PlanGEN
PlanGEN is a multi-agent framework designed to enhance planning and reasoning in LLMs through constraint-guided iterative verification and adaptive algorithm selection. Key insights include:
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Constraint-Guided Verification for Planning – PlanGEN integrates three agents: (1) a constraint agent that extracts problem-specific constraints, (2) a verification agent that evaluates plan quality and assigns scores, and (3) a selection agent that dynamically chooses the best inference algorithm based on instance complexity.
Improving Inference-Time Algorithms – PlanGEN enhances existing reasoning frameworks like Best of N, Tree-of-Thought (ToT), and REBASE by iteratively refining outputs through constraint validation.
Adaptive Algorithm Selection – Using a modified Upper Confidence Bound (UCB) policy, the selection agent optimally assigns problem instances to inference algorithms based on performance history and complexity.
State-of-the-Art Performance – PlanGEN achieves +8% improvement on NATURAL PLAN, +4% on OlympiadBench, +7% on DocFinQA, and +1% on GPQA, surpassing standard multi-agent baselines.