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

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

作者：Arunabh Srivastava, Mohammad A., Khojastepour, Srimat Chakradhar, Sennur Ulukus

来源：arXiv cs.AI（人工智能）

正文

Computer Science > Artificial Intelligence

arXiv:2609.30147v1 (cs)

[Submitted on 24 Sep 2026]

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

Authors:Arunabh Srivastava, Mohammad A. (Amir)Khojastepour, Srimat Chakradhar, Sennur Ulukus

View a PDF of the paper titled GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI, by Arunabh Srivastava and 2 other authors

View PDF

HTML (experimental)

Abstract: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$%$.

Comments:

Accepted at the Second Workshop for Research on Agent Language Models (REALM) at EMNLP 2026

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)

Cite as:

arXiv:2609.30147 [cs.AI]

(or

arXiv:2609.30147v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.30147

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

主题

人工智能 · 自然语言处理 · 机器学习 · 多智能体

由「前沿雷达」于 2026-09-25 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。