Who May Overrule the Agent? Evidence-Gated Authority in LLM Review Institutions

arXiv:2611.05390v1 Announce Type: new Abstract: Multi-agent language-model systems often add a reviewer to improve an upstream agent's decision. Yet a reviewer can both repair errors and replace decisions that were already correct. We study this as a problem of institutional jurisdiction: conditional on the same proposal, task information, model, and review output, when is the reviewer authorized to make its recommendation binding? We compare broad override authority with an evidence gate that permits replacement only after the reviewer produces a mechanically valid record of a binding-constraint violation and a compliant repair. Across two locally run open-weight models, 96 spatial-policy tasks, and a 96-task portfolio-transfer benchmark, the gate sharply reduces corruption of correct proposals while retaining most repairs of violations. The directions of this tradeoff are partly implied by the rule; its magnitude depends on reviewer transitions and upstream proposal states. A prospective validation then gives the reviewer an explicit choice to retain, replace, or escalate. This eliminates correct-proposal corruption for Qwen, but Mistral still replaces all 48 correct proposals; Qwen also declines every welfare-only repair that Mistral frequently attempts. Thus interface design changes reviewer behavior but is not a model-independent safeguard. The result is not that restricted review is universally better. It is that reviewer competence, action interface, and binding jurisdiction jointly determine both the correction domain and the corruption surface.

The study presents a neutral, technical evaluation of reviewer authority in multi-agent language-model systems.

The study identifies techniques to reduce wrongful overrides in multi-agent AI review systems, improving decision accuracy. While this can enhance AI oversight reliability, residual risks remain due to reviewer behavior variability and interface dependence. The net societal impact is modestly positive but limited, depending on implementation context and agent competence.

This is narrow technical research on institutional design for multi-agent LLM review, with controlled benchmarks showing mixed, model-dependent results. It has mild relevance to AI safety engineering but no direct claim about societal-scale human flourishing impact either way.

The research investigates how to improve multi-agent AI systems by optimizing reviewer authority. It shows a specific mechanism (evidence gate) can reduce erroneous overrides by AI reviewers while maintaining error correction. This is a technical finding with potentially minor positive societal implications for AI system reliability.

The text discusses potential issues with multi-agent language-model systems. It highlights that reviewers can both correct errors and override correct decisions. The evidence gate reduces corruption of correct proposals but does not eliminate it entirely. This suggests a concerning implication for the reliability and trustworthiness of AI systems in decision-making roles.

SOURCE     arXiv cs.AI (tier 1)
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DOCUMENT   f0caa981-b036-478c-a00f-b05e72905da8
           https://arxiv.org/abs/2611.05390
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -44  (Adverse)
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CONFIDENCE 60%