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GRASP: Generating, Revising, and Assessing for Stra... | AI Research

Key Takeaways

  • GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI Large Language Models (LLMs) often struggle to maintain reliability as task...
  • 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 $\textbf{GRASP}$, a strategy-aware, multi-stage planning framework.
  • 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.
Paper AbstractExpand

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 $\textbf{GRASP}$, 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 ($\sim$12.4$\%$$\uparrow$), ZebraLogic ($\sim$30.8$\%$$\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$\%$.

GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI
Large Language Models (LLMs) often struggle to maintain reliability as tasks become more complex, frequently suffering from "attention fatigue" or logical errors when forced to handle long, multi-step instructions. This paper introduces GRASP, a framework designed to improve the quality of natural language plans by breaking the planning process into specialized, isolated modules. By separating the generation of global guidelines from the exploration of specific strategies and the final verification of results, GRASP prevents the compounding errors that typically plague standard, monolithic planning approaches. The ai agents story also surfaces in OpenAI Unveils GPT-Red an Automated Model..., adding another angle.

A Modular Approach to Planning

GRASP functions through three distinct, sequential modules that mimic human cognitive processes: identification, reasoning, and judgment.

  • GenPlan: This module creates a structural blueprint for a task. It uses a knowledge base to track hard constraints and soft guidelines, ensuring the plan remains logically sound before it ever considers specific task details.

  • RevPlan: Once the blueprint is set, this module adapts it to a specific task instance. It explores multiple potential strategies in parallel, isolated context windows, preventing the model from prematurely committing to a single, potentially flawed path.

  • VerPlan: The final module acts as an independent evaluator. It scores the candidate plans generated by RevPlan against the original constraints and task requirements, selecting the most reliable option for execution.

Eliminating Contextual Overload

A primary challenge in AI planning is "context pollution," where a model tries to track global rules, local variables, and step-by-step actions all at once. GRASP solves this by enforcing strict context isolation. Because each module operates in its own window, the model is not overwhelmed by extraneous information. This separation ensures that if one strategy path fails or hallucinates, it does not contaminate the other potential solutions, allowing the system to maintain high performance even as task complexity increases. To see openai in practice, Note-Taking is Dead walks through a concrete example.

Performance and Scalability

Empirical testing shows that GRASP significantly outperforms standard LLM planners across diverse benchmarks, including calendar scheduling, logic puzzles, and scientific math problems. Notably, while standard planners often collapse under the pressure of multi-task scaling, GRASP effectively flattens this degradation penalty. In tests involving interleaved dual-task environments, GRASP achieved an absolute accuracy gain of up to 16.7% over direct LLM baselines and outperformed frontier reasoning models like GPT-5-mini by 14.5%. These results suggest that modular, strategy-aware planning is a robust solution for maintaining accuracy in complex, real-world agentic workflows. The ai agents story also surfaces in OpenAI Says AI Found Possible Navier–Stokes..., adding another angle. as detailed in the full paper on Arxiv

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