✨CUA-Skill: Develop Skills for Computer Using Agent

📝 Summary:
CUA-Skill introduces a large-scale library of engineered computer-use skills that enhance agent performance and efficiency on Windows-based tasks. AI-generated summary Computer-Using Agents (CUAs) aim...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21123
• PDF: https://arxiv.org/pdf/2601.21123
• Project Page: https://microsoft.github.io/cua_skill/
• Github: https://github.com/microsoft/cua_skill

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✨VoxServe: Streaming-Centric Serving System for Speech Language Models

📝 Summary:
VoxServe is a unified serving system for SpeechLMs that optimizes streaming performance. It uses model-execution abstraction, streaming-aware scheduling, and asynchronous inference pipelines. This achieves 10-20x higher throughput at comparable latency for diverse SpeechLM architectures.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00269
• PDF: https://arxiv.org/pdf/2602.00269
• Project Page: https://vox-serve.github.io/
• Github: https://github.com/vox-serve/vox-serve

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✨Competing Visions of Ethical AI: A Case Study of OpenAI

📝 Summary:
This study analyzed OpenAI's public discourse on ethical AI. It found OpenAI primarily frames the discussion around safety and risk, largely avoiding academic ethics frameworks. This indicates a distinct approach to AI ethics in industry communications.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16513
• PDF: https://arxiv.org/pdf/2601.16513

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✨CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding

📝 Summary:
Multimodal LLMs can effectively understand source code represented as compressed images, achieving up to 8x token reduction. This method leverages visual cues and sometimes outperforms text inputs, indicating a path to more efficient code comprehension.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01785
• PDF: https://arxiv.org/pdf/2602.01785

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✨Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks

📝 Summary:
Current world models lack unified frameworks despite task-specific advances, necessitating a comprehensive approach integrating interaction, perception, symbolic reasoning, and spatial representation....

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01630
• PDF: https://arxiv.org/pdf/2602.01630
• Github: https://github.com/OpenDCAI/DataFlow-MM

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✨AdaptMMBench: Benchmarking Adaptive Multimodal Reasoning for Mode Selection and Reasoning Process

📝 Summary:
AdaptMMBench presents a comprehensive benchmark for evaluating adaptive multimodal reasoning in Vision-Language Models, measuring reasoning mode selection rationality through dynamic difficulty assess...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02676
• PDF: https://arxiv.org/pdf/2602.02676

✨ Datasets citing this paper:
• https://huggingface.co/datasets/xintongzhang/AdaptMMBench

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✨Unified Personalized Reward Model for Vision Generation

📝 Summary:
UnifiedReward-Flex combines reward modeling with flexible, context-adaptive reasoning to improve visual generation by dynamically constructing hierarchical assessments based on semantic intent and vis...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02380
• PDF: https://arxiv.org/pdf/2602.02380
• Project Page: https://codegoat24.github.io/UnifiedReward/flex
• Github: https://codegoat24.github.io/UnifiedReward/flex

🔹 Models citing this paper:
• https://huggingface.co/CodeGoat24/Wan2.1-T2V-14B-UnifiedReward-Flex-lora
• https://huggingface.co/CodeGoat24/UnifiedReward-Flex-qwen3vl-2b
• https://huggingface.co/CodeGoat24/UnifiedReward-Flex-qwen3vl-4b

✨ Datasets citing this paper:
• https://huggingface.co/datasets/CodeGoat24/UnifiedReward-Flex-SFT-90K

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

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✨Glance and Focus Reinforcement for Pan-cancer Screening

📝 Summary:
A reinforcement learning framework with glance and focus models improves pan-cancer screening in CT scans by addressing foreground-background imbalance and reducing false positives through group relat...

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19103
• PDF: https://arxiv.org/pdf/2601.19103
• Github: https://github.com/Luffy03/GF-Screen

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✨FaceLinkGen: Rethinking Identity Leakage in Privacy-Preserving Face Recognition with Identity Extraction

📝 Summary:
FaceLinkGen attack demonstrates that current privacy-preserving face recognition methods fail to protect identity information despite pixel-level distortion metrics suggesting adequate protection. AI-...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02914
• PDF: https://arxiv.org/pdf/2602.02914

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✨ObjEmbed: Towards Universal Multimodal Object Embeddings

📝 Summary:
ObjEmbed is a novel multimodal language-model embedding approach that decomposes images into regional embeddings for improved object-level visual understanding and retrieval tasks. AI-generated summar...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01753
• PDF: https://arxiv.org/pdf/2602.01753
• Github: https://github.com/WeChatCV/ObjEmbed

🔹 Models citing this paper:
• https://huggingface.co/fushh7/ObjEmbed-2B
• https://huggingface.co/fushh7/ObjEmbed-4B

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✨Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation

📝 Summary:
DeepResearch report generation is improved via human-preference-aligned, query-specific rubric generators trained with reinforcement learning and a multi-agent workflow. This system significantly outperforms open-source baselines and matches leading closed-source models.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03619
• PDF: https://arxiv.org/pdf/2602.03619

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

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✨Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing

📝 Summary:
Parallel-Probe is a training-free controller that optimizes parallel thinking by using consensus-based early stopping and deviation-based branch pruning to reduce computational costs while maintaining...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03845
• PDF: https://arxiv.org/pdf/2602.03845
• Project Page: https://huggingface.co/spaces/EfficientReasoning/efficient_reasoning_online_judgement
• Github: https://github.com/zhengkid/Parallel-Probe

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✨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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