arXiv AI
Hierarchical Task Network planning in the Goal Lifecycle
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Hierarchical Task Network planning in the Goal Lifecycle
Goal reasoning (GR) is the process by which intelligent agents continually reason about the goals they are pursuing, which may lead to goal change, for instance when opportunities arise, or when event...
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Goal-Aware Dynamic Multi-Agent Collaboration for Long-Horizon Tasks
The paper introduces Goal-Aware Dynamic Multi-Agent Collaboration (GAD), a framework that assigns distinct duties to separate agents with individualized contexts to tackle long-horizon tasks. It employs a growing execution graph to structure subtasks and their dependencies, ensuring each agent retains the original instruction, task goal, and acceptance conditions. GAD dynamically adapts collaboration and information sharing, using verification and recovery checks to monitor subtask results and overall task completion, providing feedback for repair when checks fail.
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Goal-Aware Dynamic Multi-Agent Collaboration for Long-Horizon Tasks
The paper investigates how large language model agents handle long‑horizon tasks and finds that verification is crucial for success but often occurs too late. It shows that sharing a common context dilutes duty‑relevant information and that simply separating duties among agents is insufficient, as agents can lose sight of the overall goal. To address these issues, the authors propose Goal‑Aware Dynamic Multi‑Agent Collaboration (GAD), which uses an execution graph to organize subtasks, keeps each agent’s context focused on its specific goal, and dynamically adjusts task dependencies and information sharing based on execution evidence, with verification and recovery checks providing feedback for repair.
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REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation
arXiv:2503. 22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition.
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ParallelWorld: Test-Time Scaling for Agentic Embodied Reasoning
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Scaling Agentic Data for Long-Horizon Terminal Intelligence
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