mlsanityData to 7 Oct 2026

GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI

arXiv 2609.30147 ↗First posted 24 Sep 2026cs.AI cs.CL cs.LGcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks also fits: Reinforcement learning for LLM agents

Abstract (arXiv)

Large Language Models (LLMs) typically exhibit a performance profile where reliability degrades as task complexity increases. We address the challenge of generating high-quality natural language executable plans for complex tasks by introducing GRASP, a strategy-aware, multi-stage planning framework. GRASP decouples the planning pipeline across specialized, context-isolated modules: it pre-compiles global macro-guidelines (GenPlan), explores alternative localized strategies within isolated context windows (RevPlan), and independently evaluates trajectories using a multi-criteria discriminator (VerPlan). Empirical evaluations show that GRASP consistently establishes a new state-of-the-art frontier across diverse datasets, yielding substantial accuracy gains over direct LLM planners on Natural Plan Calendar Scheduling (~12.4%uparrow), ZebraLogic (~30.8%uparrow), and SciBench Math. Crucially, under multi-task scaling-where standard planners suffer immediate performance collapse-GRASP completely flattens the multi-task degradation penalty. In interleaved dual-task environments, GRASP achieves an absolute accuracy gain of up to 16.7% over direct LLM planners. Furthermore, by isolating context and enforcing strict macro-regularization, GRASP outperforms frontier reasoning models (such as GPT-5-mini) by a margin of 14.5%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 13 of its 46 references that are in the map. First posted on arXiv 24 Sep 2026.

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13 of 46 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Magentic-UI: Towards Human-in-the-loop Agentic Systems30 Jul
  2. 2025Evaluation and Benchmarking of LLM Agents: A Survey29 Jul
  3. 2025A Survey on the Optimization of Large Language Model-based Agents16 Mar

Other references · 10

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  3. 2022A Survey on In-context Learningcited by 717
  4. 2023Faith and Fate: Limits of Transformers on Compositionalitycited by 496
  5. 2025ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoningcited by 186
  6. 2024BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Samplingcited by 120
  7. 2025PlanGenLLMs: A Modern Survey of LLM Planning Capabilitiescited by 82
  8. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 38
  9. 2025Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planningcited by 15
  10. 2026RunAgent: Interpreting Natural-Language Plans with Constraint-Guided Executioncited by 2

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  1. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change
  2. 2023On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)
  3. 2024Query-Efficient Planning with Language Models
  4. 2024Exploring and Benchmarking the Planning Capabilities of Large Language Models
  5. 2023Improving Planning with Large Language Models: A Modular Agentic Architecture
  6. 2024AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
  7. 2024Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation
  8. 2025Can LLM-Reasoning Models Replace Classical Planning? A Benchmark Study