[2607.11871] Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias

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arXiv:2607.11871 (cs)

[Submitted on 13 Jul 2026]

Title:Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias

Authors:Zixiang Xu, Sixian Li, Huaxing Liu, Xiang Wang, Shuai Li, Zirui Song, Xiuying Chen<br>View a PDF of the paper titled Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias, by Zixiang Xu and 6 other authors

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Abstract:Existing studies of LLM-as-judge scoring bias work predominantly at the input-output level: they perturb inputs, measure score deltas, and propose prompt-level mitigations. We argue that the same biases admit a representation-level account in the judge's hidden state, complementary to the input-output view and operationally useful in ways it does not afford. We report three findings, across seven judges, seven bias types, and nine benchmarks. Geometry: baseline judging inputs occupy a tight activation manifold while biased inputs are displaced along a low-dimensional, type-specific subspace that sharpens with depth and is recovered consistently by three families of estimators. Causal control: steering hidden states along this subspace drives scoring in both directions, forward shifts reproducing biased scoring on clean inputs and reverse shifts restoring baseline scoring on biased ones, while matched-norm random directions produce shifts an order of magnitude smaller. Operational: a simple linear projection onto the same bias-direction features anticipates judge failures on three entirely unseen benchmarks, substantially outperforming text-based alternatives. Reading bias as activation geometry, rather than as input-output noise, unifies geometric structure, causal control, and operational prediction within a single framework. The project page is available at this https URL

Comments:<br>58 pages, 13 figures, 30 tables; project page: this https URL

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as:<br>arXiv:2607.11871 [cs.LG]

(or<br>arXiv:2607.11871v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.11871

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Zixiang Xu [view email]<br>[v1]<br>Mon, 13 Jul 2026 17:55:19 UTC (8,214 KB)

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