arXiv AI
Policy Search in Code Space for Multi-Agent Pathfinding
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ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
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Hugging Face Trending Papers
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The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows
Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods. We ask a fundamentally different question: how much of this search policy can be internalized by a single tool-using agent?
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Coding Agents for Generalized Task and Motion Planning Problems
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Coding Agents for Generalized Task and Motion Planning Problems
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arXiv AI
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The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows
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arXiv AI
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Physical Agentic AI: An Architecture for Orchestrating a Robot Crew with LLMs
Physical Agentic AI proposes an architecture that links semantic planning with physical execution for robot crews. Each robot exposes a typed skill library, while a foundation model planner decomposes tasks into phases and assigns robot‑skill pairs. A Robot Orchestrator validates and authorizes one skill at a time, ensuring actions are grounded in robot capabilities, system state, and workflow constraints before actuation.