AI RESEARCH
A Fano-Style Accuracy Upper Bound for LLM Single-Pass Reasoning in Multi-Hop QA
arXiv CS.AI
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ArXi:2509.21199v3 Announce Type: replace Multi-Hop Question Answering (MHQA) requires integrating dispersed, interdependent evidence through sequential reasoning under noise. This task is challenging for LLMs as they have a finite per-pass output capacity, beyond which the integration of task-relevant evidence proves unreliable. Consequently, the single-pass reasoning paradigm is inherently vulnerable to this capacity overflow. To formalize this bottleneck, our analysis establishes a Fano-style accuracy upper bound, defining a theoretical performance ceiling for single-pass LLMs.