PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning
  Trajectories for Complex Problem Solving
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.
67be18d2bb66802239ec8095
Mihir Parmar
67be18d2bb66802239ec8096
Xin Liu
67be18d2bb66802239ec8097
Palash Goyal
67be18d2bb66802239ec8098
Yanfei Chen
67be18d2bb66802239ec8099
Long Le
67be18d2bb66802239ec809a
Swaroop Mishra
67be18d2bb66802239ec809b
Hossein Mobahi
claimed_verified
2025-03-07T09:10:22.229Z
67ca98ba4c8e16942247053f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/eAalqZR94A1jiExKQaYTB.png
Hossein Mobahi
user
hmobahi
67be18d2bb66802239ec809c
Jindong Gu
67be18d2bb66802239ec809d
Zifeng Wang
67be18d2bb66802239ec809e
Hootan Nakhost
67be18d2bb66802239ec809f
Chitta Baral
67be18d2bb66802239ec80a0
Chen-Yu Lee
67be18d2bb66802239ec80a1
Tomas Pfister
67be18d2bb66802239ec80a2
Hamid Palangi
PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning
  Trajectories for Complex Problem Solving
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.
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.
agent framework constraint-guided iterative verification selection agent Best of N Tree-of-Thought REBASE NATURAL PLAN OlympiadBench DocFinQA GPQA