AI

Task-Decoupled Planning: Fewer Mistakes, Faster LLM Agents

Kari Jaaskelainen

13 Jan 2026 — 1 min read

TL;DR

Long-horizon LLM agents fail when one small error snowballs. Task-Decoupled Planning (TDP) breaks big jobs into a DAG of sub-goals, so planning and fixes stay local—making agents both sturdier and cheaper.

How it works:

Supervisor decomposes the task into a directed acyclic graph of sub-goals.
Planner and Executor operate with scoped context on the active sub-task only.
Errors don’t propagate; the agent re-plans just where needed.
Training-free—drop it into existing agent setups.

Why it matters: Less cognitive load, fewer cascaded mistakes, and big savings on tokens.

Results: On TravelPlanner, ScienceWorld, and HotpotQA, TDP beats strong baselines while cutting token use by up to 82%.

Paper: https://arxiv.org/abs/2601.07577v1

Paper: https://arxiv.org/abs/2601.07577v1

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AI LLM Agents Planning Research RobustAI Efficiency NLP LongHorizon TokenSavings

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