✨FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments
📝 Summary:
Failure-Aware Meta-Agentic framework improves open-source LLM performance in conversational scenarios by identifying common errors and deploying specialized agents to correct them. AI-generated summar...
🔹 Publication Date: Published on Apr 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.25135
• PDF: https://arxiv.org/pdf/2604.25135
==================================
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📝 Summary:
Failure-Aware Meta-Agentic framework improves open-source LLM performance in conversational scenarios by identifying common errors and deploying specialized agents to correct them. AI-generated summar...
🔹 Publication Date: Published on Apr 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.25135
• PDF: https://arxiv.org/pdf/2604.25135
==================================
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✨Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital
📝 Summary:
Autonomous language-model agents managing real cryptocurrency trades demonstrated high reliability through comprehensive system design encompassing prompt compilation, policy validation, and execution...
🔹 Publication Date: Published on Apr 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.26091
• PDF: https://arxiv.org/pdf/2604.26091
• Project Page: https://www.dxrg.ai/
==================================
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📝 Summary:
Autonomous language-model agents managing real cryptocurrency trades demonstrated high reliability through comprehensive system design encompassing prompt compilation, policy validation, and execution...
🔹 Publication Date: Published on Apr 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.26091
• PDF: https://arxiv.org/pdf/2604.26091
• Project Page: https://www.dxrg.ai/
==================================
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✨Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy
📝 Summary:
Adaptive quantization combined with differential privacy reduces communication overhead in federated learning while maintaining model accuracy and privacy guarantees. AI-generated summary Federated le...
🔹 Publication Date: Published on Apr 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.23426
• PDF: https://arxiv.org/pdf/2604.23426
• Github: https://github.com/eardic/FL_DPQS
==================================
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📝 Summary:
Adaptive quantization combined with differential privacy reduces communication overhead in federated learning while maintaining model accuracy and privacy guarantees. AI-generated summary Federated le...
🔹 Publication Date: Published on Apr 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.23426
• PDF: https://arxiv.org/pdf/2604.23426
• Github: https://github.com/eardic/FL_DPQS
==================================
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arXiv.org
Enhanced Privacy and Communication Efficiency in Non-IID Federated...
Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One...
✨Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data
📝 Summary:
Federated learning sample selection methods using multitask autoencoders, outlier detection techniques, and deep support vector data description enhance model accuracy under non-IID and noisy conditio...
🔹 Publication Date: Published on Apr 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.26116
• PDF: https://arxiv.org/pdf/2604.26116
• Project Page: https://github.com/eardic/FL_DPQS
==================================
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📝 Summary:
Federated learning sample selection methods using multitask autoencoders, outlier detection techniques, and deep support vector data description enhance model accuracy under non-IID and noisy conditio...
🔹 Publication Date: Published on Apr 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.26116
• PDF: https://arxiv.org/pdf/2604.26116
• Project Page: https://github.com/eardic/FL_DPQS
==================================
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✨Synthetic Computers at Scale for Long-Horizon Productivity Simulation
📝 Summary:
Synthetic Computers at Scale creates realistic computer environments with folders and content. This enables long-horizon productivity simulations for AI agents, improving their performance through experiential learning and scalable self-improvement.
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28181
• PDF: https://arxiv.org/pdf/2604.28181
• Project Page: https://huggingface.co/datasets/microsoft/synthetic-computers-at-scale
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📝 Summary:
Synthetic Computers at Scale creates realistic computer environments with folders and content. This enables long-horizon productivity simulations for AI agents, improving their performance through experiential learning and scalable self-improvement.
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28181
• PDF: https://arxiv.org/pdf/2604.28181
• Project Page: https://huggingface.co/datasets/microsoft/synthetic-computers-at-scale
==================================
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✨Heterogeneous Scientific Foundation Model Collaboration
📝 Summary:
Eywa is a heterogeneous agentic framework that extends language-centric systems to scientific foundation models by integrating domain-specific models with language-based reasoning interfaces for impro...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27351
• PDF: https://arxiv.org/pdf/2604.27351
• Project Page: https://www.zihao.website/eywa.github.io/
• Github: https://www.zihao.website/eywa.github.io/
==================================
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📝 Summary:
Eywa is a heterogeneous agentic framework that extends language-centric systems to scientific foundation models by integrating domain-specific models with language-based reasoning interfaces for impro...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27351
• PDF: https://arxiv.org/pdf/2604.27351
• Project Page: https://www.zihao.website/eywa.github.io/
• Github: https://www.zihao.website/eywa.github.io/
==================================
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✨Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
📝 Summary:
Visual generation models need to advance beyond appearance synthesis to incorporate structural, dynamic, and causal understanding through a five-level taxonomy spanning from atomic to world-modeling g...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28185
• PDF: https://arxiv.org/pdf/2604.28185
==================================
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📝 Summary:
Visual generation models need to advance beyond appearance synthesis to incorporate structural, dynamic, and causal understanding through a five-level taxonomy spanning from atomic to world-modeling g...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28185
• PDF: https://arxiv.org/pdf/2604.28185
==================================
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✨Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists
📝 Summary:
Intern-Atlas presents a methodological evolution graph that captures structured relationships between research methods across AI literature, enabling automated tracking of methodological development a...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28158
• PDF: https://arxiv.org/pdf/2604.28158
• Project Page: https://intern-atlas.opendatalab.org.cn/
==================================
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📝 Summary:
Intern-Atlas presents a methodological evolution graph that captures structured relationships between research methods across AI literature, enabling automated tracking of methodological development a...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28158
• PDF: https://arxiv.org/pdf/2604.28158
• Project Page: https://intern-atlas.opendatalab.org.cn/
==================================
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✨Representation Fréchet Loss for Visual Generation
📝 Summary:
Fréchet Distance can be effectively optimized as a training objective when decoupling population size from batch size, leading to improved generator quality and alternative evaluation metrics. AI-gene...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28190
• PDF: https://arxiv.org/pdf/2604.28190
• Github: https://github.com/Jiawei-Yang/FD-Loss
🔹 Models citing this paper:
• https://huggingface.co/jjiaweiyang/FD-Loss
==================================
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📝 Summary:
Fréchet Distance can be effectively optimized as a training objective when decoupling population size from batch size, leading to improved generator quality and alternative evaluation metrics. AI-gene...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28190
• PDF: https://arxiv.org/pdf/2604.28190
• Github: https://github.com/Jiawei-Yang/FD-Loss
🔹 Models citing this paper:
• https://huggingface.co/jjiaweiyang/FD-Loss
==================================
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✨World2Minecraft: Occupancy-Driven Simulated Scenes Construction
📝 Summary:
World2Minecraft converts real-world scenes into structured Minecraft environments using 3D semantic occupancy prediction, with MinecraftOcc dataset enhancing occupancy prediction benchmarks for embodi...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27578
• PDF: https://arxiv.org/pdf/2604.27578
• Project Page: https://world2minecraft.github.io/
• Github: https://github.com/Nepenthes-zlc/World2Minecraft
==================================
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📝 Summary:
World2Minecraft converts real-world scenes into structured Minecraft environments using 3D semantic occupancy prediction, with MinecraftOcc dataset enhancing occupancy prediction benchmarks for embodi...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27578
• PDF: https://arxiv.org/pdf/2604.27578
• Project Page: https://world2minecraft.github.io/
• Github: https://github.com/Nepenthes-zlc/World2Minecraft
==================================
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✨The Last Human-Written Paper: Agent-Native Research Artifacts
📝 Summary:
S c i e n t i f i c p u b l i c a t i o n c o m p r e s s e s a b r a n c h i n g , i t e r a t i v e r e s e a r c h p r o c e s s i n t o a l i n e a r n a r r a t i v e , d i s c a r d i n g t h e ...
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.24658
• PDF: https://arxiv.org/pdf/2604.24658
• Project Page: https://www.orchestra-research.com/ara
• Github: https://github.com/Orchestra-Research/Agent-Native-Research-Artifact
==================================
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📝 Summary:
S c i e n t i f i c p u b l i c a t i o n c o m p r e s s e s a b r a n c h i n g , i t e r a t i v e r e s e a r c h p r o c e s s i n t o a l i n e a r n a r r a t i v e , d i s c a r d i n g t h e ...
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.24658
• PDF: https://arxiv.org/pdf/2604.24658
• Project Page: https://www.orchestra-research.com/ara
• Github: https://github.com/Orchestra-Research/Agent-Native-Research-Artifact
==================================
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✨MoCapAnything V2: End-to-End Motion Capture for Arbitrary Skeletons
📝 Summary:
A fully end-to-end framework for arbitrary-skeleton motion capture that jointly optimizes video-to-pose and pose-to-rotation prediction while addressing rotation ambiguity through reference pose-rotat...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28130
• PDF: https://arxiv.org/pdf/2604.28130
• Project Page: https://animotionlab.github.io/MoCapAnythingV2/
• Github: https://github.com/animotionlab26/MocapAnything
==================================
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📝 Summary:
A fully end-to-end framework for arbitrary-skeleton motion capture that jointly optimizes video-to-pose and pose-to-rotation prediction while addressing rotation ambiguity through reference pose-rotat...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28130
• PDF: https://arxiv.org/pdf/2604.28130
• Project Page: https://animotionlab.github.io/MoCapAnythingV2/
• Github: https://github.com/animotionlab26/MocapAnything
==================================
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✨PhyCo: Learning Controllable Physical Priors for Generative Motion
📝 Summary:
PhyCo enhances video diffusion models with physics-based control through a large-scale dataset, physics-supervised fine-tuning, and vision-language model guidance for improved physical consistency. AI...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28169
• PDF: https://arxiv.org/pdf/2604.28169
• Project Page: https://phyco-video.github.io/
==================================
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📝 Summary:
PhyCo enhances video diffusion models with physics-based control through a large-scale dataset, physics-supervised fine-tuning, and vision-language model guidance for improved physical consistency. AI...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28169
• PDF: https://arxiv.org/pdf/2604.28169
• Project Page: https://phyco-video.github.io/
==================================
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✨InteractWeb-Bench: Can Multimodal Agent Escape Blind Execution in Interactive Website Generation?
📝 Summary:
InteractWeb-Bench presents the first multimodal interactive benchmark for website generation under non-expert low-code conditions, addressing semantic misalignment through diverse user agents and inte...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27419
• PDF: https://arxiv.org/pdf/2604.27419
• Project Page: https://interactweb-bench.wangqiyao.me/
• Github: https://github.com/AIforIP/InteractWeb-Bench
==================================
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📝 Summary:
InteractWeb-Bench presents the first multimodal interactive benchmark for website generation under non-expert low-code conditions, addressing semantic misalignment through diverse user agents and inte...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27419
• PDF: https://arxiv.org/pdf/2604.27419
• Project Page: https://interactweb-bench.wangqiyao.me/
• Github: https://github.com/AIforIP/InteractWeb-Bench
==================================
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✨Co-Evolving Policy Distillation
📝 Summary:
Co-Evolving Policy Distillation enables unified integration of multiple expert capabilities through parallel training and bidirectional policy distillation, outperforming existing methods in multi-mod...
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27083
• PDF: https://arxiv.org/pdf/2604.27083
==================================
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📝 Summary:
Co-Evolving Policy Distillation enables unified integration of multiple expert capabilities through parallel training and bidirectional policy distillation, outperforming existing methods in multi-mod...
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27083
• PDF: https://arxiv.org/pdf/2604.27083
==================================
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✨ExoActor: Exocentric Video Generation as Generalizable Interactive Humanoid Control
📝 Summary:
ExoActor uses third-person video generation as a unified interface to model interaction dynamics between robots, environments, and objects, enabling task-conditioned humanoid behaviors through motion ...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27711
• PDF: https://arxiv.org/pdf/2604.27711
• Project Page: https://baai-agents.github.io/ExoActor/
==================================
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📝 Summary:
ExoActor uses third-person video generation as a unified interface to model interaction dynamics between robots, environments, and objects, enabling task-conditioned humanoid behaviors through motion ...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27711
• PDF: https://arxiv.org/pdf/2604.27711
• Project Page: https://baai-agents.github.io/ExoActor/
==================================
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✨Leveraging Verifier-Based Reinforcement Learning in Image Editing
📝 Summary:
This paper introduces Edit-R1, a framework for image editing that uses a chain-of-thought verifier-based reasoning reward model Edit-RRM. Edit-RRM provides fine-grained, principle-based rewards, overcoming limitations of existing models. This approach significantly enhances image editing performa...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27505
• PDF: https://arxiv.org/pdf/2604.27505
==================================
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📝 Summary:
This paper introduces Edit-R1, a framework for image editing that uses a chain-of-thought verifier-based reasoning reward model Edit-RRM. Edit-RRM provides fine-grained, principle-based rewards, overcoming limitations of existing models. This approach significantly enhances image editing performa...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27505
• PDF: https://arxiv.org/pdf/2604.27505
==================================
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✨Length Value Model: Scalable Value Pretraining for Token-Level Length Modeling
📝 Summary:
LenVM is a token-level framework that models remaining generation length as a value estimation problem. It improves length control and efficiency in autoregressive models, significantly outperforming baselines and enabling continuous control over performance-efficiency trade-offs.
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27039
• PDF: https://arxiv.org/pdf/2604.27039
• Project Page: https://length-value-model.github.io/
• Github: https://length-value-model.github.io/demo/index.html
==================================
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📝 Summary:
LenVM is a token-level framework that models remaining generation length as a value estimation problem. It improves length control and efficiency in autoregressive models, significantly outperforming baselines and enabling continuous control over performance-efficiency trade-offs.
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27039
• PDF: https://arxiv.org/pdf/2604.27039
• Project Page: https://length-value-model.github.io/
• Github: https://length-value-model.github.io/demo/index.html
==================================
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✨Efficient Training on Multiple Consumer GPUs with RoundPipe
📝 Summary:
RoundPipe introduces a novel pipeline scheduling approach that eliminates weight binding constraints in LLM fine-tuning, enabling efficient training on consumer GPUs through dynamic stage distribution...
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27085
• PDF: https://arxiv.org/pdf/2604.27085
• Project Page: https://itcarrot.github.io/RoundPipe/
• Github: https://github.com/ITcarrot/RoundPipe
==================================
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📝 Summary:
RoundPipe introduces a novel pipeline scheduling approach that eliminates weight binding constraints in LLM fine-tuning, enabling efficient training on consumer GPUs through dynamic stage distribution...
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27085
• PDF: https://arxiv.org/pdf/2604.27085
• Project Page: https://itcarrot.github.io/RoundPipe/
• Github: https://github.com/ITcarrot/RoundPipe
==================================
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✨Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows
📝 Summary:
Claw-Eval-Live presents a dynamic benchmark for evaluating workflow agents that tracks evolving demands and verifies task execution through detailed logging and structured assessment methods. AI-gener...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28139
• PDF: https://arxiv.org/pdf/2604.28139
• Project Page: https://claw-eval-live.github.io
• Github: https://claw-eval-live.github.io
==================================
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📝 Summary:
Claw-Eval-Live presents a dynamic benchmark for evaluating workflow agents that tracks evolving demands and verifies task execution through detailed logging and structured assessment methods. AI-gener...
🔹 Publication Date: Published on Apr 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28139
• PDF: https://arxiv.org/pdf/2604.28139
• Project Page: https://claw-eval-live.github.io
• Github: https://claw-eval-live.github.io
==================================
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