PPA-Plan: Proactive Pitfall Avoidance for Reliable Planning in Long-Context LLM Reasoning

TL;DR

PPA-Plan introduces a proactive planning method for long-context reasoning in LLMs that prevents logical pitfalls before plan generation, leading to improved performance over existing methods.

Contribution

It proposes a novel proactive strategy that identifies and avoids logical pitfalls during plan generation, enhancing reasoning reliability in long-context tasks.

Findings

PPA-Plan outperforms existing plan-and-execute methods on long-context QA benchmarks.

Proactive constraint-based planning reduces logical errors in generated plans.

Experiments demonstrate improved reasoning accuracy with PPA-Plan.

Abstract

Large language models (LLMs) struggle with reasoning over long contexts where relevant information is sparsely distributed. Although plan-and-execute frameworks mitigate this by decomposing tasks into planning and execution, their effectiveness is often limited by unreliable plan generation due to dependence on surface-level cues. Consequently, plans may be based on incorrect assumptions, and once a plan is formed, identifying what went wrong and revising it reliably becomes difficult, limiting the effectiveness of reactive refinement. To address this limitation, we propose PPA-Plan, a proactive planning strategy for long-context reasoning that focuses on preventing such failures before plan generation. PPA-Plan identifies potential logical pitfalls and false assumptions, formulates them as negative constraints, and conditions plan generation on explicitly avoiding these constraints. Experiments on long-context QA benchmarks show that executing plans generated by PPA-Plan consistently outperforms existing plan-and-execute methods and direct prompting.