mlsanityData to 14 Oct 2026

IFPV: An Integrated Multi-Agent Framework for Generative Operational Planning and High-Fidelity Plan Verification

arXiv 2605.14851 ↗First posted 14 May 2026cs.MA cs.AIcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Operational plan generation and verification are critical for modern complex and rapidly changing battlefield environments, yet traditional generation and verification methods still respectively face the challenges of generation infeasibility and verification insufficiency. To alleviate these limitations, we propose an Integrated Multi-Agent Framework for Generative Operational Planning and High-Fidelity Plan Verification (IFPV). IFPV consists of two tightly coupled modules: Multi-Perspective Hierarchical Agents (MPHA) for generative operational planning and an Adversarial Cognitive Simulation Engine (ACSE) for high-fidelity adversarial plan verification. MPHA decomposes commander intent into executable multi-platform tactical action sequences through the collaboration of Pathfinder, Analyst, and Planner agents. ACSE introduces an opponent equipped with a customized world model, which predicts the future evolution of mission-critical platforms and conducts dynamic counteractions against candidate plans. Simulation experiments in the Asymmetric Combat Tactic Simulator (ACTS) show that IFPV improves mission success by 19.4% and reduces operational cost by 41.7% compared with a single-step large language model (LLM) planning baseline. Compared with a traditional rule-based validator, ACSE increases the average suppression rate by 31.8%, indicating that the proposed verification environment is stricter and more discriminative in revealing the latent vulnerabilities of candidate plans. The code for IFPV can be found at https://github.com/zhigao3ks/IFPV.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 21 of its 35 references that are in the map. First posted on arXiv 14 May 2026.

Builds on

21 of 35 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Understanding the planning of LLM agents: A survey5 Feb
  2. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges21 Jan

Other references · 19

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  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  5. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
  6. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  7. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  8. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  9. 2023Mastering Diverse Domains through World Modelscited by 1,253
  10. 2018Recurrent World Models Facilitate Policy Evolutioncited by 785
  11. 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 637
  12. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 382
7 more
  1. 2024Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agentscited by 154
  2. 2024Can Large Language Models Reason and Plan?cited by 82
  3. 2024BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysiscited by 20
  4. 2024Improving Large Models with Small models: Lower Costs and Better Performancecited by 14
  5. 2024Logic Distillation: Learning from Code Function by Function for Decision-making Taskscited by 5
  6. 2025KKA: Improving Vision Anomaly Detection through Anomaly-related Knowledge from Large Language Modelscited by 1
  7. 2025Easy Adaptation: An Efficient Task-Specific Knowledge Injection Method for Large Models in Resource-Constrained Environmentscited by 1

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