What question did this study set out to answer?

This research aims to examine how long-context LLM auditing affects logical closure and verification burdens.

March 31, 2026Open Access

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

Key Points

This research aims to examine how long-context LLM auditing affects logical closure and verification burdens.
Analyzed an 80,000-token legislative corpus under various prompting conditions.
Employed a Reader–Judge architecture with DeepSeek-V3 and DeepSeek-R1.
Introduced a Logical Needle-in-a-Haystack stress test to evaluate reasoning paths and closure integrity.
Found that late-stage closure failure is the dominant issue, not early retrieval loss.
Noted that the introduction of distractors sharply degrades closure effectiveness.
Discovered that persona conditioning leads to shorter outputs but higher verification time for auditors.

Abstract

This record contains the preprint version of the paper: "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 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 Bias Tax: persona conditioning increases downstream verification burden, and in the main DeepSeek-V3 setting this takes a particularly striking form—persona-conditioned responses are shorter, yet each token costs the auditor significantly more time to verify. Finally, we show that high tone certainty is widespread across conditions and therefore cannot be treated as a reliable proxy for logical fidelity. Status: Preprint / unpublished manuscript.

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