✨WideSeek: Advancing Wide Research via Multi-Agent Scaling

📝 Summary:
Wide Research advances search intelligence through a dedicated benchmark and multi-agent architecture that enables parallel information retrieval under complex constraints. AI-generated summary Search...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02636
• PDF: https://arxiv.org/pdf/2602.02636
• Project Page: https://wideseek-ai.github.io/
• Github: https://github.com/hzy312/WideSeek

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✨Less Noise, More Voice: Reinforcement Learning for Reasoning via Instruction Purification

📝 Summary:
LENS framework improves reinforcement learning with verifiable rewards by identifying and removing interference tokens to enhance exploration efficiency and training stability. AI-generated summary Re...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21244
• PDF: https://arxiv.org/pdf/2601.21244

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✨Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training

📝 Summary:
DeMix is a framework that uses model merging to predict optimal data ratios for LLM pre-training, decoupling search from training costs to improve mixture discovery efficiency. AI-generated summary De...

🔹 Publication Date: Published on Jan 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00747
• PDF: https://arxiv.org/pdf/2602.00747

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✨Balancing Understanding and Generation in Discrete Diffusion Models

📝 Summary:
XDLM unifies Masked Diffusion Language Models and Uniform-noise Diffusion Language Models through a stationary noise kernel, achieving improved performance in both semantic understanding and generatio...

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01362
• PDF: https://arxiv.org/pdf/2602.01362

🔹 Models citing this paper:
• https://huggingface.co/Mzero17/XDLM
• https://huggingface.co/Mzero17/LLaDA-XDLM

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✨No Global Plan in Chain-of-Thought: Uncover the Latent Planning Horizon of LLMs

📝 Summary:
Research investigates latent planning dynamics in large language models through a probing method called Tele-Lens, revealing limited global planning and enabling improved uncertainty estimation and Co...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02103
• PDF: https://arxiv.org/pdf/2602.02103
• Github: https://github.com/lxucs/tele-lens

🔹 Models citing this paper:
• https://huggingface.co/lxucs/tele-lens-llm

✨ Datasets citing this paper:
• https://huggingface.co/datasets/lxucs/tele-lens

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✨Contextualized Visual Personalization in Vision-Language Models

📝 Summary:
CoViP addresses contextualized visual personalization by treating personalized image captioning as a core task and improving capabilities through reinforcement-learning-based post-training and caption...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03454
• PDF: https://arxiv.org/pdf/2602.03454

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✨WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models

📝 Summary:
WorldVQA is a benchmark for evaluating the visual world knowledge of multimodal large language models by separating visual knowledge retrieval from reasoning to measure memorized facts. AI-generated s...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02537
• PDF: https://arxiv.org/pdf/2602.02537

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✨Accelerating Scientific Research with Gemini: Case Studies and Common Techniques

📝 Summary:
Advanced AI models demonstrate capability in supporting expert-level mathematical discovery and scientific research through collaborative approaches involving proof verification and automated code exe...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03837
• PDF: https://arxiv.org/pdf/2602.03837

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✨3D-Aware Implicit Motion Control for View-Adaptive Human Video Generation

📝 Summary:
3DiMo enables view-agnostic human motion control in video generation by training a motion encoder alongside a pretrained video generator to distill driving frames into compact motion tokens that align...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03796
• PDF: https://arxiv.org/pdf/2602.03796
• Github: https://hjrphoebus.github.io/3DiMo/

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✨CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs

📝 Summary:
CoBA-RL adapts rollout budget allocation for LLM training by evaluating sample training value and optimizing resource distribution through a capability-oriented value function and greedy strategy. AI-...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03048
• PDF: https://arxiv.org/pdf/2602.03048

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✨Adaptive Evidence Weighting for Audio-Spatiotemporal Fusion

📝 Summary:
A fusion framework called FINCH combines audio and spatiotemporal predictors for bioacoustic classification by adaptively weighting evidence based on reliability estimates, outperforming fixed-weight ...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03817
• PDF: https://arxiv.org/pdf/2602.03817

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✨Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration

📝 Summary:
Search-R2 framework improves language agent reasoning through Actor-Refiner collaboration with targeted interventions and fine-grained reward supervision for better credit assignment in reinforcement ...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03647
• PDF: https://arxiv.org/pdf/2602.03647

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✨MARS: Modular Agent with Reflective Search for Automated AI Research

📝 Summary:
MARS is a modular AI research automation framework that uses budget-aware planning, modular construction, and reflective memory to achieve state-of-the-art performance in autonomous machine learning r...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02660
• PDF: https://arxiv.org/pdf/2602.02660

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✨daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently

📝 Summary:
daVinci-Agency addresses LLM limitations in long-horizon tasks by extracting structured training data from software pull request sequences. It uses progressive decomposition, consistency enforcement, and bug-fix refinement. This method offers data-efficient supervision, boosting LLM performance o...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02619
• PDF: https://arxiv.org/pdf/2602.02619
• Github: https://github.com/GAIR-NLP/daVinci-Agency

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✨Bridging Online and Offline RL: Contextual Bandit Learning for Multi-Turn Code Generation

📝 Summary:
Offline reinforcement learning method combines contextual bandit learning with partial trajectories to improve multi-turn code generation performance while reducing training costs. AI-generated summar...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03806
• PDF: https://arxiv.org/pdf/2602.03806
• Github: https://github.com/OSU-NLP-Group/cobalt

✨ Datasets citing this paper:
• https://huggingface.co/datasets/osunlp/TACO-Cobalt
• https://huggingface.co/datasets/osunlp/TACO-Cobalt-PTB

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✨SWE-World: Building Software Engineering Agents in Docker-Free Environments

📝 Summary:
A Docker-free framework replaces physical execution environments with learned surrogates for training software engineering agents, enabling efficient training and test-time scaling without costly cont...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03419
• PDF: https://arxiv.org/pdf/2602.03419

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✨SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training

📝 Summary:
SWE-Master presents a reproducible framework for developing software engineering agents through systematic optimization across multiple stages of agent development, achieving superior performance on s...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03411
• PDF: https://arxiv.org/pdf/2602.03411

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✨AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration

📝 Summary:
AOrchestra is a framework-agnostic agentic system that uses a tuple-based abstraction to dynamically create specialized task executors, achieving improved performance on complex benchmarks through aut...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03786
• PDF: https://arxiv.org/pdf/2602.03786

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✨Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis

📝 Summary:
A novel distillation framework called DP-DMD is introduced that preserves sample diversity in text-to-image generation by separating the roles of distilled steps, using v-prediction for diversity and ...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03139
• PDF: https://arxiv.org/pdf/2602.03139
• Github: https://github.com/Multimedia-Analytics-Laboratory/dpdmd

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✨SafeGround: Know When to Trust GUI Grounding Models via Uncertainty Calibration

📝 Summary:
SafeGround is a uncertainty-aware framework for GUI grounding models that uses distribution-aware uncertainty quantification and calibration to enable risk-aware predictions with controlled false disc...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02419
• PDF: https://arxiv.org/pdf/2602.02419
• Github: https://github.com/Cece1031/SAFEGROUND

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✨FullStack-Agent: Enhancing Agentic Full-Stack Web Coding via Development-Oriented Testing and Repository Back-Translation

📝 Summary:
FullStack-Agent is a unified AI system assisting non-experts in full-stack web development. It uses a multi-agent framework and a self-improving method, demonstrating significant performance gains over prior state-of-the-art across all web functionalities.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03798
• PDF: https://arxiv.org/pdf/2602.03798
• Github: https://github.com/mnluzimu/FullStack-Agent

🔹 Models citing this paper:
• https://huggingface.co/luzimu/FullStack-Learn-LM-30B-A3B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/luzimu/FullStack-Bench

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