title: PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning
    Trajectories for Complex Problem Solving
summary: Recent agent frameworks and inference-time algorithms often struggle with
  complex planning problems due to limitations in verifying generated plans or
  reasoning and varying complexity of instances within a single task. Many
  existing methods for these tasks either perform task-level verification without
  considering constraints or apply inference-time algorithms without adapting to
  instance-level complexity. To address these limitations, we propose PlanGEN, a
  model-agnostic and easily scalable agent framework with three key components:
  constraint, verification, and selection agents. Specifically, our approach
  proposes constraint-guided iterative verification to enhance performance of
  inference-time algorithms--Best of N, Tree-of-Thought, and REBASE. In PlanGEN
  framework, the selection agent optimizes algorithm choice based on instance
  complexity, ensuring better adaptability to complex planning problems.
  Experimental results demonstrate significant improvements over the strongest
  baseline across multiple benchmarks, achieving state-of-the-art results on
  NATURAL PLAN (sim8%uparrow), OlympiadBench (sim4%uparrow), DocFinQA
  (sim7%uparrow), and GPQA (sim1%uparrow). Our key finding highlights
  that constraint-guided iterative verification improves inference-time
  algorithms, and adaptive selection further boosts performance on complex
  planning and reasoning problems.
paper_authors._id: 67be18d2bb66802239ec8095
paper_authors.name: Mihir Parmar
paper_authors._id: 67be18d2bb66802239ec8096
paper_authors.name: Xin Liu
paper_authors._id: 67be18d2bb66802239ec8097
paper_authors.name: Palash Goyal
paper_authors._id: 67be18d2bb66802239ec8098
paper_authors.name: Yanfei Chen
paper_authors._id: 67be18d2bb66802239ec8099
paper_authors.name: Long Le
paper_authors._id: 67be18d2bb66802239ec809a
paper_authors.name: Swaroop Mishra
paper_authors._id: 67be18d2bb66802239ec809b
paper_authors.name: Hossein Mobahi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-07T09:10:22.229Z
paper_authors._id: 67ca98ba4c8e16942247053f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/eAalqZR94A1jiExKQaYTB.png
paper_authors.fullname: Hossein Mobahi
paper_authors.type: user
paper_authors.user: hmobahi
paper_authors._id: 67be18d2bb66802239ec809c
paper_authors.name: Jindong Gu
paper_authors._id: 67be18d2bb66802239ec809d
paper_authors.name: Zifeng Wang
paper_authors._id: 67be18d2bb66802239ec809e
paper_authors.name: Hootan Nakhost
paper_authors._id: 67be18d2bb66802239ec809f
paper_authors.name: Chitta Baral
paper_authors._id: 67be18d2bb66802239ec80a0
paper_authors.name: Chen-Yu Lee
paper_authors._id: 67be18d2bb66802239ec80a1
paper_authors.name: Tomas Pfister
paper_authors._id: 67be18d2bb66802239ec80a2
paper_authors.name: Hamid Palangi
paper_title: PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning
    Trajectories for Complex Problem Solving
paper_summary: Recent agent frameworks and inference-time algorithms often struggle with
  complex planning problems due to limitations in verifying generated plans or
  reasoning and varying complexity of instances within a single task. Many
  existing methods for these tasks either perform task-level verification without
  considering constraints or apply inference-time algorithms without adapting to
  instance-level complexity. To address these limitations, we propose PlanGEN, a
  model-agnostic and easily scalable agent framework with three key components:
  constraint, verification, and selection agents. Specifically, our approach
  proposes constraint-guided iterative verification to enhance performance of
  inference-time algorithms--Best of N, Tree-of-Thought, and REBASE. In PlanGEN
  framework, the selection agent optimizes algorithm choice based on instance
  complexity, ensuring better adaptability to complex planning problems.
  Experimental results demonstrate significant improvements over the strongest
  baseline across multiple benchmarks, achieving state-of-the-art results on
  NATURAL PLAN (sim8%uparrow), OlympiadBench (sim4%uparrow), DocFinQA
  (sim7%uparrow), and GPQA (sim1%uparrow). Our key finding highlights
  that constraint-guided iterative verification improves inference-time
  algorithms, and adaptive selection further boosts performance on complex
  planning and reasoning problems.
paper_ai_summary: PlanGEN, a model-agnostic agent framework, uses constraint-guided iterative verification and adaptive algorithm selection to significantly improve performance on complex planning and reasoning tasks.
paper_ai_keywords: agent framework constraint-guided iterative verification selection agent Best of N Tree-of-Thought REBASE NATURAL PLAN OlympiadBench DocFinQA GPQA