✨PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution

📝 Summary:
PACEvolve framework addresses key failure modes in LLM evolutionary search through hierarchical context management, momentum-based backtracking, and adaptive sampling policies for improved self-improv...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10657
• PDF: https://arxiv.org/pdf/2601.10657

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✨RigMo: Unifying Rig and Motion Learning for Generative Animation

📝 Summary:
RigMo unifies rig and motion learning directly from raw mesh sequences, encoding deformations into compact latent spaces. This framework generates interpretable, plausible 3D animation, offering superior reconstruction and generalization over baselines.

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06378
• PDF: https://arxiv.org/pdf/2601.06378
• Project Page: https://rigmo-page.github.io/
• Github: https://rigmo-page.github.io

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✨Demystifying the Slash Pattern in Attention: The Role of RoPE

📝 Summary:
Slash-Dominant Heads in LLMs emerge when queries and keys are almost rank-one and Rotary Position Embedding has dominant medium-high frequencies. Theoretical proof shows these conditions, combined with gradient descent, explain their emergence.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08297
• PDF: https://arxiv.org/pdf/2601.08297

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✨M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints

📝 Summary:
M4olGen is a multi-agent, multi-stage framework for precise molecular generation under multiple physicochemical constraints. It uses fragment-level, retrieval-augmented reasoning and RL-based optimization, outperforming LLMs and graph-based methods.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10131
• PDF: https://arxiv.org/pdf/2601.10131

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✨Your Group-Relative Advantage Is Biased

📝 Summary:
Group-based Reinforcement Learning from Verifier Rewards has a biased advantage estimator, underestimating hard prompts and overestimating easy ones. This paper proposes History-Aware Adaptive Difficulty Weighting HA-DW to correct this bias, improving performance on reasoning tasks.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08521
• PDF: https://arxiv.org/pdf/2601.08521

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#ReinforcementLearning #MachineLearning #AIResearch #BiasCorrection #ReasoningTasks

✨RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation

📝 Summary:
This work presents an automated rubric generation framework and RubricHub dataset for open-ended AI generation. RubricHub enables significant performance gains, achieving state-of-the-art results on HealthBench and surpassing GPT-5.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08430
• PDF: https://arxiv.org/pdf/2601.08430
• Project Page: https://huggingface.co/datasets/sojuL/RubricHub_v1
• Github: https://github.com/teqkilla/RubricHub

✨ Datasets citing this paper:
• https://huggingface.co/datasets/sojuL/RubricHub_v1

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✨BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search

📝 Summary:
Reinforcement learning framework for agentic search that improves reliability by teaching agents to recognize reasoning limits and respond appropriately when evidence is insufficient. AI-generated sum...

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11037
• PDF: https://arxiv.org/pdf/2601.11037
• Github: https://github.com/Liushiyu-0709/BAPO-Reliable-Search

==================================

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✨ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection

📝 Summary:
Supervised fine-tuning with multiple references addresses overfitting to non-core expressions by masking low-probability tokens based on their semantic importance. AI-generated summary Supervised fine...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09195
• PDF: https://arxiv.org/pdf/2601.09195
• Github: https://github.com/Utaotao/ProFit

==================================

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✨Reasoning Models Generate Societies of Thought

📝 Summary:
Reasoning models demonstrate enhanced performance through multi-agent-like interactions that create diverse cognitive perspectives and improve problem-solving through structured social organization. A...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10825
• PDF: https://arxiv.org/pdf/2601.10825

==================================

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✨AstroReason-Bench: Evaluating Unified Agentic Planning across Heterogeneous Space Planning Problems

📝 Summary:
Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely foc...

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11354
• PDF: https://arxiv.org/pdf/2601.11354
• Github: https://github.com/Mtrya/astro-reason

==================================

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