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

ACL ARR 2026 January Submission6700 Authors

Keywords: Long-Context Reasoning, Proactive Pitfall Avoidance Planning

Abstract: Large language models 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 errors 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.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: Language Modeling, LLM/AI Agents, Planning in Agents, Multi-Agent Systems

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 6700