PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning
  Trajectories for Complex Problem Solving
Authors: name: Mihir Parmar; name: Xin Liu; name: Palash Goyal; name: Yanfei Chen; name: Long Le; name: Swaroop Mishra; name: Hossein Mobahi; name: Jindong Gu; name: Zifeng Wang; name: Hootan Nakhost; name: Chitta Baral; name: Chen-Yu Lee; name: Tomas Pfister; name: Hamid Palangi
Published: 2025-02-22 06:21:56

Abstract: 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.