Atomic Task Graph: A Unified Framework for Agentic Planning and Execution

Published
Source
arXiv
Paper number
592
Field
AI Agents
arXiv ID
2607.01942

Key points

  • The core idea is to represent agent task solving as a DAG composed of atomic tool-use nodes and input-output dependency edges.
  • Interface-preserving recursive graph compilation recursively decomposes coarse tasks down to atomic units while preserving the parent node's input-output interface, making graph evolution traceable.
  • Dependency-aware execution runs independent branches in parallel, shortening the average number of steps versus ReAct from 31.42 to 18.36 on ALFWorld and from 47.35 to 29.72 on ScienceWorld.
  • Minimal necessary subgraph repair preserves verified regions and repairs only the smallest affected subgraph when errors occur, and removing the repair step lowers Mistral-7B's ALFWorld score by 7.72 points.
  • A pre-execution thought experiment uses lightweight verification before execution to detect risky plans in advance, finding 24.6% to 27.4% risky-plan detection with over 74% precision.
  • The hallucinated action rate is 12.14%, a 71.7% relative reduction versus ReAct's 42.86%, and the average success rate across three benchmarks rises from 25.51 to 56.10 with Mistral-7B.

Paper links

External research summaries. These are not HDATF publications or measured product results.

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