181 lines
52 KiB
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181 lines
52 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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<generator>python-feedgen</generator>
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<language>en</language>
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<lastBuildDate>Wed, 06 Aug 2025 00:02:36 +0000</lastBuildDate>
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<item>
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<title>Dens3R: A Foundation Model for 3D Geometry Prediction</title>
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<link>https://arxiv.org/abs/2507.16290</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.16290.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xianze Fang, Jingnan Gao, Zhe Wang, Zhuo Chen, Xingyu Ren, Jiangjing Lyu, Qiaomu Ren, Zhonglei Yang, Xiaokang Yang, Yichao Yan, Chengfei Lyu</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Recent advances in dense 3D reconstruction have led to significant progress, yet achieving accurate unified geometric prediction remains a major challenge. Most existing methods are limited to predicting a single geometry quantity from input images. However, geometric quantities such as depth, surface normals, and point maps are inherently correlated, and estimating them in isolation often fails to ensure consistency, thereby limiting both accuracy and practical applicability. This motivates us to explore a unified framework that explicitly models the structural coupling among different geometric properties to enable joint regression. In this paper, we present Dens3R, a 3D foundation model designed for joint geometric dense prediction and adaptable to a wide range of downstream tasks. Dens3R adopts a two-stage training framework to progressively build a pointmap representation that is both generalizable and intrinsically invariant. Specifically, we design a lightweight shared encoder-decoder backbone and introduce position-interpolated rotary positional encoding to maintain expressive power while enhancing robustness to high-resolution inputs. By integrating image-pair matching features with intrinsic invariance modeling, Dens3R accurately regresses multiple geometric quantities such as surface normals and depth, achieving consistent geometry perception from single-view to multi-view inputs. Additionally, we propose a post-processing pipeline that supports geometrically consistent multi-view inference. Extensive experiments demonstrate the superior performance of Dens3R across various dense 3D prediction tasks and highlight its potential for broader applications.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2507.16290</guid>
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<pubDate>Tue, 22 Jul 2025 07:22:30 +0000</pubDate>
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</item>
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<item>
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<title>InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation</title>
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<link>https://arxiv.org/abs/2507.17520</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2507.17520.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Shuai Yang, Hao Li, Yilun Chen, Bin Wang, Yang Tian, Tai Wang, Hanqing Wang, Feng Zhao, Yiyi Liao, Jiangmiao Pang</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> To operate effectively in the real world, robots must integrate multimodal reasoning with precise action generation. However, existing vision-language-action (VLA) models often sacrifice one for the other, narrow their abilities to task-specific manipulation data, and suffer catastrophic forgetting of pre-trained vision-language capabilities. To bridge this gap, we introduce InstructVLA, an end-to-end VLA model that preserves the flexible reasoning of large vision-language models (VLMs) while delivering leading manipulation performance. InstructVLA introduces a novel training paradigm, Vision-Language-Action Instruction Tuning (VLA-IT), which employs multimodal training with mixture-of-experts adaptation to jointly optimize textual reasoning and action generation on both standard VLM corpora and a curated 650K-sample VLA-IT dataset. On in-domain SimplerEnv tasks, InstructVLA achieves 30.5% improvement over SpatialVLA. To evaluate generalization, we introduce SimplerEnv-Instruct, an 80-task benchmark requiring closed-loop control and high-level instruction understanding, where it outperforms a fine-tuned OpenVLA by 92% and an action expert aided by GPT-4o by 29%. Additionally, InstructVLA surpasses baseline VLMs on multimodal tasks and exhibits inference-time scaling by leveraging textual reasoning to boost manipulation performance in both simulated and real-world settings. These results demonstrate InstructVLA's potential for bridging intuitive and steerable human-robot interaction with efficient policy learning.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2507.17520</guid>
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<pubDate>Wed, 23 Jul 2025 13:57:06 +0000</pubDate>
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</item>
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<item>
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<title>AgentTTS: Large Language Model Agent for Test-time Compute-optimal Scaling Strategy in Complex Tasks</title>
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<link>https://arxiv.org/abs/2508.00890</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.00890.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Fali Wang, Hui Liu, Zhenwei Dai, Jingying Zeng, Zhiwei Zhang, Zongyu Wu, Chen Luo, Zhen Li, Xianfeng Tang, Qi He, Suhang Wang</p><p><b>Upvotes:</b> 2</p><p><b>Summary:</b> Test-time scaling (TTS) enhances the performance of large language models (LLMs) by allocating additional compute resources during inference. However, existing research primarily investigates TTS in single-stage tasks; while many real-world problems are multi-stage complex tasks, composed of a sequence of heterogeneous subtasks with each subtask requires LLM of specific capability. Therefore, we study a novel problem: the test-time compute-optimal scaling in multi-stage complex tasks, aiming to select suitable models and allocate budgets per subtask to maximize overall performance. TTS in multi-stage tasks introduces two fundamental challenges: (i) The combinatorial search space of model and budget allocations, combined with the high cost of inference, makes brute-force search impractical. (ii) The optimal model and budget allocations across subtasks are interdependent, increasing the complexity of the compute-optimal search. To address this gap, we conduct extensive pilot experiments on four tasks across six datasets, deriving three empirical insights characterizing the behavior of LLMs in multi-stage complex tasks. Informed by these insights, we propose AgentTTS, an LLM-agent-based framework that autonomously searches for compute-optimal allocations through iterative feedback-driven interactions with the execution environment. Experimental results demonstrate that AgentTTS significantly outperforms traditional and other LLM-based baselines in search efficiency, and shows improved robustness to varying training set sizes and enhanced interpretability.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.00890</guid>
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<pubDate>Sat, 26 Jul 2025 19:21:18 +0000</pubDate>
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</item>
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<item>
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<title>Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning</title>
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<link>https://arxiv.org/abs/2508.00024</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.00024.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Sebastián Andrés Cajas Ordóñez, Luis Fernando Torres Torres, Mario Bifulco, Carlos Andrés Durán, Cristian Bosch, Ricardo Simón Carbajo</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Quantum Support Vector Machines face scalability challenges due to high-dimensional quantum states and hardware limitations. We propose an embedding-aware quantum-classical pipeline combining class-balanced k-means distillation with pretrained Vision Transformer embeddings. Our key finding: ViT embeddings uniquely enable quantum advantage, achieving up to 8.02% accuracy improvements over classical SVMs on Fashion-MNIST and 4.42% on MNIST, while CNN features show performance degradation. Using 16-qubit tensor network simulation via cuTensorNet, we provide the first systematic evidence that quantum kernel advantage depends critically on embedding choice, revealing fundamental synergy between transformer attention and quantum feature spaces. This provides a practical pathway for scalable quantum machine learning that leverages modern neural architectures.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.00024</guid>
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<pubDate>Mon, 28 Jul 2025 21:23:51 +0000</pubDate>
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</item>
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<title>Cyber-Zero: Training Cybersecurity Agents without Runtime</title>
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<link>https://arxiv.org/abs/2508.00910</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.00910.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Terry Yue Zhuo, Dingmin Wang, Hantian Ding, Varun Kumar, Zijian Wang</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Large Language Models (LLMs) have achieved remarkable success in software engineering tasks when trained with executable runtime environments, particularly in resolving GitHub issues. However, such runtime environments are often unavailable in other domains, especially cybersecurity, where challenge configurations and execution contexts are ephemeral or restricted. We present Cyber-Zero, the first runtime-free framework for synthesizing high-quality agent trajectories to train cybersecurity LLMs. Cyber-Zero leverages publicly available CTF writeups and employs persona-driven LLM simulation to reverse-engineer runtime behaviors and generate realistic, long-horizon interaction sequences without actual environments. Using trajectories synthesized by Cyber-Zero, we train LLM-based agents that achieve up to 13.1% absolute performance gains over baseline models on three prominent CTF benchmarks: InterCode-CTF, NYU CTF Bench, and Cybench. Our best model, Cyber-Zero-32B, establishes new state-of-the-art performance among open-weight models, matching the capabilities of proprietary systems like DeepSeek-V3-0324 and Claude-3.5-Sonnet while offering superior cost-effectiveness, and demonstrating that runtime-free trajectory synthesis can effectively democratize the development of state-of-the-art cybersecurity agents.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.00910</guid>
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<pubDate>Tue, 29 Jul 2025 18:10:37 +0000</pubDate>
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</item>
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<title>Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report</title>
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<link>https://arxiv.org/abs/2508.01059</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01059.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Sajana Weerawardhena, Paul Kassianik, Blaine Nelson, Baturay Saglam, Anu Vellore, Aman Priyanshu, Supriti Vijay, Massimo Aufiero, Arthur Goldblatt, Fraser Burch, Ed Li, Jianliang He, Dhruv Kedia, Kojin Oshiba, Zhouran Yang, Yaron Singer, Amin Karbasi</p><p><b>Upvotes:</b> 21</p><p><b>Summary:</b> Large language models (LLMs) have shown remarkable success across many domains, yet their integration into cybersecurity applications remains limited due to a lack of general-purpose cybersecurity data, representational complexity, and safety and regulatory concerns. To address this gap, we previously introduced Foundation-Sec-8B, a cybersecurity-focused LLM suitable for fine-tuning on downstream tasks. That model, however, was not designed for chat-style interactions or instruction-following. In this report, we release Foundation-Sec-8B-Instruct: a model specifically trained for general-purpose cybersecurity dialogue. Built on Foundation-Sec-8B, it combines domain-specific knowledge with instruction-following, conversational capabilities, and alignment with human preferences to produce high-quality, relevant responses. Comprehensive evaluations show that Foundation-Sec-8B-Instruct outperforms Llama 3.1-8B-Instruct on a range of cybersecurity tasks while matching its instruction-following performance. It is also competitive with GPT-4o-mini on cyber threat intelligence and instruction-following tasks. We envision Foundation-Sec-8B-Instruct becoming an indispensable assistant in the daily workflows of cybersecurity professionals. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01059</guid>
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<pubDate>Fri, 01 Aug 2025 20:25:57 +0000</pubDate>
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<title>Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?</title>
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<link>https://arxiv.org/abs/2508.01109</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01109.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Satiyabooshan Murugaboopathy, Connor T. Jerzak, Adel Daoud</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> We investigate whether socio-economic indicators like household wealth leave recoverable imprints in satellite imagery (capturing physical features) and Internet-sourced text (reflecting historical/economic narratives). Using Demographic and Health Survey (DHS) data from African neighborhoods, we pair Landsat images with LLM-generated textual descriptions conditioned on location/year and text retrieved by an AI search agent from web sources. We develop a multimodal framework predicting household wealth (International Wealth Index) through five pipelines: (i) vision model on satellite images, (ii) LLM using only location/year, (iii) AI agent searching/synthesizing web text, (iv) joint image-text encoder, (v) ensemble of all signals. Our framework yields three contributions. First, fusing vision and agent/LLM text outperforms vision-only baselines in wealth prediction (e.g., R-squared of 0.77 vs. 0.63 on out-of-sample splits), with LLM-internal knowledge proving more effective than agent-retrieved text, improving robustness to out-of-country and out-of-time generalization. Second, we find partial representational convergence: fused embeddings from vision/language modalities correlate moderately (median cosine similarity of 0.60 after alignment), suggesting a shared latent code of material well-being while retaining complementary details, consistent with the Platonic Representation Hypothesis. Although LLM-only text outperforms agent-retrieved data, challenging our Agent-Induced Novelty Hypothesis, modest gains from combining agent data in some splits weakly support the notion that agent-gathered information introduces unique representational structures not fully captured by static LLM knowledge. Third, we release a large-scale multimodal dataset comprising more than 60,000 DHS clusters linked to satellite images, LLM-generated descriptions, and agent-retrieved texts.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01109</guid>
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<pubDate>Fri, 01 Aug 2025 23:07:16 +0000</pubDate>
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<title>Personalized Safety Alignment for Text-to-Image Diffusion Models</title>
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<link>https://arxiv.org/abs/2508.01151</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01151.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yu Lei, Jinbin Bai, Qingyu Shi, Aosong Feng, Kaidong Yu</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> Text-to-image diffusion models have revolutionized visual content generation, but current safety mechanisms apply uniform standards that often fail to account for individual user preferences. These models overlook the diverse safety boundaries shaped by factors like age, mental health, and personal beliefs. To address this, we propose Personalized Safety Alignment (PSA), a framework that allows user-specific control over safety behaviors in generative models. PSA integrates personalized user profiles into the diffusion process, adjusting the model's behavior to match individual safety preferences while preserving image quality. We introduce a new dataset, Sage, which captures user-specific safety preferences and incorporates these profiles through a cross-attention mechanism. Experiments show that PSA outperforms existing methods in harmful content suppression and aligns generated content better with user constraints, achieving higher Win Rate and Pass Rate scores. Our code, data, and models are publicly available at https://torpedo2648.github.io/PSAlign/.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01151</guid>
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<pubDate>Sat, 02 Aug 2025 02:23:20 +0000</pubDate>
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<title>Exploitation Is All You Need... for Exploration</title>
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<link>https://arxiv.org/abs/2508.01287</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01287.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Micah Rentschler, Jesse Roberts</p><p><b>Upvotes:</b> 3</p><p><b>Summary:</b> Ensuring sufficient exploration is a central challenge when training meta-reinforcement learning (meta-RL) agents to solve novel environments. Conventional solutions to the exploration-exploitation dilemma inject explicit incentives such as randomization, uncertainty bonuses, or intrinsic rewards to encourage exploration. In this work, we hypothesize that an agent trained solely to maximize a greedy (exploitation-only) objective can nonetheless exhibit emergent exploratory behavior, provided three conditions are met: (1) Recurring Environmental Structure, where the environment features repeatable regularities that allow past experience to inform future choices; (2) Agent Memory, enabling the agent to retain and utilize historical interaction data; and (3) Long-Horizon Credit Assignment, where learning propagates returns over a time frame sufficient for the delayed benefits of exploration to inform current decisions. Through experiments in stochastic multi-armed bandits and temporally extended gridworlds, we observe that, when both structure and memory are present, a policy trained on a strictly greedy objective exhibits information-seeking exploratory behavior. We further demonstrate, through controlled ablations, that emergent exploration vanishes if either environmental structure or agent memory is absent (Conditions 1 & 2). Surprisingly, removing long-horizon credit assignment (Condition 3) does not always prevent emergent exploration-a result we attribute to the pseudo-Thompson Sampling effect. These findings suggest that, under the right prerequisites, exploration and exploitation need not be treated as orthogonal objectives but can emerge from a unified reward-maximization process.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01287</guid>
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<pubDate>Sat, 02 Aug 2025 09:42:59 +0000</pubDate>
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<title>Artificial Intelligence and Misinformation in Art: Can Vision Language Models Judge the Hand or the Machine Behind the Canvas?</title>
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<link>https://arxiv.org/abs/2508.01408</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01408.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tarian Fu, Javier Conde, Gonzalo Martínez, Pedro Reviriego, Elena Merino-Gómez, Fernando Moral</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> The attribution of artworks in general and of paintings in particular has always been an issue in art. The advent of powerful artificial intelligence models that can generate and analyze images creates new challenges for painting attribution. On the one hand, AI models can create images that mimic the style of a painter, which can be incorrectly attributed, for example, by other AI models. On the other hand, AI models may not be able to correctly identify the artist for real paintings, inducing users to incorrectly attribute paintings. In this paper, both problems are experimentally studied using state-of-the-art AI models for image generation and analysis on a large dataset with close to 40,000 paintings from 128 artists. The results show that vision language models have limited capabilities to: 1) perform canvas attribution and 2) to identify AI generated images. As users increasingly rely on queries to AI models to get information, these results show the need to improve the capabilities of VLMs to reliably perform artist attribution and detection of AI generated images to prevent the spread of incorrect information.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01408</guid>
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<pubDate>Sat, 02 Aug 2025 15:27:31 +0000</pubDate>
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<title>RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong Learning in Physical Embodied Systems</title>
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<link>https://arxiv.org/abs/2508.01415</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01415.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Mingcong Lei, Honghao Cai, Zezhou Cui, Liangchen Tan, Junkun Hong, Gehan Hu, Shuangyu Zhu, Yimou Wu, Shaohan Jiang, Ge Wang, Zhen Li, Shuguang Cui, Yiming Zhao, Yatong Han</p><p><b>Upvotes:</b> 4</p><p><b>Summary:</b> We present RoboMemory, a brain-inspired multi-memory framework for lifelong learning in physical embodied systems, addressing critical challenges in real-world environments: continuous learning, multi-module memory latency, task correlation capture, and infinite-loop mitigation in closed-loop planning. Grounded in cognitive neuroscience, it integrates four core modules: the Information Preprocessor (thalamus-like), the Lifelong Embodied Memory System (hippocampus-like), the Closed-Loop Planning Module (prefrontal lobe-like), and the Low-Level Executer (cerebellum-like) to enable long-term planning and cumulative learning. The Lifelong Embodied Memory System, central to the framework, alleviates inference speed issues in complex memory frameworks via parallelized updates/retrieval across Spatial, Temporal, Episodic, and Semantic submodules. It incorporates a dynamic Knowledge Graph (KG) and consistent architectural design to enhance memory consistency and scalability. Evaluations on EmbodiedBench show RoboMemory outperforms the open-source baseline (Qwen2.5-VL-72B-Ins) by 25% in average success rate and surpasses the closed-source State-of-the-Art (SOTA) (Claude3.5-Sonnet) by 5%, establishing new SOTA. Ablation studies validate key components (critic, spatial memory, long-term memory), while real-world deployment confirms its lifelong learning capability with significantly improved success rates across repeated tasks. RoboMemory alleviates high latency challenges with scalability, serving as a foundational reference for integrating multi-modal memory systems in physical robots.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01415</guid>
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<pubDate>Sat, 02 Aug 2025 15:39:42 +0000</pubDate>
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<title>A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models</title>
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<link>https://arxiv.org/abs/2508.01548</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01548.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Quan-Sheng Zeng, Yunheng Li, Qilong Wang, Peng-Tao Jiang, Zuxuan Wu, Ming-Ming Cheng, Qibin Hou</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Visual token compression is critical for Large Vision-Language Models (LVLMs) to efficiently process high-resolution inputs. Existing methods that typically adopt fixed compression ratios cannot adapt to scenes of varying complexity, often causing imprecise pruning that discards informative visual tokens and results in degraded model performance. To address this issue, we introduce a dynamic pruning framework, GlimpsePrune, inspired by human cognition. It takes a data-driven ''glimpse'' and prunes irrelevant visual tokens in a single forward pass before answer generation. This approach prunes 92.6% of visual tokens while on average fully retaining the baseline performance on free-form VQA tasks. The reduced computational cost also enables more effective fine-tuning: an enhanced GlimpsePrune+ achieves 110% of the baseline performance while maintaining a similarly high pruning rate. Our work paves a new way for building more powerful and efficient LVLMs.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01548</guid>
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<pubDate>Sun, 03 Aug 2025 02:15:43 +0000</pubDate>
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<title>Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and Regional Languages Around the Globe</title>
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<link>https://arxiv.org/abs/2508.01691</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01691.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Tiantian Feng, Kevin Huang, Anfeng Xu, Xuan Shi, Thanathai Lertpetchpun, Jihwan Lee, Yoonjeong Lee, Dani Byrd, Shrikanth Narayanan</p><p><b>Upvotes:</b> 6</p><p><b>Summary:</b> We present Voxlect, a novel benchmark for modeling dialects and regional languages worldwide using speech foundation models. Specifically, we report comprehensive benchmark evaluations on dialects and regional language varieties in English, Arabic, Mandarin and Cantonese, Tibetan, Indic languages, Thai, Spanish, French, German, Brazilian Portuguese, and Italian. Our study used over 2 million training utterances from 30 publicly available speech corpora that are provided with dialectal information. We evaluate the performance of several widely used speech foundation models in classifying speech dialects. We assess the robustness of the dialectal models under noisy conditions and present an error analysis that highlights modeling results aligned with geographic continuity. In addition to benchmarking dialect classification, we demonstrate several downstream applications enabled by Voxlect. Specifically, we show that Voxlect can be applied to augment existing speech recognition datasets with dialect information, enabling a more detailed analysis of ASR performance across dialectal variations. Voxlect is also used as a tool to evaluate the performance of speech generation systems. Voxlect is publicly available with the license of the RAIL family at: https://github.com/tiantiaf0627/voxlect.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01691</guid>
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<pubDate>Sun, 03 Aug 2025 09:51:28 +0000</pubDate>
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<title>Uncertainty-Based Methods for Automated Process Reward Data Construction and Output Aggregation in Mathematical Reasoning</title>
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<link>https://arxiv.org/abs/2508.01773</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01773.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Jiuzhou Han, Wray Buntine, Ehsan Shareghi</p><p><b>Upvotes:</b> 0</p><p><b>Summary:</b> Large language models have demonstrated remarkable capabilities in complex mathematical reasoning tasks, but they inevitably generate errors throughout multi-step solutions. Process-level Reward Models (PRMs) have shown great promise by providing supervision and evaluation at each intermediate step, thereby effectively improving the models' reasoning abilities. However, training effective PRMs requires high-quality process reward data, yet existing methods for constructing such data are often labour-intensive or inefficient. In this paper, we propose an uncertainty-driven framework for automated process reward data construction, encompassing both data generation and annotation processes for PRMs. Additionally, we identify the limitations of both majority vote and PRMs, and introduce two generic uncertainty-aware output aggregation methods: Hybrid Majority Reward Vote and Weighted Reward Frequency Vote, which combine the strengths of majority vote with PRMs. Extensive experiments on ProcessBench, MATH, and GSMPlus show the effectiveness and efficiency of the proposed PRM data construction framework, and demonstrate that the two output aggregation methods further improve the mathematical reasoning abilities across diverse PRMs. The code and data will be publicly available at https://github.com/Jiuzhouh/UnPRM.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01773</guid>
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<pubDate>Sun, 03 Aug 2025 14:14:13 +0000</pubDate>
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<title>SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension</title>
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<link>https://arxiv.org/abs/2508.01959</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.01959.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Junjie Wu, Jiangnan Li, Yuqing Li, Lemao Liu, Liyan Xu, Jiwei Li, Dit-Yan Yeung, Jie Zhou, Mo Yu</p><p><b>Upvotes:</b> 38</p><p><b>Summary:</b> Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units for retrieval. However, due to dependencies across the original document, contextual information is often essential for accurately interpreting each chunk. To address this, prior work has explored encoding longer context windows to produce embeddings for longer chunks. Despite these efforts, gains in retrieval and downstream tasks remain limited. This is because (1) longer chunks strain the capacity of embedding models due to the increased amount of information they must encode, and (2) many real-world applications still require returning localized evidence due to constraints on model or human bandwidth. We propose an alternative approach to this challenge by representing short chunks in a way that is conditioned on a broader context window to enhance retrieval performance -- i.e., situating a chunk's meaning within its context. We further show that existing embedding models are not well-equipped to encode such situated context effectively, and thus introduce a new training paradigm and develop the situated embedding models (SitEmb). To evaluate our method, we curate a book-plot retrieval dataset specifically designed to assess situated retrieval capabilities. On this benchmark, our SitEmb-v1 model based on BGE-M3 substantially outperforms state-of-the-art embedding models, including several with up to 7-8B parameters, with only 1B parameters. Our 8B SitEmb-v1.5 model further improves performance by over 10% and shows strong results across different languages and several downstream applications.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.01959</guid>
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<pubDate>Sun, 03 Aug 2025 23:59:31 +0000</pubDate>
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<title>Fitness aligned structural modeling enables scalable virtual screening with AuroBind</title>
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<link>https://arxiv.org/abs/2508.02137</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02137.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhongyue Zhang, Jiahua Rao, Jie Zhong, Weiqiang Bai, Dongxue Wang, Shaobo Ning, Lifeng Qiao, Sheng Xu, Runze Ma, Will Hua, Jack Xiaoyu Chen, Odin Zhang, Wei Lu, Hanyi Feng, He Yang, Xinchao Shi, Rui Li, Wanli Ouyang, Xinzhu Ma, Jiahao Wang, Jixian Zhang, Jia Duan, Siqi Sun, Jian Zhang, Shuangjia Zheng</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Most human proteins remain undrugged, over 96% of human proteins remain unexploited by approved therapeutics. While structure-based virtual screening promises to expand the druggable proteome, existing methods lack atomic-level precision and fail to predict binding fitness, limiting translational impact. We present AuroBind, a scalable virtual screening framework that fine-tunes a custom atomic-level structural model on million-scale chemogenomic data. AuroBind integrates direct preference optimization, self-distillation from high-confidence complexes, and a teacher-student acceleration strategy to jointly predict ligand-bound structures and binding fitness. The proposed models outperform state-of-the-art models on structural and functional benchmarks while enabling 100,000-fold faster screening across ultra-large compound libraries. In a prospective screen across ten disease-relevant targets, AuroBind achieved experimental hit rates of 7-69%, with top compounds reaching sub-nanomolar to picomolar potency. For the orphan GPCRs GPR151 and GPR160, AuroBind identified both agonists and antagonists with success rates of 16-30%, and functional assays confirmed GPR160 modulation in liver and prostate cancer models. AuroBind offers a generalizable framework for structure-function learning and high-throughput molecular screening, bridging the gap between structure prediction and therapeutic discovery.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02137</guid>
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<pubDate>Mon, 04 Aug 2025 07:34:48 +0000</pubDate>
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<title>Beyond the Trade-off: Self-Supervised Reinforcement Learning for Reasoning Models' Instruction Following</title>
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<link>https://arxiv.org/abs/2508.02150</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02150.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qingyu Ren, Qianyu He, Bowei Zhang, Jie Zeng, Jiaqing Liang, Yanghua Xiao, Weikang Zhou, Zeye Sun, Fei Yu</p><p><b>Upvotes:</b> 24</p><p><b>Summary:</b> Reasoning models excel in complex problem solving but exhibit a concerning trade off between reasoning capabilities and instruction following abilities. Existing approaches for improving instruction following rely on stronger external models, creating methodological bottlenecks and practical limitations including increased costs and accessibility constraints. We propose a self-supervised RL framework that leverages reasoning models' own internal signals to improve instruction following capabilities without external supervision. Extensive experiments demonstrate that our framework significantly improves instruction following capabilities while maintaining reasoning performance, offering a scalable and cost-effective approach to enhance instruction following in reasoning models. The data and code are publicly available at https://github.com/Rainier-rq/verl-if.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02150</guid>
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<pubDate>Mon, 04 Aug 2025 07:48:59 +0000</pubDate>
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<title>SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic Bidirectional Machine Translation System</title>
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<link>https://arxiv.org/abs/2508.02268</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02268.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Serry Sibaee, Omer Nacar, Yasser Al-Habashi, Adel Ammar, Wadii Boulila</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> The rich linguistic landscape of the Arab world is characterized by a significant gap between Modern Standard Arabic (MSA), the language of formal communication, and the diverse regional dialects used in everyday life. This diglossia presents a formidable challenge for natural language processing, particularly machine translation. This paper introduces SHAMI-MT, a bidirectional machine translation system specifically engineered to bridge the communication gap between MSA and the Syrian dialect. We present two specialized models, one for MSA-to-Shami and another for Shami-to-MSA translation, both built upon the state-of-the-art AraT5v2-base-1024 architecture. The models were fine-tuned on the comprehensive Nabra dataset and rigorously evaluated on unseen data from the MADAR corpus. Our MSA-to-Shami model achieved an outstanding average quality score of 4.01 out of 5.0 when judged by OPENAI model GPT-4.1, demonstrating its ability to produce translations that are not only accurate but also dialectally authentic. This work provides a crucial, high-fidelity tool for a previously underserved language pair, advancing the field of dialectal Arabic translation and offering significant applications in content localization, cultural heritage, and intercultural communication.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02268</guid>
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<pubDate>Mon, 04 Aug 2025 10:21:11 +0000</pubDate>
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<title>Dynaword: From One-shot to Continuously Developed Datasets</title>
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<link>https://arxiv.org/abs/2508.02271</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02271.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Kenneth Enevoldsen, Kristian Nørgaard Jensen, Jan Kostkan, Balázs Szabó, Márton Kardos, Kirten Vad, Andrea Blasi Núñez, Gianluca Barmina, Jacob Nielsen, Rasmus Larsen, Peter Vahlstrup, Per Møldrup Dalum, Desmond Elliott, Lukas Galke, Peter Schneider-Kamp, Kristoffer Nielbo</p><p><b>Upvotes:</b> 7</p><p><b>Summary:</b> Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguously licensed sources restricting use, sharing, and derivative works; (2) static dataset releases that prevent community contributions and diminish longevity; and (3) quality assurance processes restricted to publishing teams rather than leveraging community expertise. To address these limitations, we introduce two contributions: the Dynaword approach and Danish Dynaword. The Dynaword approach is a framework for creating large-scale, open datasets that can be continuously updated through community collaboration. Danish Dynaword is a concrete implementation that validates this approach and demonstrates its potential. Danish Dynaword contains over four times as many tokens as comparable releases, is exclusively openly licensed, and has received multiple contributions across industry and research. The repository includes light-weight tests to ensure data formatting, quality, and documentation, establishing a sustainable framework for ongoing community contributions and dataset evolution.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02271</guid>
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<pubDate>Mon, 04 Aug 2025 10:30:42 +0000</pubDate>
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<title>CellForge: Agentic Design of Virtual Cell Models</title>
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<link>https://arxiv.org/abs/2508.02276</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02276.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Xiangru Tang, Zhuoyun Yu, Jiapeng Chen, Yan Cui, Daniel Shao, Weixu Wang, Fang Wu, Yuchen Zhuang, Wenqi Shi, Zhi Huang, Arman Cohan, Xihong Lin, Fabian Theis, Smita Krishnaswamy, Mark Gerstein</p><p><b>Upvotes:</b> 29</p><p><b>Summary:</b> Virtual cell modeling represents an emerging frontier at the intersection of artificial intelligence and biology, aiming to predict quantities such as responses to diverse perturbations quantitatively. However, autonomously building computational models for virtual cells is challenging due to the complexity of biological systems, the heterogeneity of data modalities, and the need for domain-specific expertise across multiple disciplines. Here, we introduce CellForge, an agentic system that leverages a multi-agent framework that transforms presented biological datasets and research objectives directly into optimized computational models for virtual cells. More specifically, given only raw single-cell multi-omics data and task descriptions as input, CellForge outputs both an optimized model architecture and executable code for training virtual cell models and inference. The framework integrates three core modules: Task Analysis for presented dataset characterization and relevant literature retrieval, Method Design, where specialized agents collaboratively develop optimized modeling strategies, and Experiment Execution for automated generation of code. The agents in the Design module are separated into experts with differing perspectives and a central moderator, and have to collaboratively exchange solutions until they achieve a reasonable consensus. We demonstrate CellForge's capabilities in single-cell perturbation prediction, using six diverse datasets that encompass gene knockouts, drug treatments, and cytokine stimulations across multiple modalities. CellForge consistently outperforms task-specific state-of-the-art methods. Overall, CellForge demonstrates how iterative interaction between LLM agents with differing perspectives provides better solutions than directly addressing a modeling challenge. Our code is publicly available at https://github.com/gersteinlab/CellForge.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02276</guid>
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<pubDate>Mon, 04 Aug 2025 10:43:31 +0000</pubDate>
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<title>VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo</title>
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<link>https://arxiv.org/abs/2508.02317</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02317.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Qianli Ma, Yaowei Zheng, Zhelun Shi, Zhongkai Zhao, Bin Jia, Ziyue Huang, Zhiqi Lin, Youjie Li, Jiacheng Yang, Yanghua Peng, Zhi Zhang, Xin Liu</p><p><b>Upvotes:</b> 8</p><p><b>Summary:</b> Recent advances in large language models (LLMs) have driven impressive progress in omni-modal understanding and generation. However, training omni-modal LLMs remains a significant challenge due to the heterogeneous model architectures required to process diverse modalities, necessitating sophisticated system design for efficient large-scale training. Existing frameworks typically entangle model definition with parallel logic, incurring limited scalability and substantial engineering overhead for end-to-end omni-modal training. % We present \veomni, a modular and efficient training framework to accelerate the development of omni-modal LLMs. \veomni introduces model-centric distributed recipes that decouples communication from computation, enabling efficient 3D parallelism on omni-modal LLMs. \veomni also features a flexible configuration interface supporting seamless integration of new modalities with minimal code change. % Using \veomni, a omni-modal mixture-of-experts (MoE) model with 30B parameters can be trained with over 2,800 tokens/sec/GPU throughput and scale to 160K context lengths via 3D parallelism on 128 GPUs, showcasing its superior efficiency and scalability for training large omni-modal LLMs.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02317</guid>
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<pubDate>Mon, 04 Aug 2025 11:33:04 +0000</pubDate>
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<title>Qwen-Image Technical Report</title>
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<link>https://arxiv.org/abs/2508.02324</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02324.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng-ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhengyi Wang, An Yang, Bowen Yu, Chen Cheng, Dayiheng Liu, Deqing Li, Hang Zhang, Hao Meng, Hu Wei, Jingyuan Ni, Kai Chen, Kuan Cao, Liang Peng, Lin Qu, Minggang Wu, Peng Wang, Shuting Yu, Tingkun Wen, Wensen Feng, Xiaoxiao Xu, Yi Wang, Yichang Zhang, Yongqiang Zhu, Yujia Wu, Yuxuan Cai, Zenan Liu</p><p><b>Upvotes:</b> 82</p><p><b>Summary:</b> We present Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. To address the challenges of complex text rendering, we design a comprehensive data pipeline that includes large-scale data collection, filtering, annotation, synthesis, and balancing. Moreover, we adopt a progressive training strategy that starts with non-text-to-text rendering, evolves from simple to complex textual inputs, and gradually scales up to paragraph-level descriptions. This curriculum learning approach substantially enhances the model's native text rendering capabilities. As a result, Qwen-Image not only performs exceptionally well in alphabetic languages such as English, but also achieves remarkable progress on more challenging logographic languages like Chinese. To enhance image editing consistency, we introduce an improved multi-task training paradigm that incorporates not only traditional text-to-image (T2I) and text-image-to-image (TI2I) tasks but also image-to-image (I2I) reconstruction, effectively aligning the latent representations between Qwen2.5-VL and MMDiT. Furthermore, we separately feed the original image into Qwen2.5-VL and the VAE encoder to obtain semantic and reconstructive representations, respectively. This dual-encoding mechanism enables the editing module to strike a balance between preserving semantic consistency and maintaining visual fidelity. Qwen-Image achieves state-of-the-art performance, demonstrating its strong capabilities in both image generation and editing across multiple benchmarks.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02324</guid>
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<pubDate>Mon, 04 Aug 2025 11:49:20 +0000</pubDate>
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<title>Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction</title>
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<link>https://arxiv.org/abs/2508.02558</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02558.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Yuerong Song, Xiaoran Liu, Ruixiao Li, Zhigeng Liu, Zengfeng Huang, Qipeng Guo, Ziwei He, Xipeng Qiu</p><p><b>Upvotes:</b> 5</p><p><b>Summary:</b> Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and parallel decoding but suffer from prohibitive quadratic computational complexity and memory overhead during inference. Current caching techniques accelerate decoding by storing full-layer states, yet impose substantial memory usage that limit long-context applications. Our analysis of attention patterns in dLLMs reveals persistent cross-layer sparsity, with pivotal tokens remaining salient across decoding steps and low-relevance tokens staying unimportant, motivating selective cache eviction. We propose Sparse-dLLM, the first training-free framework integrating dynamic cache eviction with sparse attention via delayed bidirectional sparse caching. By leveraging the stability of token saliency over steps, it retains critical tokens and dynamically evicts unimportant prefix/suffix entries using an attention-guided strategy. Extensive experiments on LLaDA and Dream series demonstrate Sparse-dLLM achieves up to 10times higher throughput than vanilla dLLMs, with comparable performance and similar peak memory costs, outperforming previous methods in efficiency and effectiveness.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02558</guid>
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<pubDate>Mon, 04 Aug 2025 16:14:03 +0000</pubDate>
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<title>ReMoMask: Retrieval-Augmented Masked Motion Generation</title>
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<link>https://arxiv.org/abs/2508.02605</link>
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<description><p><img src="https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02605.png" alt="Paper thumbnail" style="max-width: 300px; height: auto;" /></p><p><b>Authors:</b> Zhengdao Li, Siheng Wang, Zeyu Zhang, Hao Tang</p><p><b>Upvotes:</b> 1</p><p><b>Summary:</b> Text-to-Motion (T2M) generation aims to synthesize realistic and semantically aligned human motion sequences from natural language descriptions. However, current approaches face dual challenges: Generative models (e.g., diffusion models) suffer from limited diversity, error accumulation, and physical implausibility, while Retrieval-Augmented Generation (RAG) methods exhibit diffusion inertia, partial-mode collapse, and asynchronous artifacts. To address these limitations, we propose ReMoMask, a unified framework integrating three key innovations: 1) A Bidirectional Momentum Text-Motion Model decouples negative sample scale from batch size via momentum queues, substantially improving cross-modal retrieval precision; 2) A Semantic Spatio-temporal Attention mechanism enforces biomechanical constraints during part-level fusion to eliminate asynchronous artifacts; 3) RAG-Classier-Free Guidance incorporates minor unconditional generation to enhance generalization. Built upon MoMask's RVQ-VAE, ReMoMask efficiently generates temporally coherent motions in minimal steps. Extensive experiments on standard benchmarks demonstrate the state-of-the-art performance of ReMoMask, achieving a 3.88% and 10.97% improvement in FID scores on HumanML3D and KIT-ML, respectively, compared to the previous SOTA method RAG-T2M. Code: https://github.com/AIGeeksGroup/ReMoMask. Website: https://aigeeksgroup.github.io/ReMoMask.</p></description>
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<guid isPermaLink="false">https://arxiv.org/abs/2508.02605</guid>
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<pubDate>Mon, 04 Aug 2025 16:56:35 +0000</pubDate>
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