Hugging Face Trending Papers
Hierarchical Task Network planning in the Goal Lifecycle
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arXiv AI
5d ago
Hierarchical Task Network planning in the Goal Lifecycle
arXiv:2610.34668v1 Announce Type: new Abstract: 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...
arXiv AI
1d ago
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.
Hugging Face Trending Papers
3d ago
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.
arXiv AI
Oct 15
ParallelWorld: Test-Time Scaling for Agentic Embodied Reasoning
arXiv:2608.22971v2 Announce Type: replace Abstract: Embodied AI has emerged as a key paradigm for autonomous systems operating in complex physical environments. While end-to-end models have establish...
arXiv AI
Sep 4
Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models
The paper introduces Imagine-then-Plan (ITP), a framework that lets agents learn by interacting with a learned world model to generate multi-step imagined trajectories. ITP features an adaptive lookahead mechanism that balances ultimate goals with task progress, producing richer signals about future outcomes. Experiments on various benchmarks show that ITP outperforms existing baselines, and analyses suggest the adaptive lookahead improves reasoning for complex tasks.
arXiv AI
Aug 6
EASy: Towards Efficient LLM-Based Agentic System
arXiv:2608. 04588v1 Announce Type: cross Abstract: Agentic systems have emerged as a promising paradigm for solving complex tasks by coordinating specialized LLM-based agents.