✨Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration

📝 Summary:
Agents equipped with intrinsic meta-evolution capabilities demonstrate improved performance on web navigation tasks through self-generated world knowledge without external supervision. AI-generated su...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18131
• PDF: https://arxiv.org/pdf/2604.18131
• Github: https://github.com/Bklight999/world-knowledge

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✨ClawEnvKit: Automatic Environment Generation for Claw-Like Agents

📝 Summary:
An automated pipeline generates diverse, verified environments for claw-like agents from natural language descriptions, enabling large-scale benchmark construction and continuous evaluation. AI-genera...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18543
• PDF: https://arxiv.org/pdf/2604.18543
• Github: https://github.com/xirui-li/ClawEnvKit

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✨MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval

📝 Summary:
MathNet is a large-scale, multilingual, multimodal dataset of Olympiad-level math problems designed for evaluating mathematical reasoning and retrieval in generative models and embedding-based systems...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18584
• PDF: https://arxiv.org/pdf/2604.18584
• Project Page: https://mathnet.mit.edu/

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✨Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations

📝 Summary:
Multi-strategy utterance generation methods for emotional support conversations outperform single-strategy approaches by enabling multiple support strategies within individual utterances. AI-generated...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17972
• PDF: https://arxiv.org/pdf/2604.17972
• Project Page: https://github.com/aliyun/qwen-dianjin

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✨EvoMaster: A Foundational Agent Framework for Building Evolving Autonomous Scientific Agents at Scale

📝 Summary:
EvoMaster is a scalable, self-evolving agent framework designed for large-scale scientific discovery that enables iterative hypothesis refinement and knowledge accumulation across experimental cycles....

🔹 Publication Date: Published on Apr 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17406
• PDF: https://arxiv.org/pdf/2604.17406
• Github: https://github.com/sjtu-sai-agents/EvoMaster

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✨When Can LLMs Learn to Reason with Weak Supervision?

📝 Summary:
Research reveals that model generalization in reasoning tasks under weak supervision depends on reward saturation dynamics and reasoning faithfulness, with supervised fine-tuning on explicit traces be...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18574
• PDF: https://arxiv.org/pdf/2604.18574
• Project Page: https://salmanrahman.net/rlvr-weak-supervision
• Github: https://github.com/pavelslab-nyu/rlvr-weak-supervision

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✨EasyVideoR1: Easier RL for Video Understanding

📝 Summary:
EasyVideoR1 presents an efficient reinforcement learning framework for video understanding that improves training throughput, supports diverse video tasks, and enables joint image-video training with ...

🔹 Publication Date: Published on Apr 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16893
• PDF: https://arxiv.org/pdf/2604.16893
• Github: https://github.com/cyuQ1n/EasyVideoR1

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✨OmniScript: Towards Audio-Visual Script Generation for Long-Form Cinematic Video

📝 Summary:
A novel video-to-script task is introduced along with OmniScript, an 8B-parameter omni-modal language model trained through progressive pipeline techniques for long-form narrative comprehension and te...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11102
• PDF: https://arxiv.org/pdf/2604.11102
• Project Page: https://arcomniscript.github.io

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✨Back to Repair: A Minimal Denoising Network\ for Time Series Anomaly Detection

📝 Summary:
JuRe, a simple denoising network for time series anomaly detection, demonstrates that architectural simplicity can match or exceed complex models when the training objective properly implements the ma...

🔹 Publication Date: Published on Apr 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17388
• PDF: https://arxiv.org/pdf/2604.17388
• Project Page: https://huggingface.co/papers?q=manifold-projection%20principle
• Github: https://github.com/iis-esslingen/JuRe

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✨OpenGame: Open Agentic Coding for Games

📝 Summary:
OpenGame is an open-source agentic framework for end-to-end web game creation that uses specialized code models and evaluation benchmarks to overcome challenges in interactive application development....

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18394
• PDF: https://arxiv.org/pdf/2604.18394
• Project Page: https://www.opengame-project-page.com/
• Github: https://github.com/leigest519/OpenGame

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✨HSG: Hyperbolic Scene Graph

📝 Summary:
Hyperbolic Scene Graph (HSG) improves scene graph modeling by learning embeddings in hyperbolic space, enhancing hierarchical structure quality and retrieval performance through natural encoding of hi...

🔹 Publication Date: Published on Apr 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17454
• PDF: https://arxiv.org/pdf/2604.17454
• Github: https://github.com/AIGeeksGroup/HSG

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✨SkillFlow:Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agents

📝 Summary:
SkillFlow presents a benchmark for evaluating autonomous agents' ability to discover, repair, and maintain skills over time through a structured lifelong learning protocol. AI-generated summary As the...

🔹 Publication Date: Published on Apr 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17308
• PDF: https://arxiv.org/pdf/2604.17308
• Project Page: https://zhangzi-a.github.io/SkillFlow-project-page/
• Github: https://github.com/ZhangZi-a/SkillFlow

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✨Beyond Text-Dominance: Understanding Modality Preference of Omni-modal Large Language Models

📝 Summary:
Native omni-modal LLMs surprisingly show a visual preference, unlike traditional text-dominant models. This preference emerges in later layers and helps diagnose cross-modal hallucinations, improving model trustworthiness.

🔹 Publication Date: Published on Apr 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16902
• PDF: https://arxiv.org/pdf/2604.16902
• Github: https://github.com/icip-cas/OmniPreference

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#OmniModalLLM #ModalityPreference #AIHallucinations #TrustworthyAI #AIResearch

✨GFT: From Imitation to Reward Fine-Tuning with Unbiased Group Advantages and Dynamic Coefficient Rectification

📝 Summary:
Group Fine-Tuning addresses limitations in supervised fine-tuning by using diverse response groups and adaptive weight bounding to improve training stability and efficiency. AI-generated summary Large...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14258
• PDF: https://arxiv.org/pdf/2604.14258
• Project Page: https://arxiv.org/abs/2604.14258
• Github: https://github.com/ZJU-OmniAI/GFT/tree/main

✨ Datasets citing this paper:
• https://huggingface.co/datasets/OmniAI-ZJU/NuminaMath-Cot-Distillation-100K

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✨Latent Preference Modeling for Cross-Session Personalized Tool Calling

📝 Summary:
Personalized tool calling in LLM-based agents is improved through memory-augmented methods that capture user choice reasoning rather than just choices, using minimal token overhead. AI-generated summa...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17886
• PDF: https://arxiv.org/pdf/2604.17886
• Project Page: https://still-with-you.github.io/pages/prefine/

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✨Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation

📝 Summary:
Researchers extended one-step MeanFlow image generation from class labels to text inputs. They found that limited refinement steps require highly discriminative text representations. By integrating a powerful LLM-based text encoder, they achieved efficient text-conditioned synthesis and improved ...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18168
• PDF: https://arxiv.org/pdf/2604.18168
• Github: https://github.com/AMAP-ML/EMF

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✨Modeling Sparse and Bursty Vulnerability Sightings: Forecasting Under Data Constraints

📝 Summary:
Forecasting vulnerability-related activities using time-series models reveals challenges with sparse, bursty data, favoring count-based methods like Poisson regression for more stable predictions. AI-...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16038
• PDF: https://arxiv.org/pdf/2604.16038
• Project Page: https://github.com/vulnerability-lookup/TARDISsight
• Github: https://github.com/vulnerability-lookup/TARDISsight

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✨Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding

📝 Summary:
This paper improves vision-language models for compositional reasoning by using concreteness-based negative sample selection and a novel margin-based loss. Their framework, Slipform, achieves state-of-the-art accuracy on compositional benchmarks and cross-modal retrieval.

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.13313
• PDF: https://arxiv.org/pdf/2604.13313

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#VisionLanguage #DeepLearning #AIResearch #ComputerVision #NLP

✨GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)

📝 Summary:
GenericAgent is a self-evolving large language model agent system that maximizes context information density through hierarchical memory, reusable SOPs, and efficient compression to overcome long-hori...

🔹 Publication Date: Published on Apr 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17091
• PDF: https://arxiv.org/pdf/2604.17091
• Github: https://github.com/lsdefine/GenericAgent

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✨Agents Explore but Agents Ignore: LLMs Lack Environmental Curiosity

📝 Summary:
LLM-based agents fail to exploit discovered unexpected information despite recognizing it, indicating a lack of environmental curiosity that depends on tools, compute, and training data distribution. ...

🔹 Publication Date: Published on Apr 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17609
• PDF: https://arxiv.org/pdf/2604.17609

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