The Bias Tax: From Closure Failure to Verification Overhead in Long-Context LLM Auditing

Abstract

As long-context Large Language Model (LLM) evaluation shifts from simple retrieval toward audit-like reasoning, the critical challenge is no longer merely finding relevant facts, but preserving a correct logical closure under prompt-induced pressure and understanding how generation failures propagate into downstream verification cost. We study this pipeline effect in a single 80,000-token legislative corpus using six prompting conditions---Control, Management, Chain-of-Thought (CoT), Periodic Summary, Union, and One-Shot analogy prompting---within a Reader--Judge architecture, with DeepSeek-V3 as the solver and DeepSeek-R1 as the auditor. We introduce a \textbf{Logical Needle-in-a-Haystack (L-NIHS)} stress test in which success requires traversing a five-needle chain from base rule to factual evidence and final closure while resisting a late-stage distractor. Our main result is structural: the dominant solver-side failure is not early retrieval loss, but late-stage closure failure under behavioral pressure. Across prompting conditions, early anchors remain largely recoverable, while interference rejection and closure degrade sharply once persona-congruent or prompt-congruent distractors enter the reasoning path. On the auditor side, these distorted outputs induce a shorter-but-costlier verification asymmetry---the \textbf{Bias Tax}---in which management-conditioned responses are briefer yet slower to verify than neutral ones. Judge-side reasoning traces further suggest that this added cost arises not from answer length alone, but from the need to disentangle factual closure from fabricated procedural or compensatory pathways. Finally, we show that high tone certainty is widespread across conditions and therefore cannot be treated as a reliable proxy for logical fidelity. Taken together, the results suggest that in long-context auditing, prompting strategies reshape not only answer style, but the pathway by which evidence is selected, justified, and transformed into both final decisions and downstream audit burden.

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