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RegoMender: A Multi-agent Framework for Automated Rego Policy Repair
Arxiv
0+阅读 · 10月21日
Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration
Arxiv
0+阅读 · 10月13日
VideoResearcher: Self-Improving Tool Design for Long-Video Understanding
Arxiv
0+阅读 · 9月17日
EvolveNav: Proactive Preflection and Self-Evolving Memory for Zero-Shot Object Goal Navigation
Arxiv
0+阅读 · 6月16日
Generative AI and the future of scientometrics: current topics and future questions
Arxiv
0+阅读 · 6月15日
Sibyl-AutoResearch: Autonomous Research Needs Self-Evolving Trial-and-Error Harnesses, Not Paper Generators
Arxiv
0+阅读 · 5月21日
Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs
Arxiv
0+阅读 · 3月20日
Survey on Neural Routing Solvers
Arxiv
0+阅读 · 3月15日
Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs
Arxiv
0+阅读 · 2月24日
Modeling Trial-and-Error Navigation With a Sequential Decision Model of Information Scent
Arxiv
0+阅读 · 3月12日
Survey on Neural Routing Solvers
Arxiv
0+阅读 · 2月25日
Localized Control in Diffusion Models via Latent Vector Prediction
Arxiv
0+阅读 · 2月11日
Combining Example-Based and Rule-Based Program Transformations to Resolve Build Conflicts
Arxiv
0+阅读 · 2月11日
Beyond Trial-and-Error: Predicting User Abandonment After a Moderation Intervention
Arxiv
0+阅读 · 2月6日
Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science
Arxiv
0+阅读 · 2月5日
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