✨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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✨Pathwise Test-Time Correction for Autoregressive Long Video Generation

📝 Summary:
Test-Time Correction addresses error accumulation in distilled autoregressive diffusion models for long-video synthesis by using initial frames as reference anchors to calibrate stochastic states duri...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05871
• PDF: https://arxiv.org/pdf/2602.05871

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✨BABE: Biology Arena BEnchmark

📝 Summary:
BABE is a biology-focused benchmark designed to evaluate AI systems' ability to perform experimental reasoning and causal inference similar to practicing scientists. AI-generated summary The rapid evo...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05857
• PDF: https://arxiv.org/pdf/2602.05857

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✨UniAudio 2.0: A Unified Audio Language Model with Text-Aligned Factorized Audio Tokenization

📝 Summary:
Researchers developed a discrete audio codec called ReasoningCodec that separates audio into reasoning and reconstruction tokens for improved understanding and generation, and created UniAudio 2.0, a ...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04683
• PDF: https://arxiv.org/pdf/2602.04683
• Project Page: https://dongchaoyang.top/UniAudio2Demo/
• Github: https://github.com/yangdongchao/UniAudio2

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✨Steering LLMs via Scalable Interactive Oversight

📝 Summary:
Scalable Interactive Oversight framework decomposes complex tasks into manageable decision trees to enhance human supervision and alignment in AI systems. AI-generated summary As Large Language Models...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04210
• PDF: https://arxiv.org/pdf/2602.04210

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✨SAGE: Benchmarking and Improving Retrieval for Deep Research Agents

📝 Summary:
LLM-based retrievers show limited effectiveness in deep research agent workflows, with traditional BM25 performing better, though corpus-level test-time scaling can improve retrieval performance. AI-g...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05975
• PDF: https://arxiv.org/pdf/2602.05975

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✨InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions

📝 Summary:
A scalable framework called InterPrior learns a unified generative controller through imitation learning and reinforcement learning to enable humanoids to generalize loco-manipulation skills across di...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06035
• PDF: https://arxiv.org/pdf/2602.06035
• Project Page: https://sirui-xu.github.io/InterPrior/

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✨Beyond Fixed Frames: Dynamic Character-Aligned Speech Tokenization

📝 Summary:
DyCAST is a dynamic speech tokenizer that uses soft character-level alignment and duration modeling to enable variable-frame-rate tokenization, improving speech resynthesis quality with fewer tokens t...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23174
• PDF: https://arxiv.org/pdf/2601.23174

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✨Assessing Domain-Level Susceptibility to Emergent Misalignment from Narrow Finetuning

📝 Summary:
Large language models fine-tuned on insecure datasets exhibit increased misalignment rates across diverse domains, with varying vulnerability levels and potential for generalization of misalignment be...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00298
• PDF: https://arxiv.org/pdf/2602.00298

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✨Reinforced Attention Learning

📝 Summary:
Reinforced Attention Learning optimizes internal attention distributions in multimodal language models, improving information allocation and cross-modal alignment through policy-gradient methods. AI-g...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04884
• PDF: https://arxiv.org/pdf/2602.04884

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✨Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

📝 Summary:
Reinforcement learning approach for kernel generation addresses reward hacking and optimization issues through specialized environment and unbiased policy gradient methods, achieving competitive perfo...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05885
• PDF: https://arxiv.org/pdf/2602.05885

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✨Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?

📝 Summary:
Vision-language models can precisely geolocate images but often fail to align with human privacy expectations, over-disclosing location details in sensitive contexts and being vulnerable to prompt-bas...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05023
• PDF: https://arxiv.org/pdf/2602.05023
• Project Page: https://huggingface.co/datasets/RayY/VLM-GeoPrivacyBench
• Github: https://github.com/99starman/VLM-GeoPrivacyBench

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✨V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval

📝 Summary:
V-Retrver introduces an evidence-driven retrieval framework that enables multimodal large language models to actively verify visual evidence through an agentic reasoning process, improving retrieval a...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06034
• PDF: https://arxiv.org/pdf/2602.06034
• Github: https://github.com/chendy25/V-Retrver

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✨Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening

📝 Summary:
Spider-Sense is an event-driven framework for agent security using Intrinsic Risk Sensing. It provides intrinsic, selective defense through a hierarchical mechanism, activating only upon risk perception. It achieves low attack success and false positive rates with minimal latency.

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05386
• PDF: https://arxiv.org/pdf/2602.05386
• Github: https://github.com/aifinlab/Spider-Sense

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