✨FASA: Frequency-aware Sparse Attention
📝 Summary:
FASA addresses LLM KV cache memory for long contexts by dynamically predicting token importance. It leverages functional sparsity in RoPEs frequency chunks to identify critical tokens for focused attention. This significantly reduces memory and computation while maintaining high performance.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03152
• PDF: https://arxiv.org/pdf/2602.03152
==================================
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#LLM #SparseAttention #MemoryEfficiency #DeepLearning #NLP
📝 Summary:
FASA addresses LLM KV cache memory for long contexts by dynamically predicting token importance. It leverages functional sparsity in RoPEs frequency chunks to identify critical tokens for focused attention. This significantly reduces memory and computation while maintaining high performance.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03152
• PDF: https://arxiv.org/pdf/2602.03152
==================================
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#LLM #SparseAttention #MemoryEfficiency #DeepLearning #NLP
✨AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations
📝 Summary:
AutoFigure is an agentic AI framework that automatically generates publication-ready scientific illustrations from long-form text. It uses extensive thinking and validation to ensure structural soundness and aesthetic appeal. Supported by FigureBench, a large new benchmark, AutoFigure surpasses b...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03828
• PDF: https://arxiv.org/pdf/2602.03828
• Github: https://github.com/ResearAI/AutoFigure-Edit
✨ Datasets citing this paper:
• https://huggingface.co/datasets/WestlakeNLP/FigureBench
==================================
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#AI #GenerativeAI #ScientificIllustrations #ResearchTools #AcademicPublishing
📝 Summary:
AutoFigure is an agentic AI framework that automatically generates publication-ready scientific illustrations from long-form text. It uses extensive thinking and validation to ensure structural soundness and aesthetic appeal. Supported by FigureBench, a large new benchmark, AutoFigure surpasses b...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03828
• PDF: https://arxiv.org/pdf/2602.03828
• Github: https://github.com/ResearAI/AutoFigure-Edit
✨ Datasets citing this paper:
• https://huggingface.co/datasets/WestlakeNLP/FigureBench
==================================
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#AI #GenerativeAI #ScientificIllustrations #ResearchTools #AcademicPublishing
✨D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
📝 Summary:
D-CORE is a two-stage training framework improving large reasoning models' task decomposition and reasoning. It overcomes Lazy Reasoning using self-distillation and diversity-aware reinforcement learning. D-CORE achieves superior tool-use performance, setting new state-of-the-art results even wit...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02160
• PDF: https://arxiv.org/pdf/2602.02160
• Github: https://github.com/alibaba/EfficientAI
🔹 Models citing this paper:
• https://huggingface.co/bowiehsu/D-CORE-8B
==================================
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#LLM #TaskDecomposition #ToolUse #ReinforcementLearning #AIResearch
📝 Summary:
D-CORE is a two-stage training framework improving large reasoning models' task decomposition and reasoning. It overcomes Lazy Reasoning using self-distillation and diversity-aware reinforcement learning. D-CORE achieves superior tool-use performance, setting new state-of-the-art results even wit...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02160
• PDF: https://arxiv.org/pdf/2602.02160
• Github: https://github.com/alibaba/EfficientAI
🔹 Models citing this paper:
• https://huggingface.co/bowiehsu/D-CORE-8B
==================================
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#LLM #TaskDecomposition #ToolUse #ReinforcementLearning #AIResearch
✨No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data
📝 Summary:
This paper explores machine translation for five Turkic languages using nllb-200 LoRA fine-tuning on synthetic data and prompt-based methods. It achieved varied chrF++ scores for different language pairs and releases the dataset and model weights.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04442
• PDF: https://arxiv.org/pdf/2602.04442
==================================
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📝 Summary:
This paper explores machine translation for five Turkic languages using nllb-200 LoRA fine-tuning on synthetic data and prompt-based methods. It achieved varied chrF++ scores for different language pairs and releases the dataset and model weights.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04442
• PDF: https://arxiv.org/pdf/2602.04442
==================================
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✨SAFE: Stable Alignment Finetuning with Entropy-Aware Predictive Control for RLHF
📝 Summary:
A new reinforcement learning algorithm for language model alignment that improves stability and performance over PPO through enhanced KL divergence control and adaptive reward management. AI-generated...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04651
• PDF: https://arxiv.org/pdf/2602.04651
==================================
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📝 Summary:
A new reinforcement learning algorithm for language model alignment that improves stability and performance over PPO through enhanced KL divergence control and adaptive reward management. AI-generated...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04651
• PDF: https://arxiv.org/pdf/2602.04651
==================================
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✨SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonization
📝 Summary:
SkeletonGaussian enables editable 4D generation by decomposing motion into rigid skeleton-driven and non-rigid fine-grained components using hexplane-based refinement. AI-generated summary 4D generati...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04271
• PDF: https://arxiv.org/pdf/2602.04271
• Project Page: https://wusar.github.io/projects/skeletongaussian/
==================================
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📝 Summary:
SkeletonGaussian enables editable 4D generation by decomposing motion into rigid skeleton-driven and non-rigid fine-grained components using hexplane-based refinement. AI-generated summary 4D generati...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04271
• PDF: https://arxiv.org/pdf/2602.04271
• Project Page: https://wusar.github.io/projects/skeletongaussian/
==================================
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✨Reward-free Alignment for Conflicting Objectives
📝 Summary:
This paper introduces RACO, a reward-free alignment framework for LLMs facing multiple conflicting objectives. It uses a novel clipped conflict-averse gradient descent to resolve gradient conflicts directly from pairwise preferences. Experiments show RACO consistently achieves superior Pareto tra...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02495
• PDF: https://arxiv.org/pdf/2602.02495
==================================
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📝 Summary:
This paper introduces RACO, a reward-free alignment framework for LLMs facing multiple conflicting objectives. It uses a novel clipped conflict-averse gradient descent to resolve gradient conflicts directly from pairwise preferences. Experiments show RACO consistently achieves superior Pareto tra...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02495
• PDF: https://arxiv.org/pdf/2602.02495
==================================
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❤2
✨FOTBCD: A Large-Scale Building Change Detection Benchmark from French Orthophotos and Topographic Data
📝 Summary:
A large-scale building change detection dataset named FOTBCD is introduced, covering 28 French departments with high-resolution imagery and comprehensive annotations for both binary and instance-level...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22596
• PDF: https://arxiv.org/pdf/2601.22596
• Github: https://github.com/abdelpy/FOTBCD-datasets
✨ Datasets citing this paper:
• https://huggingface.co/datasets/retgenai/FOTBCD-Binary
==================================
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📝 Summary:
A large-scale building change detection dataset named FOTBCD is introduced, covering 28 French departments with high-resolution imagery and comprehensive annotations for both binary and instance-level...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22596
• PDF: https://arxiv.org/pdf/2601.22596
• Github: https://github.com/abdelpy/FOTBCD-datasets
✨ Datasets citing this paper:
• https://huggingface.co/datasets/retgenai/FOTBCD-Binary
==================================
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❤2
✨"I May Not Have Articulated Myself Clearly": Diagnosing Dynamic Instability in LLM Reasoning at Inference Time
📝 Summary:
An instability signal from LLM token log probabilities and entropy predicts reasoning failures. This signal, combining distributional shift and uncertainty, reliably forecasts wrong answers. Early instability can be corrective, but late instability more often leads to failure, indicating timing i...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02863
• PDF: https://arxiv.org/pdf/2602.02863
==================================
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📝 Summary:
An instability signal from LLM token log probabilities and entropy predicts reasoning failures. This signal, combining distributional shift and uncertainty, reliably forecasts wrong answers. Early instability can be corrective, but late instability more often leads to failure, indicating timing i...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02863
• PDF: https://arxiv.org/pdf/2602.02863
==================================
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❤1
✨AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent
📝 Summary:
AgentArk distills multi-agent reasoning into a single LLM to overcome the high computational cost of multi-agent systems. This framework enables a single agent to achieve multi-agent intelligence, offering efficient yet powerful reasoning, self-correction, and robustness across diverse tasks.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03955
• PDF: https://arxiv.org/pdf/2602.03955
• Github: https://github.com/AIFrontierLab/AgentArk
==================================
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📝 Summary:
AgentArk distills multi-agent reasoning into a single LLM to overcome the high computational cost of multi-agent systems. This framework enables a single agent to achieve multi-agent intelligence, offering efficient yet powerful reasoning, self-correction, and robustness across diverse tasks.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03955
• PDF: https://arxiv.org/pdf/2602.03955
• Github: https://github.com/AIFrontierLab/AgentArk
==================================
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✨HalluHard: A Hard Multi-Turn Hallucination Benchmark
📝 Summary:
Large language models continue to generate plausible but ungrounded factual claims in multi-turn dialogue, with hallucinations remaining significant even when utilizing web search for verification acr...
🔹 Publication Date: Published on Feb 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01031
• PDF: https://arxiv.org/pdf/2602.01031
==================================
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📝 Summary:
Large language models continue to generate plausible but ungrounded factual claims in multi-turn dialogue, with hallucinations remaining significant even when utilizing web search for verification acr...
🔹 Publication Date: Published on Feb 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01031
• PDF: https://arxiv.org/pdf/2602.01031
==================================
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✨Trust The Typical
📝 Summary:
Trust The Typical T3 frames LLM safety as an out-of-distribution detection problem, learning what is safe in semantic space. It achieves state-of-the-art performance without harmful example training, drastically reducing false positives and generalizing across languages with low overhead.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04581
• PDF: https://arxiv.org/pdf/2602.04581
==================================
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📝 Summary:
Trust The Typical T3 frames LLM safety as an out-of-distribution detection problem, learning what is safe in semantic space. It achieves state-of-the-art performance without harmful example training, drastically reducing false positives and generalizing across languages with low overhead.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04581
• PDF: https://arxiv.org/pdf/2602.04581
==================================
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✨Learning to Repair Lean Proofs from Compiler Feedback
📝 Summary:
A new dataset, APRIL, pairs erroneous Lean proofs with compiler feedback, corrected proofs, and natural language diagnoses. Training language models on APRIL substantially improves proof repair accuracy and feedback-conditioned reasoning, outperforming existing baselines.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02990
• PDF: https://arxiv.org/pdf/2602.02990
✨ Datasets citing this paper:
• https://huggingface.co/datasets/uw-math-ai/APRIL
==================================
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📝 Summary:
A new dataset, APRIL, pairs erroneous Lean proofs with compiler feedback, corrected proofs, and natural language diagnoses. Training language models on APRIL substantially improves proof repair accuracy and feedback-conditioned reasoning, outperforming existing baselines.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02990
• PDF: https://arxiv.org/pdf/2602.02990
✨ Datasets citing this paper:
• https://huggingface.co/datasets/uw-math-ai/APRIL
==================================
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✨MeKi: Memory-based Expert Knowledge Injection for Efficient LLM Scaling
📝 Summary:
MeKi enables efficient large language model deployment on edge devices by injecting pre-stored semantic knowledge through token-level memory experts and re-parameterization techniques. AI-generated su...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03359
• PDF: https://arxiv.org/pdf/2602.03359
==================================
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📝 Summary:
MeKi enables efficient large language model deployment on edge devices by injecting pre-stored semantic knowledge through token-level memory experts and re-parameterization techniques. AI-generated su...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03359
• PDF: https://arxiv.org/pdf/2602.03359
==================================
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✨Semantic Search over 9 Million Mathematical Theorems
📝 Summary:
Large-scale semantic theorem retrieval system demonstrates superior performance over existing baselines using a 9.2 million theorem corpus with systematic analysis of representation context, language ...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05216
• PDF: https://arxiv.org/pdf/2602.05216
✨ Datasets citing this paper:
• https://huggingface.co/datasets/uw-math-ai/theorem-search-dataset
==================================
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📝 Summary:
Large-scale semantic theorem retrieval system demonstrates superior performance over existing baselines using a 9.2 million theorem corpus with systematic analysis of representation context, language ...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05216
• PDF: https://arxiv.org/pdf/2602.05216
✨ Datasets citing this paper:
• https://huggingface.co/datasets/uw-math-ai/theorem-search-dataset
==================================
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✨RISE-Video: Can Video Generators Decode Implicit World Rules?
📝 Summary:
RISE-Video presents a novel benchmark for evaluating text-image-to-video synthesis models based on cognitive reasoning rather than visual fidelity, using a multi-dimensional metric system and automate...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05986
• PDF: https://arxiv.org/pdf/2602.05986
• Github: https://github.com/VisionXLab/Rise-Video
==================================
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📝 Summary:
RISE-Video presents a novel benchmark for evaluating text-image-to-video synthesis models based on cognitive reasoning rather than visual fidelity, using a multi-dimensional metric system and automate...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05986
• PDF: https://arxiv.org/pdf/2602.05986
• Github: https://github.com/VisionXLab/Rise-Video
==================================
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✨Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR
📝 Summary:
Research analyzes RLVR algorithms' impact on response length in LLMs and VLMs, proposing LUSPO to eliminate length bias and improve reasoning performance. AI-generated summary Recent applications of R...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05261
• PDF: https://arxiv.org/pdf/2602.05261
• Github: https://github.com/murphy4122/LUSPO
==================================
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📝 Summary:
Research analyzes RLVR algorithms' impact on response length in LLMs and VLMs, proposing LUSPO to eliminate length bias and improve reasoning performance. AI-generated summary Recent applications of R...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05261
• PDF: https://arxiv.org/pdf/2602.05261
• Github: https://github.com/murphy4122/LUSPO
==================================
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✨SwimBird: Eliciting Switchable Reasoning Mode in Hybrid Autoregressive MLLMs
📝 Summary:
SwimBird is a reasoning-switchable multimodal large language model that dynamically selects between text-only, vision-only, and interleaved vision-text reasoning modes based on input queries, achievin...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06040
• PDF: https://arxiv.org/pdf/2602.06040
• Project Page: https://accio-lab.github.io/SwimBird
• Github: https://github.com/Accio-Lab/SwimBird
==================================
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📝 Summary:
SwimBird is a reasoning-switchable multimodal large language model that dynamically selects between text-only, vision-only, and interleaved vision-text reasoning modes based on input queries, achievin...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06040
• PDF: https://arxiv.org/pdf/2602.06040
• Project Page: https://accio-lab.github.io/SwimBird
• Github: https://github.com/Accio-Lab/SwimBird
==================================
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✨Grounding and Enhancing Informativeness and Utility in Dataset Distillation
📝 Summary:
Dataset distillation method that balances informativeness and utility through game-theoretic and gradient-based optimization techniques, achieving improved performance on ImageNet-1K. AI-generated sum...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21296
• PDF: https://arxiv.org/pdf/2601.21296
==================================
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📝 Summary:
Dataset distillation method that balances informativeness and utility through game-theoretic and gradient-based optimization techniques, achieving improved performance on ImageNet-1K. AI-generated sum...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21296
• PDF: https://arxiv.org/pdf/2601.21296
==================================
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✨Reinforcement World Model Learning for LLM-based Agents
📝 Summary:
Reinforcement World Model Learning enables LLM-based agents to better anticipate action consequences and adapt to environment dynamics through self-supervised training that aligns simulated and real-w...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05842
• PDF: https://arxiv.org/pdf/2602.05842
==================================
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📝 Summary:
Reinforcement World Model Learning enables LLM-based agents to better anticipate action consequences and adapt to environment dynamics through self-supervised training that aligns simulated and real-w...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05842
• PDF: https://arxiv.org/pdf/2602.05842
==================================
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✨Breaking the Static Graph: Context-Aware Traversal for Robust Retrieval-Augmented Generation
📝 Summary:
CatRAG addresses limitations in retrieval-augmented generation by introducing a query-adaptive framework that improves multi-hop reasoning through symbolic anchoring, dynamic edge weighting, and key-f...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01965
• PDF: https://arxiv.org/pdf/2602.01965
==================================
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📝 Summary:
CatRAG addresses limitations in retrieval-augmented generation by introducing a query-adaptive framework that improves multi-hop reasoning through symbolic anchoring, dynamic edge weighting, and key-f...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01965
• PDF: https://arxiv.org/pdf/2602.01965
==================================
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