✨From Perception to Action: An Interactive Benchmark for Vision Reasoning
📝 Summary:
Current vision-language models struggle with physical structures and causal constraints for complex 3D tasks. The new CHAIN benchmark evaluates this capability, revealing that state-of-the-art models still fail to plan effective actions based on perceived physical structure.
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21015
• PDF: https://arxiv.org/pdf/2602.21015
• Project Page: https://social-ai-studio.github.io/CHAIN/
• Github: https://social-ai-studio.github.io/CHAIN/
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📝 Summary:
Current vision-language models struggle with physical structures and causal constraints for complex 3D tasks. The new CHAIN benchmark evaluates this capability, revealing that state-of-the-art models still fail to plan effective actions based on perceived physical structure.
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21015
• PDF: https://arxiv.org/pdf/2602.21015
• Project Page: https://social-ai-studio.github.io/CHAIN/
• Github: https://social-ai-studio.github.io/CHAIN/
==================================
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✨PyVision-RL: Forging Open Agentic Vision Models via RL
📝 Summary:
PyVision-RL framework addresses interaction collapse in multimodal models through enhanced reinforcement learning techniques and efficient video processing strategies. AI-generated summary Reinforceme...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20739
• PDF: https://arxiv.org/pdf/2602.20739
• Project Page: https://agent-x.space/pyvision-rl/
• Github: https://github.com/agents-x-project/PyVision-RL
🔹 Models citing this paper:
• https://huggingface.co/Agents-X/PyVision-Image-7B-SFT
• https://huggingface.co/Agents-X/PyVision-Image-7B-RL
• https://huggingface.co/Agents-X/PyVision-Video-7B-RL
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Agents-X/PyVision-Image-SFT-Data
• https://huggingface.co/datasets/Agents-X/PyVision-Video-RL-Data
• https://huggingface.co/datasets/Agents-X/PyVision-Image-RL-Data
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📝 Summary:
PyVision-RL framework addresses interaction collapse in multimodal models through enhanced reinforcement learning techniques and efficient video processing strategies. AI-generated summary Reinforceme...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20739
• PDF: https://arxiv.org/pdf/2602.20739
• Project Page: https://agent-x.space/pyvision-rl/
• Github: https://github.com/agents-x-project/PyVision-RL
🔹 Models citing this paper:
• https://huggingface.co/Agents-X/PyVision-Image-7B-SFT
• https://huggingface.co/Agents-X/PyVision-Image-7B-RL
• https://huggingface.co/Agents-X/PyVision-Video-7B-RL
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Agents-X/PyVision-Image-SFT-Data
• https://huggingface.co/datasets/Agents-X/PyVision-Video-RL-Data
• https://huggingface.co/datasets/Agents-X/PyVision-Image-RL-Data
==================================
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arXiv.org
PyVision-RL: Forging Open Agentic Vision Models via RL
Reinforcement learning for agentic multimodal models often suffers from interaction collapse, where models learn to reduce tool usage and multi-turn reasoning, limiting the benefits of agentic...
✨LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces
📝 Summary:
LongCLI-Bench evaluates AI agents' ability to complete complex, multi-step programming tasks through command-line interfaces with detailed failure analysis and human-agent collaboration insights. AI-g...
🔹 Publication Date: Published on Feb 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14337
• PDF: https://arxiv.org/pdf/2602.14337
• Project Page: https://github.com/finyorko/longcli-bench
• Github: https://github.com/finyorko/longcli-bench
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📝 Summary:
LongCLI-Bench evaluates AI agents' ability to complete complex, multi-step programming tasks through command-line interfaces with detailed failure analysis and human-agent collaboration insights. AI-g...
🔹 Publication Date: Published on Feb 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14337
• PDF: https://arxiv.org/pdf/2602.14337
• Project Page: https://github.com/finyorko/longcli-bench
• Github: https://github.com/finyorko/longcli-bench
==================================
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✨Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation
📝 Summary:
A new conversational financial recommendation benchmark evaluates large language models' ability to balance rational decision-making with user behavior alignment using multi-view references derived fr...
🔹 Publication Date: Published on Feb 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16990
• PDF: https://arxiv.org/pdf/2602.16990
• Github: https://github.com/The-FinAI/Conv-FinRe
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📝 Summary:
A new conversational financial recommendation benchmark evaluates large language models' ability to balance rational decision-making with user behavior alignment using multi-view references derived fr...
🔹 Publication Date: Published on Feb 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16990
• PDF: https://arxiv.org/pdf/2602.16990
• Github: https://github.com/The-FinAI/Conv-FinRe
==================================
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✨FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving
📝 Summary:
FlowPrefill addresses head-of-line blocking in large language model serving by decoupling preemption granularity from scheduling frequency through operator-level preemption and event-driven scheduling...
🔹 Publication Date: Published on Feb 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16603
• PDF: https://arxiv.org/pdf/2602.16603
• Github: https://github.com/HSIEHCHIACHI/FlowPrefill
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📝 Summary:
FlowPrefill addresses head-of-line blocking in large language model serving by decoupling preemption granularity from scheduling frequency through operator-level preemption and event-driven scheduling...
🔹 Publication Date: Published on Feb 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16603
• PDF: https://arxiv.org/pdf/2602.16603
• Github: https://github.com/HSIEHCHIACHI/FlowPrefill
==================================
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✨The Art of Efficient Reasoning: Data, Reward, and Optimization
📝 Summary:
Large language models benefit from scaled chain-of-thought reasoning through efficient training methods that balance trajectory length and accuracy using reinforcement learning with reward shaping. AI...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20945
• PDF: https://arxiv.org/pdf/2602.20945
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📝 Summary:
Large language models benefit from scaled chain-of-thought reasoning through efficient training methods that balance trajectory length and accuracy using reinforcement learning with reward shaping. AI...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20945
• PDF: https://arxiv.org/pdf/2602.20945
==================================
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✨Implicit Intelligence -- Evaluating Agents on What Users Don't Say
📝 Summary:
AI agents struggle to interpret implicitly specified real-world requests that require contextual reasoning beyond explicit instructions, as demonstrated by an evaluation framework using interactive YA...
🔹 Publication Date: Published on Feb 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20424
• PDF: https://arxiv.org/pdf/2602.20424
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📝 Summary:
AI agents struggle to interpret implicitly specified real-world requests that require contextual reasoning beyond explicit instructions, as demonstrated by an evaluation framework using interactive YA...
🔹 Publication Date: Published on Feb 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20424
• PDF: https://arxiv.org/pdf/2602.20424
==================================
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✨On Data Engineering for Scaling LLM Terminal Capabilities
📝 Summary:
Researchers developed a synthetic task generation pipeline and analyzed data strategies to improve terminal agent performance, creating a large-scale dataset and models that outperform larger counterp...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21193
• PDF: https://arxiv.org/pdf/2602.21193
• Project Page: https://huggingface.co/collections/nvidia/nemotron-terminal
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📝 Summary:
Researchers developed a synthetic task generation pipeline and analyzed data strategies to improve terminal agent performance, creating a large-scale dataset and models that outperform larger counterp...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21193
• PDF: https://arxiv.org/pdf/2602.21193
• Project Page: https://huggingface.co/collections/nvidia/nemotron-terminal
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✨Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs
📝 Summary:
Reflective Test-Time Planning enhances robot decision-making by integrating multiple reflection mechanisms that enable learning from experience and improving long-horizon task performance. AI-generate...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21198
• PDF: https://arxiv.org/pdf/2602.21198
• Project Page: https://reflective-test-time-planning.github.io/
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📝 Summary:
Reflective Test-Time Planning enhances robot decision-making by integrating multiple reflection mechanisms that enable learning from experience and improving long-horizon task performance. AI-generate...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21198
• PDF: https://arxiv.org/pdf/2602.21198
• Project Page: https://reflective-test-time-planning.github.io/
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✨Aletheia tackles FirstProof autonomously
📝 Summary:
We report the performance of Aletheia (Feng et al., 2026b), a mathematics research agent powered by Gemini 3 Deep Think, on the inaugural FirstProof challenge. Within the allowed timeframe of the chal...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21201
• PDF: https://arxiv.org/pdf/2602.21201
• Project Page: https://github.com/google-deepmind/superhuman/tree/main/aletheia
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📝 Summary:
We report the performance of Aletheia (Feng et al., 2026b), a mathematics research agent powered by Gemini 3 Deep Think, on the inaugural FirstProof challenge. Within the allowed timeframe of the chal...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21201
• PDF: https://arxiv.org/pdf/2602.21201
• Project Page: https://github.com/google-deepmind/superhuman/tree/main/aletheia
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