✨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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✨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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✨"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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✨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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✨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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✨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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✨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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✨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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✨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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✨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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✨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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✨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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✨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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✨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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✨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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✨Context Forcing: Consistent Autoregressive Video Generation with Long Context

📝 Summary:
Context Forcing addresses student-teacher mismatch in long video generation by using a long-context teacher to guide long-rollout students through a Slow-Fast Memory architecture that extends context ...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06028
• PDF: https://arxiv.org/pdf/2602.06028
• Project Page: https://chenshuo20.github.io/Context_Forcing/
• Github: https://github.com/TIGER-AI-Lab/Context-Forcing

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✨Late-to-Early Training: LET LLMs Learn Earlier, So Faster and Better

📝 Summary:
Large language models can be trained more efficiently by transferring knowledge from later training phases to earlier layers during initial training, achieving faster convergence and improved performa...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05393
• PDF: https://arxiv.org/pdf/2602.05393

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✨LatentMem: Customizing Latent Memory for Multi-Agent Systems

📝 Summary:
LatentMem is a learnable multi-agent memory framework that customizes agent-specific memories through latent representations, improving performance in multi-agent systems without modifying underlying ...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03036
• PDF: https://arxiv.org/pdf/2602.03036
• Github: https://github.com/KANABOON1/LatentMem

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✨ProAct: Agentic Lookahead in Interactive Environments

📝 Summary:
ProAct enhances LLM agents' long-horizon planning by combining supervised fine-tuning with search-derived trajectories and a Monte-Carlo critic for improved policy optimization. AI-generated summary E...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05327
• PDF: https://arxiv.org/pdf/2602.05327
• Github: https://github.com/GreatX3/ProAct

🔹 Models citing this paper:
• https://huggingface.co/biang889/ProAct

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✨FastVMT: Eliminating Redundancy in Video Motion Transfer

📝 Summary:
FastVMT accelerates video motion transfer by addressing computational redundancies in Diffusion Transformer architecture through localized attention masking and gradient reuse optimization. AI-generat...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05551
• PDF: https://arxiv.org/pdf/2602.05551
• Project Page: https://fastvmt.github.io/

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✨Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities

📝 Summary:
DeR2 presents a controlled evaluation framework for assessing language models' document-grounded reasoning capabilities by isolating reasoning from retrieval and toolchain decisions. AI-generated summ...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21937
• PDF: https://arxiv.org/pdf/2601.21937
• Project Page: https://huggingface.co/m-a-p
• Github: https://retrieval-infused-reasoning-sandbox.github.io/

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