✨Context Learning for Multi-Agent Discussion

📝 Summary:
Multi-Agent Discussion methods suffer from inconsistency due to individual context misalignment, which is addressed through a context learning approach that dynamically generates context instructions ...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02350
• PDF: https://arxiv.org/pdf/2602.02350

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✨A2Eval: Agentic and Automated Evaluation for Embodied Brain

📝 Summary:
Agentic automatic evaluation framework automates embodied vision-language model assessment through collaborative agents that reduce evaluation costs and improve ranking accuracy. AI-generated summary ...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01640
• PDF: https://arxiv.org/pdf/2602.01640

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✨UI-TARS: Pioneering Automated GUI Interaction with Native Agents

📝 Summary:
UI-TARS, a native GUI agent model using screenshots as input, outperforms commercial models in various benchmarks through enhanced perception, unified action modeling, system-2 reasoning, and iterativ...

🔹 Publication Date: Published on Jan 21, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.12326
• PDF: https://arxiv.org/pdf/2501.12326
• Github: https://github.com/bytedance/UI-TARS

🔹 Models citing this paper:
• https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B
• https://huggingface.co/ByteDance-Seed/UI-TARS-7B-DPO
• https://huggingface.co/ByteDance-Seed/UI-TARS-7B-SFT

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Hcompany/WebClick

✨ Spaces citing this paper:
• https://huggingface.co/spaces/omar0scarf/ui-tars-api
• https://huggingface.co/spaces/bytedance-research/UI-TARS
• https://huggingface.co/spaces/Aheader/gui_test_app

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✨Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization

📝 Summary:
Quant VideoGen addresses KV cache memory limitations in autoregressive video diffusion models through semantic-aware smoothing and progressive residual quantization, achieving significant memory reduc...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02958
• PDF: https://arxiv.org/pdf/2602.02958

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✨EgoActor: Grounding Task Planning into Spatial-aware Egocentric Actions for Humanoid Robots via Visual-Language Models

📝 Summary:
EgoActor is a unified vision-language model that translates high-level instructions into precise humanoid robot actions through integrated perception and execution across simulated and real-world envi...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04515
• PDF: https://arxiv.org/pdf/2602.04515
• Github: https://baai-agents.github.io/EgoActor/

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✨PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR

📝 Summary:
Search agents trained on scientific paper corpora demonstrate advanced reasoning capabilities for technical question-answering tasks, outperforming traditional retrieval methods through reinforcement ...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18207
• PDF: https://arxiv.org/pdf/2601.18207
• Project Page: https://jmhb0.github.io/PaperSearchQA/
• Github: https://jmhb0.github.io/PaperSearchQA/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/jmhb/PaperSearchQA
• https://huggingface.co/datasets/jmhb/pubmed_bioasq_2022
• https://huggingface.co/datasets/jmhb/bioasq_factoid

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✨Rethinking the Trust Region in LLM Reinforcement Learning

📝 Summary:
DPPO addresses limitations in PPO for LLM fine-tuning by replacing ratio clipping with direct policy divergence constraints, improving training stability and efficiency. AI-generated summary Reinforce...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04879
• PDF: https://arxiv.org/pdf/2602.04879

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✨Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration

📝 Summary:
Vibe AIGC introduces a new generative AI paradigm where users provide high-level aesthetic and functional preferences, which are then orchestrated through multi-agent workflows to bridge the gap betwe...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04575
• PDF: https://arxiv.org/pdf/2602.04575

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✨Residual Context Diffusion Language Models

📝 Summary:
Residual Context Diffusion (RCD) enhances diffusion large language models by recycling discarded token information through contextual residuals, improving accuracy with minimal computational overhead....

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22954
• PDF: https://arxiv.org/pdf/2601.22954
• Project Page: https://yuezhouhu.github.io/projects/residual-context-diffusion/index.html
• Github: https://github.com/yuezhouhu/residual-context-diffusion

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✨Training Data Efficiency in Multimodal Process Reward Models

📝 Summary:
Training multimodal process reward models efficiently through balanced-information scoring that prioritizes label mixture and reliability while achieving full-data performance with only 10% of trainin...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04145
• PDF: https://arxiv.org/pdf/2602.04145

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✨BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation

📝 Summary:
BatCoder is a self-supervised reinforcement learning framework that jointly optimizes code and documentation generation through back-translation, achieving superior performance on code-related benchma...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02554
• PDF: https://arxiv.org/pdf/2602.02554

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✨Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition

📝 Summary:
MLLMs suffer from modality bias in GMNER tasks, which is addressed through a proposed method that enforces cross-modal reasoning via multi-style reasoning schema injection and constraint-guided verifi...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04486
• PDF: https://arxiv.org/pdf/2602.04486

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✨RexBERT: Context Specialized Bidirectional Encoders for E-commerce

📝 Summary:
RexBERT, a family of BERT-style encoders designed for e-commerce semantics, achieves superior performance on domain-specific tasks through specialized pretraining and high-quality in-domain data. AI-g...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04605
• PDF: https://arxiv.org/pdf/2602.04605

🔹 Models citing this paper:
• https://huggingface.co/thebajajra/RexBERT-base
• https://huggingface.co/thebajajra/RexBERT-large
• https://huggingface.co/thebajajra/RexBERT-mini

✨ Datasets citing this paper:
• https://huggingface.co/datasets/thebajajra/Ecom-niverse

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✨Agent-Omit: Training Efficient LLM Agents for Adaptive Thought and Observation Omission via Agentic Reinforcement Learning

📝 Summary:
Agent-Omit is a training framework that enables LLM agents to adaptively omit redundant thoughts and observations during multi-turn interactions, achieving superior effectiveness-efficiency trade-offs...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04284
• PDF: https://arxiv.org/pdf/2602.04284
• Project Page: https://github.com/usail-hkust/Agent-Omit
• Github: https://github.com/usail-hkust/Agent-Omit

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✨Horizon-LM: A RAM-Centric Architecture for LLM Training

📝 Summary:
Horizon-LM enables large-model training on single GPUs by redefining CPU-GPU roles and eliminating persistent GPU memory usage through explicit recomputation and pipelined execution. AI-generated summ...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04816
• PDF: https://arxiv.org/pdf/2602.04816
• Github: https://github.com/DLYuanGod/Horizon-LM

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✨WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning

📝 Summary:
Multi-agent systems using reinforcement learning enable parallel information seeking with scalable orchestration, achieving performance comparable to larger single agents. AI-generated summary Recent ...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04634
• PDF: https://arxiv.org/pdf/2602.04634
• Project Page: https://wideseek-r1.github.io/

🔹 Models citing this paper:
• https://huggingface.co/RLinf/WideSeek-R1-4b

✨ Datasets citing this paper:
• https://huggingface.co/datasets/RLinf/WideSeek-R1-train-data
• https://huggingface.co/datasets/RLinf/WideSeek-R1-Corpus

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✨HySparse: A Hybrid Sparse Attention Architecture with Oracle Token Selection and KV Cache Sharing

📝 Summary:
Hybrid Sparse Attention architecture interleaves full and sparse attention layers, using full attention output to guide sparse layer token selection and cache reuse for improved efficiency and perform...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03560
• PDF: https://arxiv.org/pdf/2602.03560

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✨Skin Tokens: A Learned Compact Representation for Unified Autoregressive Rigging

📝 Summary:
Generative 3D models face challenges in animation rigging, which this work addresses by introducing SkinTokens—a learned discrete representation for skinning weights—and TokenRig, a unified autoregres...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04805
• PDF: https://arxiv.org/pdf/2602.04805
• Project Page: https://zjp-shadow.github.io/works/SkinTokens/

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✨HY3D-Bench: Generation of 3D Assets

📝 Summary:
HY3D-Bench presents an open-source ecosystem for 3D content creation that provides high-fidelity 3D objects and synthetic assets to advance 3D generation capabilities. AI-generated summary While recen...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03907
• PDF: https://arxiv.org/pdf/2602.03907
• Project Page: https://3d.hunyuan.tencent.com/login?redirect_url=https%3A%2F%2F3d.hunyuan.tencent.com%2F

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✨TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents

📝 Summary:
Test-Time Improvement (TTI) in autonomous LLM agents involves iterative environmental interaction that enhances performance, but current evaluation methods inadequately capture task optimization effic...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02196
• PDF: https://arxiv.org/pdf/2602.02196

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