✨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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✨The Diffusion Duality, Chapter II: Ψ-Samplers and Efficient Curriculum
📝 Summary:
Discrete diffusion models with predictor-corrector samplers surpass traditional methods in generation quality and efficiency, challenging assumptions about masked diffusion's necessity in language mod...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21185
• PDF: https://arxiv.org/pdf/2602.21185
• Project Page: https://s-sahoo.com/duo-ch2/
==================================
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📝 Summary:
Discrete diffusion models with predictor-corrector samplers surpass traditional methods in generation quality and efficiency, challenging assumptions about masked diffusion's necessity in language mod...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21185
• PDF: https://arxiv.org/pdf/2602.21185
• Project Page: https://s-sahoo.com/duo-ch2/
==================================
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✨Test-Time Training with KV Binding Is Secretly Linear Attention
📝 Summary:
This paper reinterprets Test-Time Training TTT with KV binding. Instead of memorization, it shows TTT is a form of learned linear attention with enhanced representational capacity. This new perspective explains puzzling behaviors, simplifies architectures, and boosts efficiency.
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21204
• PDF: https://arxiv.org/pdf/2602.21204
• Project Page: https://research.nvidia.com/labs/sil/projects/tttla/
==================================
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📝 Summary:
This paper reinterprets Test-Time Training TTT with KV binding. Instead of memorization, it shows TTT is a form of learned linear attention with enhanced representational capacity. This new perspective explains puzzling behaviors, simplifies architectures, and boosts efficiency.
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21204
• PDF: https://arxiv.org/pdf/2602.21204
• Project Page: https://research.nvidia.com/labs/sil/projects/tttla/
==================================
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✨Generative AI and Machine Learning Collaboration for Container Dwell Time Prediction via Data Standardization
📝 Summary:
A collaborative framework integrating generative artificial intelligence with machine learning improves container dwell time prediction by standardizing unstructured text data, leading to reduced reha...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20540
• PDF: https://arxiv.org/pdf/2602.20540
==================================
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📝 Summary:
A collaborative framework integrating generative artificial intelligence with machine learning improves container dwell time prediction by standardizing unstructured text data, leading to reduced reha...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20540
• PDF: https://arxiv.org/pdf/2602.20540
==================================
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✨DREAM: Deep Research Evaluation with Agentic Metrics
📝 Summary:
Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research quality. Recent ben...
🔹 Publication Date: Published on Feb 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18940
• PDF: https://arxiv.org/pdf/2602.18940
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📝 Summary:
Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research quality. Recent ben...
🔹 Publication Date: Published on Feb 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18940
• PDF: https://arxiv.org/pdf/2602.18940
==================================
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✨Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking
📝 Summary:
UPipe enables efficient processing of long sequences in Transformer models through fine-grained chunking at the attention head level, significantly reducing activation memory usage while maintaining t...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21196
• PDF: https://arxiv.org/pdf/2602.21196
• Project Page: https://rghadia.github.io/untied_ulysses_proj/
• Github: https://github.com/togethercomputer/Untied-Ulysses
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📝 Summary:
UPipe enables efficient processing of long sequences in Transformer models through fine-grained chunking at the attention head level, significantly reducing activation memory usage while maintaining t...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21196
• PDF: https://arxiv.org/pdf/2602.21196
• Project Page: https://rghadia.github.io/untied_ulysses_proj/
• Github: https://github.com/togethercomputer/Untied-Ulysses
==================================
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✨OCR-Agent: Agentic OCR with Capability and Memory Reflection
📝 Summary:
A novel iterative self-correction framework enhances vision-language models' reasoning robustness through capability reflection and memory reflection mechanisms, achieving superior performance on visu...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21053
• PDF: https://arxiv.org/pdf/2602.21053
• Github: https://github.com/AIGeeksGroup/OCR-Agent
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📝 Summary:
A novel iterative self-correction framework enhances vision-language models' reasoning robustness through capability reflection and memory reflection mechanisms, achieving superior performance on visu...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21053
• PDF: https://arxiv.org/pdf/2602.21053
• Github: https://github.com/AIGeeksGroup/OCR-Agent
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✨OmniOCR: Generalist OCR for Ethnic Minority Languages
📝 Summary:
OmniOCR presents a universal framework for ethnic minority scripts using Dynamic LoRA and sparsity regularization to achieve state-of-the-art accuracy with improved parameter efficiency in low-resourc...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21042
• PDF: https://arxiv.org/pdf/2602.21042
• Github: https://github.com/AIGeeksGroup/OmniOCR
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📝 Summary:
OmniOCR presents a universal framework for ethnic minority scripts using Dynamic LoRA and sparsity regularization to achieve state-of-the-art accuracy with improved parameter efficiency in low-resourc...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21042
• PDF: https://arxiv.org/pdf/2602.21042
• Github: https://github.com/AIGeeksGroup/OmniOCR
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✨LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency
📝 Summary:
LaS-Comp is a zero-shot 3D shape completion method that leverages 3D foundation models. It uses a two-stage approach for faithful reconstruction and seamless boundary refinement. This training-free framework outperforms prior state-of-the-art methods.
🔹 Publication Date: Published on Feb 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18735
• PDF: https://arxiv.org/pdf/2602.18735
• Github: https://github.com/DavidYan2001/LaS-Comp
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📝 Summary:
LaS-Comp is a zero-shot 3D shape completion method that leverages 3D foundation models. It uses a two-stage approach for faithful reconstruction and seamless boundary refinement. This training-free framework outperforms prior state-of-the-art methods.
🔹 Publication Date: Published on Feb 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18735
• PDF: https://arxiv.org/pdf/2602.18735
• Github: https://github.com/DavidYan2001/LaS-Comp
==================================
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✨One-step Language Modeling via Continuous Denoising
📝 Summary:
This paper introduces flow-based language models that use continuous denoising over one-hot token encodings. They surpass discrete diffusion models in quality and speed, particularly for few-step generation, challenging discrete diffusion's necessity for discrete data.
🔹 Publication Date: Published on Feb 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16813
• PDF: https://arxiv.org/pdf/2602.16813
• Project Page: https://one-step-lm.github.io/
• Github: https://github.com/david3684/flm
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📝 Summary:
This paper introduces flow-based language models that use continuous denoising over one-hot token encodings. They surpass discrete diffusion models in quality and speed, particularly for few-step generation, challenging discrete diffusion's necessity for discrete data.
🔹 Publication Date: Published on Feb 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16813
• PDF: https://arxiv.org/pdf/2602.16813
• Project Page: https://one-step-lm.github.io/
• Github: https://github.com/david3684/flm
==================================
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✨TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering
📝 Summary:
TextPecker proposes a reinforcement learning strategy to improve visual text rendering by perceiving and mitigating structural anomalies in text-to-image generation. It uses a new annotated dataset and synthesis engine to significantly enhance structural fidelity and semantic alignment, setting a...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20903
• PDF: https://arxiv.org/pdf/2602.20903
• Project Page: https://github.com/CIawevy/TextPecker
• Github: https://github.com/CIawevy/TextPecker
🔹 Models citing this paper:
• https://huggingface.co/CIawevy/TextPecker-8B-InternVL3
• https://huggingface.co/CIawevy/TextPecker-8B-Qwen3VL
• https://huggingface.co/CIawevy/QwenImage-TextPecker-SQPA
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CIawevy/TextPecker-1.5M
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📝 Summary:
TextPecker proposes a reinforcement learning strategy to improve visual text rendering by perceiving and mitigating structural anomalies in text-to-image generation. It uses a new annotated dataset and synthesis engine to significantly enhance structural fidelity and semantic alignment, setting a...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20903
• PDF: https://arxiv.org/pdf/2602.20903
• Project Page: https://github.com/CIawevy/TextPecker
• Github: https://github.com/CIawevy/TextPecker
🔹 Models citing this paper:
• https://huggingface.co/CIawevy/TextPecker-8B-InternVL3
• https://huggingface.co/CIawevy/TextPecker-8B-Qwen3VL
• https://huggingface.co/CIawevy/QwenImage-TextPecker-SQPA
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CIawevy/TextPecker-1.5M
==================================
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arXiv.org
TextPecker: Rewarding Structural Anomaly Quantification for...
Visual Text Rendering (VTR) remains a critical challenge in text-to-image generation, where even advanced models frequently produce text with structural anomalies such as distortion, blurriness,...