✨FactReview: Evidence-Grounded Reviews with Literature Positioning and Execution-Based Claim Verification
📝 Summary:
FactReview is an evidence-grounded peer review system for machine learning that analyzes manuscript claims through claim extraction, literature positioning, and execution-based verification to provide...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04074
• PDF: https://arxiv.org/pdf/2604.04074
==================================
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📝 Summary:
FactReview is an evidence-grounded peer review system for machine learning that analyzes manuscript claims through claim extraction, literature positioning, and execution-based verification to provide...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04074
• PDF: https://arxiv.org/pdf/2604.04074
==================================
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✨Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding
📝 Summary:
Video-MME-v2 presents a comprehensive benchmark for evaluating video understanding models through a progressive hierarchy and group-based evaluation to assess robustness and faithfulness. AI-generated...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.05015
• PDF: https://arxiv.org/pdf/2604.05015
• Project Page: https://video-mme-v2.netlify.app/
• Github: https://github.com/MME-Benchmarks/Video-MME-v2
✨ Datasets citing this paper:
• https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2
==================================
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📝 Summary:
Video-MME-v2 presents a comprehensive benchmark for evaluating video understanding models through a progressive hierarchy and group-based evaluation to assess robustness and faithfulness. AI-generated...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.05015
• PDF: https://arxiv.org/pdf/2604.05015
• Project Page: https://video-mme-v2.netlify.app/
• Github: https://github.com/MME-Benchmarks/Video-MME-v2
✨ Datasets citing this paper:
• https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2
==================================
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✨Learning to Retrieve from Agent Trajectories
📝 Summary:
Retrieval models for agentic search should be trained directly from agent interaction data using a new paradigm that mines supervision from multi-step agent trajectories and incorporates relevance int...
🔹 Publication Date: Published on Mar 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04949
• PDF: https://arxiv.org/pdf/2604.04949
• Project Page: https://yuqi-zhou.github.io/LRAT-homepage/
• Github: https://github.com/Yuqi-Zhou/LRAT
==================================
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📝 Summary:
Retrieval models for agentic search should be trained directly from agent interaction data using a new paradigm that mines supervision from multi-step agent trajectories and incorporates relevance int...
🔹 Publication Date: Published on Mar 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04949
• PDF: https://arxiv.org/pdf/2604.04949
• Project Page: https://yuqi-zhou.github.io/LRAT-homepage/
• Github: https://github.com/Yuqi-Zhou/LRAT
==================================
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✨Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents
📝 Summary:
Claw-Eval addresses limitations in agent benchmarks by providing comprehensive evaluation across multiple modalities with trajectory-aware grading and safety assessments. AI-generated summary Large la...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06132
• PDF: https://arxiv.org/pdf/2604.06132
• Project Page: https://claw-eval.github.io/
• Github: https://github.com/claw-eval/claw-eval
==================================
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📝 Summary:
Claw-Eval addresses limitations in agent benchmarks by providing comprehensive evaluation across multiple modalities with trajectory-aware grading and safety assessments. AI-generated summary Large la...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06132
• PDF: https://arxiv.org/pdf/2604.06132
• Project Page: https://claw-eval.github.io/
• Github: https://github.com/claw-eval/claw-eval
==================================
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✨Vanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision
📝 Summary:
Vanast is a unified framework that generates garment-transferred human animation videos by combining image-based virtual try-on and pose-driven animation in a single process, addressing issues like id...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04934
• PDF: https://arxiv.org/pdf/2604.04934
• Project Page: https://hyunsoocha.github.io/vanast/
• Github: https://github.com/snuvclab/vanast
==================================
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📝 Summary:
Vanast is a unified framework that generates garment-transferred human animation videos by combining image-based virtual try-on and pose-driven animation in a single process, addressing issues like id...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04934
• PDF: https://arxiv.org/pdf/2604.04934
• Project Page: https://hyunsoocha.github.io/vanast/
• Github: https://github.com/snuvclab/vanast
==================================
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✨ACES: Who Tests the Tests? Leave-One-Out AUC Consistency for Code Generation
📝 Summary:
Researchers address the challenge of selecting correct code candidates from LLM-generated outputs by developing ACES, a method that ranks tests based on their ability to distinguish correct from incor...
🔹 Publication Date: Published on Apr 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03922
• PDF: https://arxiv.org/pdf/2604.03922
==================================
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📝 Summary:
Researchers address the challenge of selecting correct code candidates from LLM-generated outputs by developing ACES, a method that ranks tests based on their ability to distinguish correct from incor...
🔹 Publication Date: Published on Apr 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03922
• PDF: https://arxiv.org/pdf/2604.03922
==================================
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✨MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control
📝 Summary:
An adaptive multimodal embedding framework that selectively applies reasoning through latent variables and reinforcement learning to improve efficiency and performance on benchmark tasks. AI-generated...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06156
• PDF: https://arxiv.org/pdf/2604.06156
==================================
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📝 Summary:
An adaptive multimodal embedding framework that selectively applies reasoning through latent variables and reinforcement learning to improve efficiency and performance on benchmark tasks. AI-generated...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06156
• PDF: https://arxiv.org/pdf/2604.06156
==================================
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✨In-Place Test-Time Training
📝 Summary:
In-Place Test-Time Training enables large language models to adapt parameters during inference by modifying the final projection matrix in MLP blocks with a task-aligned objective and efficient update...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06169
• PDF: https://arxiv.org/pdf/2604.06169
• Github: https://github.com/ByteDance-Seed/In-Place-TTT
==================================
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📝 Summary:
In-Place Test-Time Training enables large language models to adapt parameters during inference by modifying the final projection matrix in MLP blocks with a task-aligned objective and efficient update...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06169
• PDF: https://arxiv.org/pdf/2604.06169
• Github: https://github.com/ByteDance-Seed/In-Place-TTT
==================================
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✨Demystifying When Pruning Works via Representation Hierarchies
📝 Summary:
Network pruning affects different representation spaces differently, leading to varying performance across tasks due to instability in probability space transformations during generation. AI-generated...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24652
• PDF: https://arxiv.org/pdf/2603.24652
• Github: https://github.com/CASE-Lab-UMD/Pruning-on-Representations
==================================
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📝 Summary:
Network pruning affects different representation spaces differently, leading to varying performance across tasks due to instability in probability space transformations during generation. AI-generated...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24652
• PDF: https://arxiv.org/pdf/2603.24652
• Github: https://github.com/CASE-Lab-UMD/Pruning-on-Representations
==================================
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✨Experience Transfer for Multimodal LLM Agents in Minecraft Game
📝 Summary:
Echo is a transfer-oriented memory framework for multimodal LLM agents that enables efficient task solving in complex game environments by deriving actionable knowledge from prior interactions through...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05533
• PDF: https://arxiv.org/pdf/2604.05533
==================================
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📝 Summary:
Echo is a transfer-oriented memory framework for multimodal LLM agents that enables efficient task solving in complex game environments by deriving actionable knowledge from prior interactions through...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05533
• PDF: https://arxiv.org/pdf/2604.05533
==================================
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✨Action Images: End-to-End Policy Learning via Multiview Video Generation
📝 Summary:
World action models that formulate policy learning as multiview video generation use pixel-grounded action images to enable zero-shot policy learning without separate action modules. AI-generated summ...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06168
• PDF: https://arxiv.org/pdf/2604.06168
• Project Page: https://actionimages.github.io/
==================================
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📝 Summary:
World action models that formulate policy learning as multiview video generation use pixel-grounded action images to enable zero-shot policy learning without separate action modules. AI-generated summ...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06168
• PDF: https://arxiv.org/pdf/2604.06168
• Project Page: https://actionimages.github.io/
==================================
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✨Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning
📝 Summary:
A closed-loop framework for graphics program synthesis combines a large-scale dataset and benchmark with a novel reinforcement learning optimization method to improve the generation of executable TikZ...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06079
• PDF: https://arxiv.org/pdf/2604.06079
==================================
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📝 Summary:
A closed-loop framework for graphics program synthesis combines a large-scale dataset and benchmark with a novel reinforcement learning optimization method to improve the generation of executable TikZ...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06079
• PDF: https://arxiv.org/pdf/2604.06079
==================================
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✨MedGemma 1.5 Technical Report
📝 Summary:
MedGemma 1.5 4B enhances medical AI capabilities through expanded multimodal support and improved performance across medical imaging, document understanding, and clinical reasoning tasks. AI-generated...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05081
• PDF: https://arxiv.org/pdf/2604.05081
==================================
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📝 Summary:
MedGemma 1.5 4B enhances medical AI capabilities through expanded multimodal support and improved performance across medical imaging, document understanding, and clinical reasoning tasks. AI-generated...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05081
• PDF: https://arxiv.org/pdf/2604.05081
==================================
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🔥1
✨ThinkTwice: Jointly Optimizing Large Language Models for Reasoning and Self-Refinement
📝 Summary:
ThinkTwice is a two-phase framework that jointly optimizes large language models for reasoning and self-refinement using Group Relative Policy Optimization, demonstrating improved performance on mathe...
🔹 Publication Date: Published on Apr 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01591
• PDF: https://arxiv.org/pdf/2604.01591
• Github: https://github.com/CSSLab/ThinkTwice
==================================
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📝 Summary:
ThinkTwice is a two-phase framework that jointly optimizes large language models for reasoning and self-refinement using Group Relative Policy Optimization, demonstrating improved performance on mathe...
🔹 Publication Date: Published on Apr 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01591
• PDF: https://arxiv.org/pdf/2604.01591
• Github: https://github.com/CSSLab/ThinkTwice
==================================
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✨Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework
📝 Summary:
A multi-agent system called Paper Circle is presented that automates the discovery and analysis of scientific literature through integrated retrieval and knowledge graph construction capabilities. AI-...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06170
• PDF: https://arxiv.org/pdf/2604.06170
• Project Page: https://papercircle.vercel.app/
• Github: https://github.com/MAXNORM8650/papercircle
==================================
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📝 Summary:
A multi-agent system called Paper Circle is presented that automates the discovery and analysis of scientific literature through integrated retrieval and knowledge graph construction capabilities. AI-...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06170
• PDF: https://arxiv.org/pdf/2604.06170
• Project Page: https://papercircle.vercel.app/
• Github: https://github.com/MAXNORM8650/papercircle
==================================
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✨How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings
📝 Summary:
Research demonstrates that skill utilization in LLM-based agents degrades significantly under realistic conditions where skills must be retrieved and refined rather than handcrafted, though targeted r...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04323
• PDF: https://arxiv.org/pdf/2604.04323
• Project Page: https://github.com/UCSB-NLP-Chang/Skill-Usage
• Github: https://github.com/UCSB-NLP-Chang/Skill-Usage
==================================
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📝 Summary:
Research demonstrates that skill utilization in LLM-based agents degrades significantly under realistic conditions where skills must be retrieved and refined rather than handcrafted, though targeted r...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04323
• PDF: https://arxiv.org/pdf/2604.04323
• Project Page: https://github.com/UCSB-NLP-Chang/Skill-Usage
• Github: https://github.com/UCSB-NLP-Chang/Skill-Usage
==================================
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✨MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU
📝 Summary:
MegaTrain trains large language models with over 100 billion parameters on a single GPU. It stores parameters in host memory and streams them to the GPU using pipelined execution and stateless layer templates to overcome bandwidth. This enables 120 billion parameter training and outperforms other...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05091
• PDF: https://arxiv.org/pdf/2604.05091
• Github: https://github.com/DLYuanGod/MegaTrain
==================================
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📝 Summary:
MegaTrain trains large language models with over 100 billion parameters on a single GPU. It stores parameters in host memory and streams them to the GPU using pipelined execution and stateless layer templates to overcome bandwidth. This enables 120 billion parameter training and outperforms other...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05091
• PDF: https://arxiv.org/pdf/2604.05091
• Github: https://github.com/DLYuanGod/MegaTrain
==================================
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✨GBQA: A Game Benchmark for Evaluating LLMs as Quality Assurance Engineers
📝 Summary:
Large language models struggle with autonomous bug discovery in complex runtime environments, as demonstrated by a new game development benchmark that reveals limited effectiveness of current approach...
🔹 Publication Date: Published on Apr 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02648
• PDF: https://arxiv.org/pdf/2604.02648
• Github: https://github.com/camel-ai/GBQA
==================================
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📝 Summary:
Large language models struggle with autonomous bug discovery in complex runtime environments, as demonstrated by a new game development benchmark that reveals limited effectiveness of current approach...
🔹 Publication Date: Published on Apr 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02648
• PDF: https://arxiv.org/pdf/2604.02648
• Github: https://github.com/camel-ai/GBQA
==================================
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🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning
Both code and weights are available under the MIT license on HuggingFace.
👉 Key details:
• Trained from scratch (not a finetune) on proprietary data and infrastructure
• Mixture-of-Experts (MoE) architecture
Models:
🧠 GigaChat-3.1 Ultra
• 702B MoE model for high-performance environments
• Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
• Supports FP8 training and MTP
⚡️ GigaChat-3.1 Lightning
• 10B model (1.8B active parameters)
• Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
• Efficient local inference
• Up to 256k context
Engineering highlights:
• Custom metric to detect and reduce generation loops
• DPO training moved to native FP8
• Improvements in post-training pipeline
• Identified and fixed a critical issue affecting evaluation quality
🌍 Trained on 14 languages (optimized for English and Russian)
Use cases:
• chatbots
• AI assistants
• copilots
• internal ML systems
Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.
Both code and weights are available under the MIT license on HuggingFace.
👉 Key details:
• Trained from scratch (not a finetune) on proprietary data and infrastructure
• Mixture-of-Experts (MoE) architecture
Models:
🧠 GigaChat-3.1 Ultra
• 702B MoE model for high-performance environments
• Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
• Supports FP8 training and MTP
⚡️ GigaChat-3.1 Lightning
• 10B model (1.8B active parameters)
• Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
• Efficient local inference
• Up to 256k context
Engineering highlights:
• Custom metric to detect and reduce generation loops
• DPO training moved to native FP8
• Improvements in post-training pipeline
• Identified and fixed a critical issue affecting evaluation quality
🌍 Trained on 14 languages (optimized for English and Russian)
Use cases:
• chatbots
• AI assistants
• copilots
• internal ML systems
Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.
❤1
✨QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization
📝 Summary:
PRepair tackles over-editing in AI program repair by maximizing correct code reuse. It combines controlled bug injection and edit-aware policy optimization using an edit-aware reward. This framework significantly improves repair precision and decoding throughput.
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05963
• PDF: https://arxiv.org/pdf/2604.05963
• Github: https://github.com/kcxain/QiMeng-PRepair
🔹 Models citing this paper:
• https://huggingface.co/kcxain/Prepair-Python-7B-EA
• https://huggingface.co/kcxain/Prepair-Verilog-7B-EA
==================================
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📝 Summary:
PRepair tackles over-editing in AI program repair by maximizing correct code reuse. It combines controlled bug injection and edit-aware policy optimization using an edit-aware reward. This framework significantly improves repair precision and decoding throughput.
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05963
• PDF: https://arxiv.org/pdf/2604.05963
• Github: https://github.com/kcxain/QiMeng-PRepair
🔹 Models citing this paper:
• https://huggingface.co/kcxain/Prepair-Python-7B-EA
• https://huggingface.co/kcxain/Prepair-Verilog-7B-EA
==================================
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