. This task requires simultaneous modeling of spatial information and dramatic prosody based on multimodal inputs, with high data collection costs. To the best of our knowledge, our work is the first attempt to address these challenges. We construct MRSDrama, the first multimodal recorded spatial drama dataset, containing binaural drama audios, scripts, videos, geometric poses, and textual prompts. Then, we propose ISDrama, the first immersive spatial drama generation model through multimodal prompting. ISDrama comprises these primary components: 1) Multimodal Pose Encoder, based on contrastive learning, considering the Doppler effect caused by moving speakers to extract unified pose information from multimodal prompts. 2) Immersive Drama Transformer, a flow-based mamba-transformer model that generates high-quality drama, incorporating Drama-MOE to select proper experts for enhanced prosody and pose control. We also design a context-consistent classifier-free guidance strategy to coherently generate complete drama. Experimental results show that ISDrama outperforms baseline models on objective and subjective metrics. The demos and dataset are available at https://aaronz345.github.io/ISDramaDemo.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20630</guid>
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<pubDate>Tue, 29 Apr 2025 10:56:44 +0000</pubDate>
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<title>In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer</title>
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<link>https://arxiv.org/abs/2504.20690</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20690.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zechuan Zhang, Ji Xie, Yu Lu, Zongxin Yang, Yi Yang</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Instruction-based image editing enables robust image modification via natural language prompts, yet current methods face a precision-efficiency tradeoff. Fine-tuning methods demand significant computational resources and large datasets, while training-free techniques struggle with instruction comprehension and edit quality. We resolve this dilemma by leveraging large-scale Diffusion Transformer (DiT)' enhanced generation capacity and native contextual awareness. Our solution introduces three contributions: (1) an in-context editing framework for zero-shot instruction compliance using in-context prompting, avoiding structural changes; (2) a LoRA-MoE hybrid tuning strategy that enhances flexibility with efficient adaptation and dynamic expert routing, without extensive retraining; and (3) an early filter inference-time scaling method using vision-language models (VLMs) to select better initial noise early, improving edit quality. Extensive evaluations demonstrate our method's superiority: it outperforms state-of-the-art approaches while requiring only 0.5% training data and 1% trainable parameters compared to conventional baselines. This work establishes a new paradigm that enables high-precision yet efficient instruction-guided editing. Codes and demos can be found in https://river-zhang.github.io/ICEdit-gh-pages/.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20690</guid>
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<pubDate>Tue, 29 Apr 2025 12:14:47 +0000</pubDate>
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<title>Beyond the Last Answer: Your Reasoning Trace Uncovers More than You Think</title>
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<link>https://arxiv.org/abs/2504.20708</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20708.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Hasan Abed Al Kader Hammoud, Hani Itani, Bernard Ghanem</p><p><b>Upvotes:</b> 20</p><p><b>Summary:</b> Large Language Models (LLMs) leverage step-by-step reasoning to solve complex problems. Standard evaluation practice involves generating a complete reasoning trace and assessing the correctness of the final answer presented at its conclusion. In this paper, we challenge the reliance on the final answer by posing the following two questions: Does the final answer reliably represent the model's optimal conclusion? Can alternative reasoning paths yield different results? To answer these questions, we analyze intermediate reasoning steps, termed subthoughts, and propose a method based on our findings. Our approach involves segmenting a reasoning trace into sequential subthoughts based on linguistic cues. We start by prompting the model to generate continuations from the end-point of each intermediate subthought. We extract a potential answer from every completed continuation originating from different subthoughts. We find that aggregating these answers by selecting the most frequent one (the mode) often yields significantly higher accuracy compared to relying solely on the answer derived from the original complete trace. Analyzing the consistency among the answers derived from different subthoughts reveals characteristics that correlate with the model's confidence and correctness, suggesting potential for identifying less reliable answers. Our experiments across various LLMs and challenging mathematical reasoning datasets (AIME2024 and AIME2025) show consistent accuracy improvements, with gains reaching up to 13\% and 10\% respectively. Implementation is available at: https://github.com/hammoudhasan/SubthoughtReasoner.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20708</guid>
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<pubDate>Tue, 29 Apr 2025 12:39:07 +0000</pubDate>
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<title>UniversalRAG: Retrieval-Augmented Generation over Multiple Corpora with Diverse Modalities and Granularities</title>
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<link>https://arxiv.org/abs/2504.20734</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20734.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek, Sung Ju Hwang</p><p><b>Upvotes:</b> 56</p><p><b>Summary:</b> Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. However, most existing RAG approaches are limited to a text-only corpus, and while recent efforts have extended RAG to other modalities such as images and videos, they typically operate over a single modality-specific corpus. In contrast, real-world queries vary widely in the type of knowledge they require, which a single type of knowledge source cannot address. To address this, we introduce UniversalRAG, a novel RAG framework designed to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. Specifically, motivated by the observation that forcing all modalities into a unified representation space derived from a single combined corpus causes a modality gap, where the retrieval tends to favor items from the same modality as the query, we propose a modality-aware routing mechanism that dynamically identifies the most appropriate modality-specific corpus and performs targeted retrieval within it. Also, beyond modality, we organize each modality into multiple granularity levels, enabling fine-tuned retrieval tailored to the complexity and scope of the query. We validate UniversalRAG on 8 benchmarks spanning multiple modalities, showing its superiority over modality-specific and unified baselines.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20734</guid>
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<pubDate>Tue, 29 Apr 2025 13:18:58 +0000</pubDate>
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<title>Chain-of-Defensive-Thought: Structured Reasoning Elicits Robustness in Large Language Models against Reference Corruption</title>
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<link>https://arxiv.org/abs/2504.20769</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20769.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wenxiao Wang, Parsa Hosseini, Soheil Feizi</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Chain-of-thought prompting has demonstrated great success in facilitating the reasoning abilities of large language models. In this work, we explore how these enhanced reasoning abilities can be exploited to improve the robustness of large language models in tasks that are not necessarily reasoning-focused. In particular, we show how a wide range of large language models exhibit significantly improved robustness against reference corruption using a simple method called chain-of-defensive-thought, where only a few exemplars with structured and defensive reasoning are provided as demonstrations. Empirically, the improvements can be astounding, especially given the simplicity and applicability of the method. For example, in the Natural Questions task, the accuracy of GPT-4o degrades from 60% to as low as 3% with standard prompting when 1 out of 10 references provided is corrupted with prompt injection attacks. In contrast, GPT-4o using chain-of-defensive-thought prompting maintains an accuracy of 50%.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20769</guid>
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<pubDate>Tue, 29 Apr 2025 13:50:05 +0000</pubDate>
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<title>The Leaderboard Illusion</title>
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<link>https://arxiv.org/abs/2504.20879</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20879.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shivalika Singh, Yiyang Nan, Alex Wang, Daniel D'Souza, Sayash Kapoor, Ahmet Üstün, Sanmi Koyejo, Yuntian Deng, Shayne Longpre, Noah Smith, Beyza Ermis, Marzieh Fadaee, Sara Hooker</p><p><b>Upvotes:</b> 60</p><p><b>Summary:</b> Measuring progress is fundamental to the advancement of any scientific field. As benchmarks play an increasingly central role, they also grow more susceptible to distortion. Chatbot Arena has emerged as the go-to leaderboard for ranking the most capable AI systems. Yet, in this work we identify systematic issues that have resulted in a distorted playing field. We find that undisclosed private testing practices benefit a handful of providers who are able to test multiple variants before public release and retract scores if desired. We establish that the ability of these providers to choose the best score leads to biased Arena scores due to selective disclosure of performance results. At an extreme, we identify 27 private LLM variants tested by Meta in the lead-up to the Llama-4 release. We also establish that proprietary closed models are sampled at higher rates (number of battles) and have fewer models removed from the arena than open-weight and open-source alternatives. Both these policies lead to large data access asymmetries over time. Providers like Google and OpenAI have received an estimated 19.2% and 20.4% of all data on the arena, respectively. In contrast, a combined 83 open-weight models have only received an estimated 29.7% of the total data. We show that access to Chatbot Arena data yields substantial benefits; even limited additional data can result in relative performance gains of up to 112% on the arena distribution, based on our conservative estimates. Together, these dynamics result in overfitting to Arena-specific dynamics rather than general model quality. The Arena builds on the substantial efforts of both the organizers and an open community that maintains this valuable evaluation platform. We offer actionable recommendations to reform the Chatbot Arena's evaluation framework and promote fairer, more transparent benchmarking for the field</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20879</guid>
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<pubDate>Tue, 29 Apr 2025 15:48:49 +0000</pubDate>
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<title>Softpick: No Attention Sink, No Massive Activations with Rectified Softmax</title>
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<link>https://arxiv.org/abs/2504.20966</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20966.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zayd M. K. Zuhri, Erland Hilman Fuadi, Alham Fikri Aji</p><p><b>Upvotes:</b> 22</p><p><b>Summary:</b> We introduce softpick, a rectified, not sum-to-one, drop-in replacement for softmax in transformer attention mechanisms that eliminates attention sink and massive activations. Our experiments with 340M parameter models demonstrate that softpick maintains performance parity with softmax on standard benchmarks while achieving 0% sink rate. The softpick transformer produces hidden states with significantly lower kurtosis (340 vs 33,510) and creates sparse attention maps (46.97% sparsity). Models using softpick consistently outperform softmax when quantized, with particularly pronounced advantages at lower bit precisions. Our analysis and discussion shows how softpick has the potential to open new possibilities for quantization, low-precision training, sparsity optimization, pruning, and interpretability. Our code is available at https://github.com/zaydzuhri/softpick-attention.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20966</guid>
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<pubDate>Tue, 29 Apr 2025 17:36:18 +0000</pubDate>
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<title>TesserAct: Learning 4D Embodied World Models</title>
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<link>https://arxiv.org/abs/2504.20995</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20995.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haoyu Zhen, Qiao Sun, Hongxin Zhang, Junyan Li, Siyuan Zhou, Yilun Du, Chuang Gan</p><p><b>Upvotes:</b> 18</p><p><b>Summary:</b> This paper presents an effective approach for learning novel 4D embodied world models, which predict the dynamic evolution of 3D scenes over time in response to an embodied agent's actions, providing both spatial and temporal consistency. We propose to learn a 4D world model by training on RGB-DN (RGB, Depth, and Normal) videos. This not only surpasses traditional 2D models by incorporating detailed shape, configuration, and temporal changes into their predictions, but also allows us to effectively learn accurate inverse dynamic models for an embodied agent. Specifically, we first extend existing robotic manipulation video datasets with depth and normal information leveraging off-the-shelf models. Next, we fine-tune a video generation model on this annotated dataset, which jointly predicts RGB-DN (RGB, Depth, and Normal) for each frame. We then present an algorithm to directly convert generated RGB, Depth, and Normal videos into a high-quality 4D scene of the world. Our method ensures temporal and spatial coherence in 4D scene predictions from embodied scenarios, enables novel view synthesis for embodied environments, and facilitates policy learning that significantly outperforms those derived from prior video-based world models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20995</guid>
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<pubDate>Tue, 29 Apr 2025 17:59:30 +0000</pubDate>
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<title>X-Fusion: Introducing New Modality to Frozen Large Language Models</title>
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<link>https://arxiv.org/abs/2504.20996</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20996.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Sicheng Mo, Thao Nguyen, Xun Huang, Siddharth Srinivasan Iyer, Yijun Li, Yuchen Liu, Abhishek Tandon, Eli Shechtman, Krishna Kumar Singh, Yong Jae Lee, Bolei Zhou, Yuheng Li</p><p><b>Upvotes:</b> 11</p><p><b>Summary:</b> We propose X-Fusion, a framework that extends pretrained Large Language Models (LLMs) for multimodal tasks while preserving their language capabilities. X-Fusion employs a dual-tower design with modality-specific weights, keeping the LLM's parameters frozen while integrating vision-specific information for both understanding and generation. Our experiments demonstrate that X-Fusion consistently outperforms alternative architectures on both image-to-text and text-to-image tasks. We find that incorporating understanding-focused data improves generation quality, reducing image data noise enhances overall performance, and feature alignment accelerates convergence for smaller models but has minimal impact on larger ones. Our findings provide valuable insights into building efficient unified multimodal models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20996</guid>
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<pubDate>Tue, 29 Apr 2025 17:59:45 +0000</pubDate>
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<title>YoChameleon: Personalized Vision and Language Generation</title>
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<link>https://arxiv.org/abs/2504.20998</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.20998.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Thao Nguyen, Krishna Kumar Singh, Jing Shi, Trung Bui, Yong Jae Lee, Yuheng Li</p><p><b>Upvotes:</b> 11</p><p><b>Summary:</b> Large Multimodal Models (e.g., GPT-4, Gemini, Chameleon) have evolved into powerful tools with millions of users. However, they remain generic models and lack personalized knowledge of specific user concepts. Previous work has explored personalization for text generation, yet it remains unclear how these methods can be adapted to new modalities, such as image generation. In this paper, we introduce Yo'Chameleon, the first attempt to study personalization for large multimodal models. Given 3-5 images of a particular concept, Yo'Chameleon leverages soft-prompt tuning to embed subject-specific information to (i) answer questions about the subject and (ii) recreate pixel-level details to produce images of the subject in new contexts. Yo'Chameleon is trained with (i) a self-prompting optimization mechanism to balance performance across multiple modalities, and (ii) a ``soft-positive" image generation approach to enhance image quality in a few-shot setting.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.20998</guid>
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<pubDate>Tue, 29 Apr 2025 17:59:57 +0000</pubDate>
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<title>Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math</title>
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<link>https://arxiv.org/abs/2504.21233</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21233.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haoran Xu, Baolin Peng, Hany Awadalla, Dongdong Chen, Yen-Chun Chen, Mei Gao, Young Jin Kim, Yunsheng Li, Liliang Ren, Yelong Shen, Shuohang Wang, Weijian Xu, Jianfeng Gao, Weizhu Chen</p><p><b>Upvotes:</b> 34</p><p><b>Summary:</b> Chain-of-Thought (CoT) significantly enhances formal reasoning capabilities in Large Language Models (LLMs) by training them to explicitly generate intermediate reasoning steps. While LLMs readily benefit from such techniques, improving reasoning in Small Language Models (SLMs) remains challenging due to their limited model capacity. Recent work by Deepseek-R1 demonstrates that distillation from LLM-generated synthetic data can substantially improve the reasoning ability of SLM. However, the detailed modeling recipe is not disclosed. In this work, we present a systematic training recipe for SLMs that consists of four steps: (1) large-scale mid-training on diverse distilled long-CoT data, (2) supervised fine-tuning on high-quality long-CoT data, (3) Rollout DPO leveraging a carefully curated preference dataset, and (4) Reinforcement Learning (RL) with Verifiable Reward. We apply our method on Phi-4-Mini, a compact 3.8B-parameter model. The resulting Phi-4-Mini-Reasoning model exceeds, on math reasoning tasks, much larger reasoning models, e.g., outperforming DeepSeek-R1-Distill-Qwen-7B by 3.2 points and DeepSeek-R1-Distill-Llama-8B by 7.7 points on Math-500. Our results validate that a carefully designed training recipe, with large-scale high-quality CoT data, is effective to unlock strong reasoning capabilities even in resource-constrained small models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21233</guid>
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<pubDate>Wed, 30 Apr 2025 00:04:35 +0000</pubDate>
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<title>Phi-4-reasoning Technical Report</title>
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<link>https://arxiv.org/abs/2504.21318</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21318.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Marah Abdin, Sahaj Agarwal, Ahmed Awadallah, Vidhisha Balachandran, Harkirat Behl, Lingjiao Chen, Gustavo de Rosa, Suriya Gunasekar, Mojan Javaheripi, Neel Joshi, Piero Kauffmann, Yash Lara, Caio César Teodoro Mendes, Arindam Mitra, Besmira Nushi, Dimitris Papailiopoulos, Olli Saarikivi, Shital Shah, Vaishnavi Shrivastava, Vibhav Vineet, Yue Wu, Safoora Yousefi, Guoqing Zheng</p><p><b>Upvotes:</b> 28</p><p><b>Summary:</b> We introduce Phi-4-reasoning, a 14-billion parameter reasoning model that achieves strong performance on complex reasoning tasks. Trained via supervised fine-tuning of Phi-4 on carefully curated set of "teachable" prompts-selected for the right level of complexity and diversity-and reasoning demonstrations generated using o3-mini, Phi-4-reasoning generates detailed reasoning chains that effectively leverage inference-time compute. We further develop Phi-4-reasoning-plus, a variant enhanced through a short phase of outcome-based reinforcement learning that offers higher performance by generating longer reasoning traces. Across a wide range of reasoning tasks, both models outperform significantly larger open-weight models such as DeepSeek-R1-Distill-Llama-70B model and approach the performance levels of full DeepSeek-R1 model. Our comprehensive evaluations span benchmarks in math and scientific reasoning, coding, algorithmic problem solving, planning, and spatial understanding. Interestingly, we observe a non-trivial transfer of improvements to general-purpose benchmarks as well. In this report, we provide insights into our training data, our training methodologies, and our evaluations. We show that the benefit of careful data curation for supervised fine-tuning (SFT) extends to reasoning language models, and can be further amplified by reinforcement learning (RL). Finally, our evaluation points to opportunities for improving how we assess the performance and robustness of reasoning models.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21318</guid>
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<pubDate>Wed, 30 Apr 2025 05:05:09 +0000</pubDate>
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<title>UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation</title>
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<link>https://arxiv.org/abs/2504.21336</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21336.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Linshan Wu, Yuxiang Nie, Sunan He, Jiaxin Zhuang, Hao Chen</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Multi-modal interpretation of biomedical images opens up novel opportunities in biomedical image analysis. Conventional AI approaches typically rely on disjointed training, i.e., Large Language Models (LLMs) for clinical text generation and segmentation models for target extraction, which results in inflexible real-world deployment and a failure to leverage holistic biomedical information. To this end, we introduce UniBiomed, the first universal foundation model for grounded biomedical image interpretation. UniBiomed is based on a novel integration of Multi-modal Large Language Model (MLLM) and Segment Anything Model (SAM), which effectively unifies the generation of clinical texts and the segmentation of corresponding biomedical objects for grounded interpretation. In this way, UniBiomed is capable of tackling a wide range of biomedical tasks across ten diverse biomedical imaging modalities. To develop UniBiomed, we curate a large-scale dataset comprising over 27 million triplets of images, annotations, and text descriptions across ten imaging modalities. Extensive validation on 84 internal and external datasets demonstrated that UniBiomed achieves state-of-the-art performance in segmentation, disease recognition, region-aware diagnosis, visual question answering, and report generation. Moreover, unlike previous models that rely on clinical experts to pre-diagnose images and manually craft precise textual or visual prompts, UniBiomed can provide automated and end-to-end grounded interpretation for biomedical image analysis. This represents a novel paradigm shift in clinical workflows, which will significantly improve diagnostic efficiency. In summary, UniBiomed represents a novel breakthrough in biomedical AI, unlocking powerful grounded interpretation capabilities for more accurate and efficient biomedical image analysis.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21336</guid>
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<pubDate>Wed, 30 Apr 2025 05:51:48 +0000</pubDate>
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<title>Sadeed: Advancing Arabic Diacritization Through Small Language Model</title>
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<link>https://arxiv.org/abs/2504.21635</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21635.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zeina Aldallal, Sara Chrouf, Khalil Hennara, Mohamed Motaism Hamed, Muhammad Hreden, Safwan AlModhayan</p><p><b>Upvotes:</b> 50</p><p><b>Summary:</b> Arabic text diacritization remains a persistent challenge in natural language processing due to the language's morphological richness. In this paper, we introduce Sadeed, a novel approach based on a fine-tuned decoder-only language model adapted from Kuwain 1.5B Hennara et al. [2025], a compact model originally trained on diverse Arabic corpora. Sadeed is fine-tuned on carefully curated, high-quality diacritized datasets, constructed through a rigorous data-cleaning and normalization pipeline. Despite utilizing modest computational resources, Sadeed achieves competitive results compared to proprietary large language models and outperforms traditional models trained on similar domains. Additionally, we highlight key limitations in current benchmarking practices for Arabic diacritization. To address these issues, we introduce SadeedDiac-25, a new benchmark designed to enable fairer and more comprehensive evaluation across diverse text genres and complexity levels. Together, Sadeed and SadeedDiac-25 provide a robust foundation for advancing Arabic NLP applications, including machine translation, text-to-speech, and language learning tools.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21635</guid>
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<pubDate>Wed, 30 Apr 2025 13:37:24 +0000</pubDate>
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<title>AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization</title>
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<link>https://arxiv.org/abs/2504.21659</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21659.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Haotian Luo, Haiying He, Yibo Wang, Jinluan Yang, Rui Liu, Naiqiang Tan, Xiaochun Cao, Dacheng Tao, Li Shen</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Recently, long-thought reasoning models achieve strong performance on complex reasoning tasks, but often incur substantial inference overhead, making efficiency a critical concern. Our empirical analysis reveals that the benefit of using Long-CoT varies across problems: while some problems require elaborate reasoning, others show no improvement, or even degraded accuracy. This motivates adaptive reasoning strategies that tailor reasoning depth to the input. However, prior work primarily reduces redundancy within long reasoning paths, limiting exploration of more efficient strategies beyond the Long-CoT paradigm. To address this, we propose a novel two-stage framework for adaptive and efficient reasoning. First, we construct a hybrid reasoning model by merging long and short CoT models to enable diverse reasoning styles. Second, we apply bi-level preference training to guide the model to select suitable reasoning styles (group-level), and prefer concise and correct reasoning within each style group (instance-level). Experiments demonstrate that our method significantly reduces inference costs compared to other baseline approaches, while maintaining performance. Notably, on five mathematical datasets, the average length of reasoning is reduced by more than 50%, highlighting the potential of adaptive strategies to optimize reasoning efficiency in large language models. Our code is coming soon at https://github.com/StarDewXXX/AdaR1</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21659</guid>
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<pubDate>Wed, 30 Apr 2025 14:01:45 +0000</pubDate>
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<title>WebThinker: Empowering Large Reasoning Models with Deep Research Capability</title>
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<link>https://arxiv.org/abs/2504.21776</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21776.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xiaoxi Li, Jiajie Jin, Guanting Dong, Hongjin Qian, Yutao Zhu, Yongkang Wu, Ji-Rong Wen, Zhicheng Dou</p><p><b>Upvotes:</b> 37</p><p><b>Summary:</b> Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate impressive long-horizon reasoning capabilities. However, their reliance on static internal knowledge limits their performance on complex, knowledge-intensive tasks and hinders their ability to produce comprehensive research reports requiring synthesis of diverse web information. To address this, we propose WebThinker, a deep research agent that empowers LRMs to autonomously search the web, navigate web pages, and draft research reports during the reasoning process. WebThinker integrates a Deep Web Explorer module, enabling LRMs to dynamically search, navigate, and extract information from the web when encountering knowledge gaps. It also employs an Autonomous Think-Search-and-Draft strategy, allowing the model to seamlessly interleave reasoning, information gathering, and report writing in real time. To further enhance research tool utilization, we introduce an RL-based training strategy via iterative online Direct Preference Optimization (DPO). Extensive experiments on complex reasoning benchmarks (GPQA, GAIA, WebWalkerQA, HLE) and scientific report generation tasks (Glaive) demonstrate that WebThinker significantly outperforms existing methods and strong proprietary systems. Our approach enhances LRM reliability and applicability in complex scenarios, paving the way for more capable and versatile deep research systems. The code is available at https://github.com/RUC-NLPIR/WebThinker.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21776</guid>
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<pubDate>Wed, 30 Apr 2025 16:25:25 +0000</pubDate>
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<title>COMPACT: COMPositional Atomic-to-Complex Visual Capability Tuning</title>
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<link>https://arxiv.org/abs/2504.21850</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21850.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xindi Wu, Hee Seung Hwang, Polina Kirichenko, Olga Russakovsky</p><p><b>Upvotes:</b> 24</p><p><b>Summary:</b> Multimodal Large Language Models (MLLMs) excel at simple vision-language tasks but struggle when faced with complex tasks that require multiple capabilities, such as simultaneously recognizing objects, counting them, and understanding their spatial relationships. This might be partially the result of the fact that Visual Instruction Tuning (VIT), a critical training step for MLLMs, has traditionally focused on scaling data volume, but not the compositional complexity of training examples. We propose COMPACT (COMPositional Atomic-to-complex visual Capability Tuning), which generates a training dataset explicitly controlling for the compositional complexity of the training examples. The data from COMPACT allows MLLMs to train on combinations of atomic capabilities to learn complex capabilities more efficiently. Across all benchmarks, COMPACT achieves comparable performance to the LLaVA-665k VIT while using less than 10% of its data budget, and even outperforms it on several, especially those involving complex multi-capability tasks. For example, COMPACT achieves substantial 83.3% improvement on MMStar and 94.0% improvement on MM-Vet compared to the full-scale VIT on particularly complex questions that require four or more atomic capabilities. COMPACT offers a scalable, data-efficient, visual compositional tuning recipe to improve on complex visual-language tasks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21850</guid>
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<pubDate>Wed, 30 Apr 2025 17:57:22 +0000</pubDate>
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<title>A Survey of Interactive Generative Video</title>
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<link>https://arxiv.org/abs/2504.21853</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21853.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jiwen Yu, Yiran Qin, Haoxuan Che, Quande Liu, Xintao Wang, Pengfei Wan, Di Zhang, Kun Gai, Hao Chen, Xihui Liu</p><p><b>Upvotes:</b> 41</p><p><b>Summary:</b> Interactive Generative Video (IGV) has emerged as a crucial technology in response to the growing demand for high-quality, interactive video content across various domains. In this paper, we define IGV as a technology that combines generative capabilities to produce diverse high-quality video content with interactive features that enable user engagement through control signals and responsive feedback. We survey the current landscape of IGV applications, focusing on three major domains: 1) gaming, where IGV enables infinite exploration in virtual worlds; 2) embodied AI, where IGV serves as a physics-aware environment synthesizer for training agents in multimodal interaction with dynamically evolving scenes; and 3) autonomous driving, where IGV provides closed-loop simulation capabilities for safety-critical testing and validation. To guide future development, we propose a comprehensive framework that decomposes an ideal IGV system into five essential modules: Generation, Control, Memory, Dynamics, and Intelligence. Furthermore, we systematically analyze the technical challenges and future directions in realizing each component for an ideal IGV system, such as achieving real-time generation, enabling open-domain control, maintaining long-term coherence, simulating accurate physics, and integrating causal reasoning. We believe that this systematic analysis will facilitate future research and development in the field of IGV, ultimately advancing the technology toward more sophisticated and practical applications.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21853</guid>
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<pubDate>Wed, 30 Apr 2025 17:59:02 +0000</pubDate>
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<title>ReVision: High-Quality, Low-Cost Video Generation with Explicit 3D Physics Modeling for Complex Motion and Interaction</title>
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<link>https://arxiv.org/abs/2504.21855</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2504.21855.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qihao Liu, Ju He, Qihang Yu, Liang-Chieh Chen, Alan Yuille</p><p><b>Upvotes:</b> 10</p><p><b>Summary:</b> In recent years, video generation has seen significant advancements. However, challenges still persist in generating complex motions and interactions. To address these challenges, we introduce ReVision, a plug-and-play framework that explicitly integrates parameterized 3D physical knowledge into a pretrained conditional video generation model, significantly enhancing its ability to generate high-quality videos with complex motion and interactions. Specifically, ReVision consists of three stages. First, a video diffusion model is used to generate a coarse video. Next, we extract a set of 2D and 3D features from the coarse video to construct a 3D object-centric representation, which is then refined by our proposed parameterized physical prior model to produce an accurate 3D motion sequence. Finally, this refined motion sequence is fed back into the same video diffusion model as additional conditioning, enabling the generation of motion-consistent videos, even in scenarios involving complex actions and interactions. We validate the effectiveness of our approach on Stable Video Diffusion, where ReVision significantly improves motion fidelity and coherence. Remarkably, with only 1.5B parameters, it even outperforms a state-of-the-art video generation model with over 13B parameters on complex video generation by a substantial margin. Our results suggest that, by incorporating 3D physical knowledge, even a relatively small video diffusion model can generate complex motions and interactions with greater realism and controllability, offering a promising solution for physically plausible video generation.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2504.21855</guid>
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<pubDate>Wed, 30 Apr 2025 17:59:56 +0000</pubDate>
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<title>Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks</title>
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<link>https://arxiv.org/abs/2505.00234</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2505.00234.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Vishnu Sarukkai, Zhiqiang Xie, Kayvon Fatahalian</p><p><b>Upvotes:</b> 11</p><p><b>Summary:</b> Many methods for improving Large Language Model (LLM) agents for sequential decision-making tasks depend on task-specific knowledge engineering--such as prompt tuning, curated in-context examples, or customized observation and action spaces. Using these approaches, agent performance improves with the quality or amount of knowledge engineering invested. Instead, we investigate how LLM agents can automatically improve their performance by learning in-context from their own successful experiences on similar tasks. Rather than relying on task-specific knowledge engineering, we focus on constructing and refining a database of self-generated examples. We demonstrate that even a naive accumulation of successful trajectories across training tasks boosts test performance on three benchmarks: ALFWorld (73% to 89%), Wordcraft (55% to 64%), and InterCode-SQL (75% to 79%)--matching the performance the initial agent achieves if allowed two to three attempts per task. We then introduce two extensions: (1) database-level selection through population-based training to identify high-performing example collections, and (2) exemplar-level selection that retains individual trajectories based on their empirical utility as in-context examples. These extensions further enhance performance, achieving 91% on ALFWorld--matching more complex approaches that employ task-specific components and prompts. Our results demonstrate that automatic trajectory database construction offers a compelling alternative to labor-intensive knowledge engineering.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2505.00234</guid>
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<pubDate>Thu, 01 May 2025 00:48:12 +0000</pubDate>
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<title>KeySync: A Robust Approach for Leakage-free Lip Synchronization in High Resolution</title>
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<link>https://arxiv.org/abs/2505.00497</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2505.00497.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Antoni Bigata, Rodrigo Mira, Stella Bounareli, Michał Stypułkowski, Konstantinos Vougioukas, Stavros Petridis, Maja Pantic</p><p><b>Upvotes:</b> 10</p><p><b>Summary:</b> Lip synchronization, known as the task of aligning lip movements in an existing video with new input audio, is typically framed as a simpler variant of audio-driven facial animation. However, as well as suffering from the usual issues in talking head generation (e.g., temporal consistency), lip synchronization presents significant new challenges such as expression leakage from the input video and facial occlusions, which can severely impact real-world applications like automated dubbing, but are often neglected in existing works. To address these shortcomings, we present KeySync, a two-stage framework that succeeds in solving the issue of temporal consistency, while also incorporating solutions for leakage and occlusions using a carefully designed masking strategy. We show that KeySync achieves state-of-the-art results in lip reconstruction and cross-synchronization, improving visual quality and reducing expression leakage according to LipLeak, our novel leakage metric. Furthermore, we demonstrate the effectiveness of our new masking approach in handling occlusions and validate our architectural choices through several ablation studies. Code and model weights can be found at https://antonibigata.github.io/KeySync.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2505.00497</guid>
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<pubDate>Thu, 01 May 2025 12:56:17 +0000</pubDate>
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<title>A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic</title>
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<link>https://arxiv.org/abs/2505.00534</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2505.00534.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Muhammad Imran Zaman, Usama Ijaz Bajwa, Gulshan Saleem, Rana Hammad Raza</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Vision sensors are becoming more important in Intelligent Transportation Systems (ITS) for traffic monitoring, management, and optimization as the number of network cameras continues to rise. However, manual object tracking and matching across multiple non-overlapping cameras pose significant challenges in city-scale urban traffic scenarios. These challenges include handling diverse vehicle attributes, occlusions, illumination variations, shadows, and varying video resolutions. To address these issues, we propose an efficient and cost-effective deep learning-based framework for Multi-Object Multi-Camera Tracking (MO-MCT). The proposed framework utilizes Mask R-CNN for object detection and employs Non-Maximum Suppression (NMS) to select target objects from overlapping detections. Transfer learning is employed for re-identification, enabling the association and generation of vehicle tracklets across multiple cameras. Moreover, we leverage appropriate loss functions and distance measures to handle occlusion, illumination, and shadow challenges. The final solution identification module performs feature extraction using ResNet-152 coupled with Deep SORT based vehicle tracking. The proposed framework is evaluated on the 5th AI City Challenge dataset (Track 3), comprising 46 camera feeds. Among these 46 camera streams, 40 are used for model training and validation, while the remaining six are utilized for model testing. The proposed framework achieves competitive performance with an IDF1 score of 0.8289, and precision and recall scores of 0.9026 and 0.8527 respectively, demonstrating its effectiveness in robust and accurate vehicle tracking.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2505.00534</guid>
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<pubDate>Thu, 01 May 2025 14:00:25 +0000</pubDate>
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<title>100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models</title>
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<link>https://arxiv.org/abs/2505.00551</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2505.00551.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chong Zhang, Yue Deng, Xiang Lin, Bin Wang, Dianwen Ng, Hai Ye, Xingxuan Li, Yao Xiao, Zhanfeng Mo, Qi Zhang, Lidong Bing</p><p><b>Upvotes:</b> 24</p><p><b>Summary:</b> The recent development of reasoning language models (RLMs) represents a novel evolution in large language models. In particular, the recent release of DeepSeek-R1 has generated widespread social impact and sparked enthusiasm in the research community for exploring the explicit reasoning paradigm of language models. However, the implementation details of the released models have not been fully open-sourced by DeepSeek, including DeepSeek-R1-Zero, DeepSeek-R1, and the distilled small models. As a result, many replication studies have emerged aiming to reproduce the strong performance achieved by DeepSeek-R1, reaching comparable performance through similar training procedures and fully open-source data resources. These works have investigated feasible strategies for supervised fine-tuning (SFT) and reinforcement learning from verifiable rewards (RLVR), focusing on data preparation and method design, yielding various valuable insights. In this report, we provide a summary of recent replication studies to inspire future research. We primarily focus on SFT and RLVR as two main directions, introducing the details for data construction, method design and training procedure of current replication studies. Moreover, we conclude key findings from the implementation details and experimental results reported by these studies, anticipating to inspire future research. We also discuss additional techniques of enhancing RLMs, highlighting the potential of expanding the application scope of these models, and discussing the challenges in development. By this survey, we aim to help researchers and developers of RLMs stay updated with the latest advancements, and seek to inspire new ideas to further enhance RLMs.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2505.00551</guid>
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<pubDate>Thu, 01 May 2025 14:28:35 +0000</pubDate>
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<title>DeepCritic: Deliberate Critique with Large Language Models</title>
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<link>https://arxiv.org/abs/2505.00662</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2505.00662.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Wenkai Yang, Jingwen Chen, Yankai Lin, Ji-Rong Wen</p><p><b>Upvotes:</b> 40</p><p><b>Summary:</b> As Large Language Models (LLMs) are rapidly evolving, providing accurate feedback and scalable oversight on their outputs becomes an urgent and critical problem. Leveraging LLMs as critique models to achieve automated supervision is a promising solution. In this work, we focus on studying and enhancing the math critique ability of LLMs. Current LLM critics provide critiques that are too shallow and superficial on each step, leading to low judgment accuracy and struggling to offer sufficient feedback for the LLM generator to correct mistakes. To tackle this issue, we propose a novel and effective two-stage framework to develop LLM critics that are capable of deliberately critiquing on each reasoning step of math solutions. In the first stage, we utilize Qwen2.5-72B-Instruct to generate 4.5K long-form critiques as seed data for supervised fine-tuning. Each seed critique consists of deliberate step-wise critiques that includes multi-perspective verifications as well as in-depth critiques of initial critiques for each reasoning step. Then, we perform reinforcement learning on the fine-tuned model with either existing human-labeled data from PRM800K or our automatically annotated data obtained via Monte Carlo sampling-based correctness estimation, to further incentivize its critique ability. Our developed critique model built on Qwen2.5-7B-Instruct not only significantly outperforms existing LLM critics (including the same-sized DeepSeek-R1-distill models and GPT-4o) on various error identification benchmarks, but also more effectively helps the LLM generator refine erroneous steps through more detailed feedback.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2505.00662</guid>
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<pubDate>Thu, 01 May 2025 17:03:17 +0000</pubDate>
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<title>T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT</title>
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<link>https://arxiv.org/abs/2505.00703</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2505.00703.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Dongzhi Jiang, Ziyu Guo, Renrui Zhang, Zhuofan Zong, Hao Li, Le Zhuo, Shilin Yan, Pheng-Ann Heng, Hongsheng Li</p><p><b>Upvotes:</b> 32</p><p><b>Summary:</b> Recent advancements in large language models have demonstrated how chain-of-thought (CoT) and reinforcement learning (RL) can improve performance. However, applying such reasoning strategies to the visual generation domain remains largely unexplored. In this paper, we present T2I-R1, a novel reasoning-enhanced text-to-image generation model, powered by RL with a bi-level CoT reasoning process. Specifically, we identify two levels of CoT that can be utilized to enhance different stages of generation: (1) the semantic-level CoT for high-level planning of the prompt and (2) the token-level CoT for low-level pixel processing during patch-by-patch generation. To better coordinate these two levels of CoT, we introduce BiCoT-GRPO with an ensemble of generation rewards, which seamlessly optimizes both generation CoTs within the same training step. By applying our reasoning strategies to the baseline model, Janus-Pro, we achieve superior performance with 13% improvement on T2I-CompBench and 19% improvement on the WISE benchmark, even surpassing the state-of-the-art model FLUX.1. Code is available at: https://github.com/CaraJ7/T2I-R1</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2505.00703</guid>
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<pubDate>Thu, 01 May 2025 17:59:46 +0000</pubDate>
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