174 lines
49 KiB
XML
174 lines
49 KiB
XML
<?xml version='1.0' encoding='UTF-8'?>
|
||
<rss xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/" version="2.0">
|
||
<channel>
|
||
<title>Hugging Face Daily Papers</title>
|
||
<link>https://huggingface.co/papers</link>
|
||
<description>Daily research papers curated by the Hugging Face community.</description>
|
||
<docs>http://www.rssboard.org/rss-specification</docs>
|
||
<generator>python-feedgen</generator>
|
||
<language>en</language>
|
||
<lastBuildDate>Tue, 04 Nov 2025 00:02:34 +0000</lastBuildDate>
|
||
<item>
|
||
<title>Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning</title>
|
||
<link>https://arxiv.org/abs/2510.20150</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.20150.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yaochen Zhu, Harald Steck, Dawen Liang, Yinhan He, Jundong Li, Nathan Kallus</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Large language models (LLMs) are reshaping the recommender system paradigm by enabling users to express preferences and receive recommendations through conversations. Yet, aligning LLMs to the recommendation task remains challenging: pretrained LLMs often generate out-of-catalog items, violate required output formats, and their ranking quality degrades sharply toward the end of the generated list. To this end, we propose ConvRec-R1, a two-stage framework for end-to-end training of LLM-based conversational recommender systems. In Stage 1, we construct a behavioral-cloning dataset with a Remap-Reflect-Adjust pipeline, which produces high-quality, catalog-grounded demonstrations from powerful blackbox LLMs to warm-start the RL training. In Stage 2, we propose Rank-GRPO, a principled extension of group relative policy optimization (GRPO) tailored to tasks with rank-style outputs. Rank-GRPO treats each rank in the recommendation list as the unit instead of token (too fine-grained) or sequence (too coarse), redefining rewards to remove non-causal credit assignment and introducing a rank-level importance ratio based on the geometric mean of rank-wise token probabilities to stabilize policy updates. Experiments on the public Reddit-v2 dataset show that ConvRec-R1 converges faster and achieves higher Recall and NDCG than GRPO-style baselines. Code and datasets are released at https://github.com/yaochenzhu/Rank-GRPO.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.20150</guid>
|
||
<pubDate>Thu, 23 Oct 2025 02:56:00 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Revisiting Multimodal Positional Encoding in Vision-Language Models</title>
|
||
<link>https://arxiv.org/abs/2510.23095</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.23095.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jie Huang, Xuejing Liu, Sibo Song, Ruibing Hou, Hong Chang, Junyang Lin, Shuai Bai</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Multimodal position encoding is essential for vision-language models, yet there has been little systematic investigation into multimodal position encoding. We conduct a comprehensive analysis of multimodal Rotary Positional Embedding (RoPE) by examining its two core components: position design and frequency allocation. Through extensive experiments, we identify three key guidelines: positional coherence, full frequency utilization, and preservation of textual priors-ensuring unambiguous layout, rich representation, and faithful transfer from the pre-trained LLM. Based on these insights, we propose Multi-Head RoPE (MHRoPE) and MRoPE-Interleave (MRoPE-I), two simple and plug-and-play variants that require no architectural changes. Our methods consistently outperform existing approaches across diverse benchmarks, with significant improvements in both general and fine-grained multimodal understanding. Code will be avaliable at https://github.com/JJJYmmm/Multimodal-RoPEs.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.23095</guid>
|
||
<pubDate>Mon, 27 Oct 2025 08:00:46 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>A Survey on Efficient Vision-Language-Action Models</title>
|
||
<link>https://arxiv.org/abs/2510.24795</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.24795.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhaoshu Yu, Bo Wang, Pengpeng Zeng, Haonan Zhang, Ji Zhang, Lianli Gao, Jingkuan Song, Nicu Sebe, Heng Tao Shen</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Vision-Language-Action models (VLAs) represent a significant frontier in embodied intelligence, aiming to bridge digital knowledge with physical-world interaction. While these models have demonstrated remarkable generalist capabilities, their deployment is severely hampered by the substantial computational and data requirements inherent to their underlying large-scale foundation models. Motivated by the urgent need to address these challenges, this survey presents the first comprehensive review of Efficient Vision-Language-Action models (Efficient VLAs) across the entire data-model-training process. Specifically, we introduce a unified taxonomy to systematically organize the disparate efforts in this domain, categorizing current techniques into three core pillars: (1) Efficient Model Design, focusing on efficient architectures and model compression; (2) Efficient Training, which reduces computational burdens during model learning; and (3) Efficient Data Collection, which addresses the bottlenecks in acquiring and utilizing robotic data. Through a critical review of state-of-the-art methods within this framework, this survey not only establishes a foundational reference for the community but also summarizes representative applications, delineates key challenges, and charts a roadmap for future research. We maintain a continuously updated project page to track our latest developments: https://evla-survey.github.io/</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.24795</guid>
|
||
<pubDate>Mon, 27 Oct 2025 17:57:33 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Beyond Objects: Contextual Synthetic Data Generation for Fine-Grained Classification</title>
|
||
<link>https://arxiv.org/abs/2510.24078</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.24078.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> William Yang, Xindi Wu, Zhiwei Deng, Esin Tureci, Olga Russakovsky</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Text-to-image (T2I) models are increasingly used for synthetic dataset generation, but generating effective synthetic training data for classification remains challenging. Fine-tuning a T2I model with a few real examples can help improve the quality of synthetic training data; however, it may also cause overfitting and reduce diversity in the generated samples. We propose a fine-tuning strategy BOB (BeyondOBjects) to mitigate these concerns for fine-grained classification. Given a small set of real examples, we first extract class-agnostic attributes such as scene background and object pose. We then explicitly condition on these attributes during fine-tuning of the T2I model and marginalize them out during generation. This design mitigates overfitting, preserves the T2I model's generative prior, reduces estimation errors, and further minimizes unintended inter-class associations. Extensive experiments across multiple T2I models, backbones, and datasets show that our method achieves state-of-the-art performance in low-shot fine-grained classification when augmented with synthetic data. Concretely, BOB outperforms DataDream by 7.4% on the Aircraft dataset (from 50.0% to 57.4% when fine-tuning a CLIP classifier with five real images augmented with 100 synthetic images). In three of the four benchmarks, fine-tuning downstream models with 5 real images augmented with BOB achieves better performance than fine-tuning with 10 real images. Collectively, BOB outperforms prior art in 18 of 24 experimental settings, with 2+% accuracy improvements in 14 of these settings.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.24078</guid>
|
||
<pubDate>Tue, 28 Oct 2025 05:40:14 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows</title>
|
||
<link>https://arxiv.org/abs/2510.24411</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.24411.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qiushi Sun, Mukai Li, Zhoumianze Liu, Zhihui Xie, Fangzhi Xu, Zhangyue Yin, Kanzhi Cheng, Zehao Li, Zichen Ding, Qi Liu, Zhiyong Wu, Zhuosheng Zhang, Ben Kao, Lingpeng Kong</p><p><b>Upvotes:</b> 60</p><p><b>Summary:</b> Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms. While these agents hold great promise for advancing digital automation, their potential for unsafe operations, such as system compromise and privacy leakage, is raising significant concerns. Detecting these safety concerns across the vast and complex operational space of mobile environments presents a formidable challenge that remains critically underexplored. To establish a foundation for mobile agent safety research, we introduce MobileRisk-Live, a dynamic sandbox environment accompanied by a safety detection benchmark comprising realistic trajectories with fine-grained annotations. Built upon this, we propose OS-Sentinel, a novel hybrid safety detection framework that synergistically combines a Formal Verifier for detecting explicit system-level violations with a VLM-based Contextual Judge for assessing contextual risks and agent actions. Experiments show that OS-Sentinel achieves 10%-30% improvements over existing approaches across multiple metrics. Further analysis provides critical insights that foster the development of safer and more reliable autonomous mobile agents.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.24411</guid>
|
||
<pubDate>Tue, 28 Oct 2025 13:22:39 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens</title>
|
||
<link>https://arxiv.org/abs/2510.24940</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.24940.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yinhan He, Wendy Zheng, Yaochen Zhu, Zaiyi Zheng, Lin Su, Sriram Vasudevan, Qi Guo, Liangjie Hong, Jundong Li</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> The verbosity of Chain-of-Thought (CoT) reasoning hinders its mass deployment in efficiency-critical applications. Recently, implicit CoT approaches have emerged, which encode reasoning steps within LLM's hidden embeddings (termed ``implicit reasoning'') rather than explicit tokens. This approach accelerates CoT by reducing the reasoning length and bypassing some LLM components. However, existing implicit CoT methods face two significant challenges: (1) they fail to preserve the semantic alignment between the implicit reasoning (when transformed to natural language) and the ground-truth reasoning, resulting in a significant CoT performance degradation, and (2) they focus on reducing the length of the implicit reasoning; however, they neglect the considerable time cost for an LLM to generate one individual implicit reasoning token. To tackle these challenges, we propose a novel semantically-aligned implicit CoT framework termed SemCoT. In particular, for the first challenge, we design a contrastively trained sentence transformer that evaluates semantic alignment between implicit and explicit reasoning, which is used to enforce semantic preservation during implicit reasoning optimization. To address the second challenge, we introduce an efficient implicit reasoning generator by finetuning a lightweight language model using knowledge distillation. This generator is guided by our sentence transformer to distill ground-truth reasoning into semantically aligned implicit reasoning, while also optimizing for accuracy. SemCoT is the first approach that enhances CoT efficiency by jointly optimizing token-level generation speed and preserving semantic alignment with ground-truth reasoning. Extensive experiments demonstrate the superior performance of SemCoT compared to state-of-the-art methods in both efficiency and effectiveness. Our code can be found at https://github.com/YinhanHe123/SemCoT/.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.24940</guid>
|
||
<pubDate>Tue, 28 Oct 2025 20:11:54 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Monopoly Deal: A Benchmark Environment for Bounded One-Sided Response Games</title>
|
||
<link>https://arxiv.org/abs/2510.25080</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.25080.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Will Wolf</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Card games are widely used to study sequential decision-making under uncertainty, with real-world analogues in negotiation, finance, and cybersecurity. These games typically fall into three categories based on the flow of control: strictly sequential (players alternate single actions), deterministic response (some actions trigger a fixed outcome), and unbounded reciprocal response (alternating counterplays are permitted). A less-explored but strategically rich structure is the bounded one-sided response, where a player's action briefly transfers control to the opponent, who must satisfy a fixed condition through one or more moves before the turn resolves. We term games featuring this mechanism Bounded One-Sided Response Games (BORGs). We introduce a modified version of Monopoly Deal as a benchmark environment that isolates this dynamic, where a Rent action forces the opponent to choose payment assets. The gold-standard algorithm, Counterfactual Regret Minimization (CFR), converges on effective strategies without novel algorithmic extensions. A lightweight full-stack research platform unifies the environment, a parallelized CFR runtime, and a human-playable web interface. The trained CFR agent and source code are available at https://monopolydeal.ai.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.25080</guid>
|
||
<pubDate>Wed, 29 Oct 2025 01:38:19 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats</title>
|
||
<link>https://arxiv.org/abs/2510.25602</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.25602.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mengzhao Chen, Meng Wu, Hui Jin, Zhihang Yuan, Jing Liu, Chaoyi Zhang, Yunshui Li, Jie Huang, Jin Ma, Zeyue Xue, Zhiheng Liu, Xingyan Bin, Ping Luo</p><p><b>Upvotes:</b> 47</p><p><b>Summary:</b> Modern AI hardware, such as Nvidia's Blackwell architecture, is increasingly embracing low-precision floating-point (FP) formats to handle the pervasive activation outliers in Large Language Models (LLMs). Despite this industry trend, a unified comparison of FP and integer (INT) quantization across varying granularities has been missing, leaving algorithm and hardware co-design without clear guidance. This paper fills that gap by systematically investigating the trade-offs between FP and INT formats. We reveal a critical performance crossover: while FP excels in coarse-grained quantization, the comparison at fine-grained (block-wise) levels is more nuanced. Our comprehensive comparison demonstrates that for popular 8-bit fine-grained formats (e.g., MX with block size 32), MXINT8 is superior to its FP counterpart in both algorithmic accuracy and hardware efficiency. However, for 4-bit formats, FP (e.g., MXFP4, NVFP4) often holds an accuracy advantage , though we show that NVINT4 can surpass NVFP4 when outlier-mitigation techniques like Hadamard rotation are applied. We also introduce a symmetric clipping method that resolves gradient bias in fine-grained low-bit INT training, enabling nearly lossless performance for MXINT8 training. These findings challenge the current hardware trajectory, demonstrating that a one-size-fits-all FP approach is suboptimal and advocating that fine-grained INT formats, particularly MXINT8, offer a better balance of accuracy, power, and efficiency for future AI accelerators.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.25602</guid>
|
||
<pubDate>Wed, 29 Oct 2025 15:11:53 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>π_RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models</title>
|
||
<link>https://arxiv.org/abs/2510.25889</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.25889.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Kang Chen, Zhihao Liu, Tonghe Zhang, Zhen Guo, Si Xu, Hao Lin, Hongzhi Zang, Quanlu Zhang, Zhaofei Yu, Guoliang Fan, Tiejun Huang, Yu Wang, Chao Yu</p><p><b>Upvotes:</b> 40</p><p><b>Summary:</b> Vision-Language-Action (VLA) models enable robots to understand and perform complex tasks from multimodal input. Although recent work explores using reinforcement learning (RL) to automate the laborious data collection process in scaling supervised fine-tuning (SFT), applying large-scale RL to flow-based VLAs (e.g., pi_0, pi_{0.5}) remains challenging due to intractable action log-likelihoods from iterative denoising. We address this challenge with pi_{RL}, an open-source framework for training flow-based VLAs in parallel simulation. pi_{RL} implements two RL algorithms: (1) {Flow-Noise} models the denoising process as a discrete-time MDP with a learnable noise network for exact log-likelihood computation. (2) {Flow-SDE} integrates denoising with agent-environment interaction, formulating a two-layer MDP that employs ODE-to-SDE conversion for efficient RL exploration. We evaluate pi_{RL} on LIBERO and ManiSkill benchmarks. On LIBERO, pi_{RL} boosts few-shot SFT models pi_0 and pi_{0.5} from 57.6% to 97.6% and from 77.1% to 98.3%, respectively. In ManiSkill, we train pi_{RL} in 320 parallel environments, improving pi_0 from 41.6% to 85.7% and pi_{0.5} from 40.0% to 84.8% across 4352 pick-and-place tasks, demonstrating scalable multitask RL under heterogeneous simulation. Overall, pi_{RL} achieves significant performance gains and stronger generalization over SFT-models, validating the effectiveness of online RL for flow-based VLAs.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.25889</guid>
|
||
<pubDate>Wed, 29 Oct 2025 18:37:39 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>MisSynth: Improving MISSCI Logical Fallacies Classification with Synthetic Data</title>
|
||
<link>https://arxiv.org/abs/2510.26345</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.26345.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mykhailo Poliakov, Nadiya Shvai</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Health-related misinformation is very prevalent and potentially harmful. It is difficult to identify, especially when claims distort or misinterpret scientific findings. We investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large language models (LLMs) to recognize fallacious arguments using the MISSCI dataset and framework. In this work, we propose MisSynth, a pipeline that applies retrieval-augmented generation (RAG) to produce synthetic fallacy samples, which are then used to fine-tune an LLM model. Our results show substantial accuracy gains with fine-tuned models compared to vanilla baselines. For instance, the LLaMA 3.1 8B fine-tuned model achieved an over 35% F1-score absolute improvement on the MISSCI test split over its vanilla baseline. We demonstrate that introducing synthetic fallacy data to augment limited annotated resources can significantly enhance zero-shot LLM classification performance on real-world scientific misinformation tasks, even with limited computational resources. The code and synthetic dataset are available on https://github.com/mxpoliakov/MisSynth.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.26345</guid>
|
||
<pubDate>Thu, 30 Oct 2025 10:52:43 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Value Drifts: Tracing Value Alignment During LLM Post-Training</title>
|
||
<link>https://arxiv.org/abs/2510.26707</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.26707.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mehar Bhatia, Shravan Nayak, Gaurav Kamath, Marius Mosbach, Karolina Stańczak, Vered Shwartz, Siva Reddy</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw on their general knowledge but also to align with certain human value systems. Therefore, studying the alignment of LLMs with human values has become a crucial field of inquiry. Prior work, however, mostly focuses on evaluating the alignment of fully trained models, overlooking the training dynamics by which models learn to express human values. In this work, we investigate how and at which stage value alignment arises during the course of a model's post-training. Our analysis disentangles the effects of post-training algorithms and datasets, measuring both the magnitude and time of value drifts during training. Experimenting with Llama-3 and Qwen-3 models of different sizes and popular supervised fine-tuning (SFT) and preference optimization datasets and algorithms, we find that the SFT phase generally establishes a model's values, and subsequent preference optimization rarely re-aligns these values. Furthermore, using a synthetic preference dataset that enables controlled manipulation of values, we find that different preference optimization algorithms lead to different value alignment outcomes, even when preference data is held constant. Our findings provide actionable insights into how values are learned during post-training and help to inform data curation, as well as the selection of models and algorithms for preference optimization to improve model alignment to human values.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.26707</guid>
|
||
<pubDate>Thu, 30 Oct 2025 17:09:09 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning</title>
|
||
<link>https://arxiv.org/abs/2510.27492</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27492.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jiawei Gu, Yunzhuo Hao, Huichen Will Wang, Linjie Li, Michael Qizhe Shieh, Yejin Choi, Ranjay Krishna, Yu Cheng</p><p><b>Upvotes:</b> 56</p><p><b>Summary:</b> Multimodal reasoning requires iterative coordination between language and vision, yet it remains unclear what constitutes a meaningful interleaved chain of thought. We posit that text and image thoughts should function as complementary, rather than isomorphic, modalities that mutually advance reasoning. Guided by this principle, we build ThinkMorph, a unified model fine-tuned on 24K high-quality interleaved reasoning traces spanning tasks with varying visual engagement. ThinkMorph learns to generate progressive text-image reasoning steps that concretely manipulate visual content while maintaining coherent verbal logic. It delivers large gains on vision-centric benchmarks (averaging 34.7% over the base model) and generalizes to out-of-domain tasks, matching or surpassing larger and proprietary VLMs. Beyond performance, ThinkMorph exhibits emergent multimodal intelligence, including unseen visual manipulation skills, adaptive switching between reasoning modes, and better test-time scaling through diversified multimodal thoughts.These findings suggest promising directions for characterizing the emergent capabilities of unified models for multimodal reasoning.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27492</guid>
|
||
<pubDate>Thu, 30 Oct 2025 17:51:38 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Defeating the Training-Inference Mismatch via FP16</title>
|
||
<link>https://arxiv.org/abs/2510.26788</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.26788.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Penghui Qi, Zichen Liu, Xiangxin Zhou, Tianyu Pang, Chao Du, Wee Sun Lee, Min Lin</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> Reinforcement learning (RL) fine-tuning of large language models (LLMs) often suffers from instability due to the numerical mismatch between the training and inference policies. While prior work has attempted to mitigate this issue through algorithmic corrections or engineering alignments, we show that its root cause lies in the floating point precision itself. The widely adopted BF16, despite its large dynamic range, introduces large rounding errors that breaks the consistency between training and inference. In this work, we demonstrate that simply reverting to FP16 effectively eliminates this mismatch. The change is simple, fully supported by modern frameworks with only a few lines of code change, and requires no modification to the model architecture or learning algorithm. Our results suggest that using FP16 uniformly yields more stable optimization, faster convergence, and stronger performance across diverse tasks, algorithms and frameworks. We hope these findings motivate a broader reconsideration of precision trade-offs in RL fine-tuning.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.26788</guid>
|
||
<pubDate>Thu, 30 Oct 2025 17:58:11 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>The Denario project: Deep knowledge AI agents for scientific discovery</title>
|
||
<link>https://arxiv.org/abs/2510.26887</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.26887.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo, Adrian E. Bayer, Aidan Acquah, Chetana Amancharla, Almog Barzilay-Siegal, Pablo Bermejo, Camille Bilodeau, Pablo Cárdenas Ramírez, Miles Cranmer, Urbano L. França, ChangHoon Hahn, Yan-Fei Jiang, Raul Jimenez, Jun-Young Lee, Antonio Lerario, Osman Mamun, Thomas Meier, Anupam A. Ojha, Pavlos Protopapas, Shimanto Roy, David N. Spergel, Pedro Tarancón-Álvarez, Ujjwal Tiwari, Matteo Viel, Digvijay Wadekar, Chi Wang, Bonny Y. Wang, Licong Xu, Yossi Yovel, Shuwen Yue, Wen-Han Zhou, Qiyao Zhu, Jiajun Zou, Íñigo Zubeldia</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.26887</guid>
|
||
<pubDate>Thu, 30 Oct 2025 18:00:12 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Limits of Generalization in RLVR: Two Case Studies in Mathematical Reasoning</title>
|
||
<link>https://arxiv.org/abs/2510.27044</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27044.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Md Tanvirul Alam, Nidhi Rastogi</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Mathematical reasoning is a central challenge for large language models (LLMs), requiring not only correct answers but also faithful reasoning processes. Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising approach for enhancing such capabilities; however, its ability to foster genuine reasoning remains unclear. We investigate RLVR on two combinatorial problems with fully verifiable solutions: Activity Scheduling and the Longest Increasing Subsequence, using carefully curated datasets with unique optima. Across multiple reward designs, we find that RLVR improves evaluation metrics but often by reinforcing superficial heuristics rather than acquiring new reasoning strategies. These findings highlight the limits of RLVR generalization, emphasizing the importance of benchmarks that disentangle genuine mathematical reasoning from shortcut exploitation and provide faithful measures of progress. Code available at https://github.com/xashru/rlvr-seq-generalization.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27044</guid>
|
||
<pubDate>Thu, 30 Oct 2025 23:16:02 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery</title>
|
||
<link>https://arxiv.org/abs/2510.27224</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27224.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mahmoud El Hussieni, Bahadır K. Güntürk, Hasan F. Ateş, Oğuz Hanoğlu</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Accurate building instance segmentation and height classification are critical for urban planning, 3D city modeling, and infrastructure monitoring. This paper presents a detailed analysis of YOLOv11, the recent advancement in the YOLO series of deep learning models, focusing on its application to joint building extraction and discrete height classification from satellite imagery. YOLOv11 builds on the strengths of earlier YOLO models by introducing a more efficient architecture that better combines features at different scales, improves object localization accuracy, and enhances performance in complex urban scenes. Using the DFC2023 Track 2 dataset -- which includes over 125,000 annotated buildings across 12 cities -- we evaluate YOLOv11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP). Our findings demonstrate that YOLOv11 achieves strong instance segmentation performance with 60.4\% mAP@50 and 38.3\% mAP@50--95 while maintaining robust classification accuracy across five predefined height tiers. The model excels in handling occlusions, complex building shapes, and class imbalance, particularly for rare high-rise structures. Comparative analysis confirms that YOLOv11 outperforms earlier multitask frameworks in both detection accuracy and inference speed, making it well-suited for real-time, large-scale urban mapping. This research highlights YOLOv11's potential to advance semantic urban reconstruction through streamlined categorical height modeling, offering actionable insights for future developments in remote sensing and geospatial intelligence.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27224</guid>
|
||
<pubDate>Fri, 31 Oct 2025 06:37:08 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Higher-order Linear Attention</title>
|
||
<link>https://arxiv.org/abs/2510.27258</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27258.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yifan Zhang, Zhen Qin, Quanquan Gu</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> The quadratic cost of scaled dot-product attention is a central obstacle to scaling autoregressive language models to long contexts. Linear-time attention and State Space Models (SSMs) provide scalable alternatives but are typically restricted to first-order or kernel-based approximations, which can limit expressivity. We introduce Higher-order Linear Attention (HLA), a causal, streaming mechanism that realizes higher interactions via compact prefix sufficient statistics. In the second-order case, HLA maintains a constant-size state and computes per-token outputs in linear time without materializing any n times n matrices. We give closed-form streaming identities, a strictly causal masked variant using two additional summaries, and a chunk-parallel training scheme based on associative scans that reproduces the activations of a serial recurrence exactly. We further outline extensions to third and higher orders. Collectively, these results position HLA as a principled, scalable building block that combines attention-like, data-dependent mixing with the efficiency of modern recurrent architectures. Project Page: https://github.com/yifanzhang-pro/HLA.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27258</guid>
|
||
<pubDate>Fri, 31 Oct 2025 07:54:37 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>HyperClick: Advancing Reliable GUI Grounding via Uncertainty Calibration</title>
|
||
<link>https://arxiv.org/abs/2510.27266</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27266.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shaojie Zhang, Pei Fu, Ruoceng Zhang, Jiahui Yang, Anan Du, Xiuwen Xi, Shaokang Wang, Ying Huang, Bin Qin, Zhenbo Luo, Jian Luan</p><p><b>Upvotes:</b> 15</p><p><b>Summary:</b> Autonomous Graphical User Interface (GUI) agents rely on accurate GUI grounding, which maps language instructions to on-screen coordinates, to execute user commands. However, current models, whether trained via supervised fine-tuning (SFT) or reinforcement fine-tuning (RFT), lack self-awareness of their capability boundaries, leading to overconfidence and unreliable predictions. We first systematically evaluate probabilistic and verbalized confidence in general and GUI-specific models, revealing a misalignment between confidence and actual accuracy, which is particularly critical in dynamic GUI automation tasks, where single errors can cause task failure. To address this, we propose HyperClick, a novel framework that enhances reliable GUI grounding through uncertainty calibration. HyperClick introduces a dual reward mechanism, combining a binary reward for correct actions with a truncated Gaussian-based spatial confidence modeling, calibrated using the Brier score. This approach jointly optimizes grounding accuracy and confidence reliability, fostering introspective self-criticism. Extensive experiments on seven challenge benchmarks show that HyperClick achieves state-of-the-art performance while providing well-calibrated confidence. By enabling explicit confidence calibration and introspective self-criticism, HyperClick reduces overconfidence and supports more reliable GUI automation.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27266</guid>
|
||
<pubDate>Fri, 31 Oct 2025 08:07:02 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning</title>
|
||
<link>https://arxiv.org/abs/2510.27606</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27606.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuhong Liu, Beichen Zhang, Yuhang Zang, Yuhang Cao, Long Xing, Xiaoyi Dong, Haodong Duan, Dahua Lin, Jiaqi Wang</p><p><b>Upvotes:</b> 20</p><p><b>Summary:</b> Spatial understanding remains a weakness of Large Vision-Language Models (LVLMs). Existing supervised fine-tuning (SFT) and recent reinforcement learning with verifiable rewards (RLVR) pipelines depend on costly supervision, specialized tools, or constrained environments that limit scale. We introduce Spatial-SSRL, a self-supervised RL paradigm that derives verifiable signals directly from ordinary RGB or RGB-D images. Spatial-SSRL automatically formulates five pretext tasks that capture 2D and 3D spatial structure: shuffled patch reordering, flipped patch recognition, cropped patch inpainting, regional depth ordering, and relative 3D position prediction. These tasks provide ground-truth answers that are easy to verify and require no human or LVLM annotation. Training on our tasks substantially improves spatial reasoning while preserving general visual capabilities. On seven spatial understanding benchmarks in both image and video settings, Spatial-SSRL delivers average accuracy gains of 4.63% (3B) and 3.89% (7B) over the Qwen2.5-VL baselines. Our results show that simple, intrinsic supervision enables RLVR at scale and provides a practical route to stronger spatial intelligence in LVLMs.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27606</guid>
|
||
<pubDate>Fri, 31 Oct 2025 16:30:08 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model</title>
|
||
<link>https://arxiv.org/abs/2510.27607</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27607.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> John Won, Kyungmin Lee, Huiwon Jang, Dongyoung Kim, Jinwoo Shin</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> Recently, augmenting Vision-Language-Action models (VLAs) with world modeling has shown promise in improving robotic policy learning. However, it remains challenging to jointly predict next-state observations and action sequences because of the inherent difference between the two modalities. To address this, we propose DUal-STream diffusion (DUST), a world-model augmented VLA framework that handles the modality conflict and enhances the performance of VLAs across diverse tasks. Specifically, we propose a multimodal diffusion transformer architecture that explicitly maintains separate modality streams while still enabling cross-modal knowledge sharing. In addition, we introduce independent noise perturbations for each modality and a decoupled flow-matching loss. This design enables the model to learn the joint distribution in a bidirectional manner while avoiding the need for a unified latent space. Based on the decoupling of modalities during training, we also introduce a joint sampling method that supports test-time scaling, where action and vision tokens evolve asynchronously at different rates. Through experiments on simulated benchmarks such as RoboCasa and GR-1, DUST achieves up to 6% gains over baseline methods, while our test-time scaling approach provides an additional 2-5% boost. On real-world tasks with the Franka Research 3, DUST improves success rates by 13%, confirming its effectiveness beyond simulation. Furthermore, pre-training on action-free videos from BridgeV2 yields significant transfer gains on RoboCasa, underscoring DUST's potential for large-scale VLA pretraining.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27607</guid>
|
||
<pubDate>Fri, 31 Oct 2025 16:32:12 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Visual Backdoor Attacks on MLLM Embodied Decision Making via Contrastive Trigger Learning</title>
|
||
<link>https://arxiv.org/abs/2510.27623</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27623.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qiusi Zhan, Hyeonjeong Ha, Rui Yang, Sirui Xu, Hanyang Chen, Liang-Yan Gui, Yu-Xiong Wang, Huan Zhang, Heng Ji, Daniel Kang</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Multimodal large language models (MLLMs) have advanced embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs. However, such vision driven embodied agents open a new attack surface: visual backdoor attacks, where the agent behaves normally until a visual trigger appears in the scene, then persistently executes an attacker-specified multi-step policy. We introduce BEAT, the first framework to inject such visual backdoors into MLLM-based embodied agents using objects in the environments as triggers. Unlike textual triggers, object triggers exhibit wide variation across viewpoints and lighting, making them difficult to implant reliably. BEAT addresses this challenge by (1) constructing a training set that spans diverse scenes, tasks, and trigger placements to expose agents to trigger variability, and (2) introducing a two-stage training scheme that first applies supervised fine-tuning (SFT) and then our novel Contrastive Trigger Learning (CTL). CTL formulates trigger discrimination as preference learning between trigger-present and trigger-free inputs, explicitly sharpening the decision boundaries to ensure precise backdoor activation. Across various embodied agent benchmarks and MLLMs, BEAT achieves attack success rates up to 80%, while maintaining strong benign task performance, and generalizes reliably to out-of-distribution trigger placements. Notably, compared to naive SFT, CTL boosts backdoor activation accuracy up to 39% under limited backdoor data. These findings expose a critical yet unexplored security risk in MLLM-based embodied agents, underscoring the need for robust defenses before real-world deployment.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27623</guid>
|
||
<pubDate>Fri, 31 Oct 2025 16:50:49 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals</title>
|
||
<link>https://arxiv.org/abs/2510.27684</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27684.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xiangyu Fan, Zesong Qiu, Zhuguanyu Wu, Fanzhou Wang, Zhiqian Lin, Tianxiang Ren, Dahua Lin, Ruihao Gong, Lei Yang</p><p><b>Upvotes:</b> 14</p><p><b>Summary:</b> Distribution Matching Distillation (DMD) distills score-based generative models into efficient one-step generators, without requiring a one-to-one correspondence with the sampling trajectories of their teachers. However, limited model capacity causes one-step distilled models underperform on complex generative tasks, e.g., synthesizing intricate object motions in text-to-video generation. Directly extending DMD to multi-step distillation increases memory usage and computational depth, leading to instability and reduced efficiency. While prior works propose stochastic gradient truncation as a potential solution, we observe that it substantially reduces the generation diversity of multi-step distilled models, bringing it down to the level of their one-step counterparts. To address these limitations, we propose Phased DMD, a multi-step distillation framework that bridges the idea of phase-wise distillation with Mixture-of-Experts (MoE), reducing learning difficulty while enhancing model capacity. Phased DMD is built upon two key ideas: progressive distribution matching and score matching within subintervals. First, our model divides the SNR range into subintervals, progressively refining the model to higher SNR levels, to better capture complex distributions. Next, to ensure the training objective within each subinterval is accurate, we have conducted rigorous mathematical derivations. We validate Phased DMD by distilling state-of-the-art image and video generation models, including Qwen-Image (20B parameters) and Wan2.2 (28B parameters). Experimental results demonstrate that Phased DMD preserves output diversity better than DMD while retaining key generative capabilities. We will release our code and models.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27684</guid>
|
||
<pubDate>Fri, 31 Oct 2025 17:55:10 +0000</pubDate>
|
||
</item>
|
||
<item>
|
||
<title>Continuous Autoregressive Language Models</title>
|
||
<link>https://arxiv.org/abs/2510.27688</link>
|
||
<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2510.27688.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chenze Shao, Darren Li, Fandong Meng, Jie Zhou</p><p><b>Upvotes:</b> 29</p><p><b>Summary:</b> The efficiency of large language models (LLMs) is fundamentally limited by their sequential, token-by-token generation process. We argue that overcoming this bottleneck requires a new design axis for LLM scaling: increasing the semantic bandwidth of each generative step. To this end, we introduce Continuous Autoregressive Language Models (CALM), a paradigm shift from discrete next-token prediction to continuous next-vector prediction. CALM uses a high-fidelity autoencoder to compress a chunk of K tokens into a single continuous vector, from which the original tokens can be reconstructed with over 99.9\% accuracy. This allows us to model language as a sequence of continuous vectors instead of discrete tokens, which reduces the number of generative steps by a factor of K. The paradigm shift necessitates a new modeling toolkit; therefore, we develop a comprehensive likelihood-free framework that enables robust training, evaluation, and controllable sampling in the continuous domain. Experiments show that CALM significantly improves the performance-compute trade-off, achieving the performance of strong discrete baselines at a significantly lower computational cost. More importantly, these findings establish next-vector prediction as a powerful and scalable pathway towards ultra-efficient language models. Code: https://github.com/shaochenze/calm. Project: https://shaochenze.github.io/blog/2025/CALM.</p></description>
|
||
<guid isPermaLink="false">https://arxiv.org/abs/2510.27688</guid>
|
||
<pubDate>Fri, 31 Oct 2025 17:58:11 +0000</pubDate>
|
||
</item>
|
||
</channel>
|
||
</rss>
|