arXiv — Machine Learning · · 4 min read

Constitutional Value Potentials: reading and steering internal priority margins in language models

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Computer Science > Machine Learning

arXiv:2606.15420 (cs)
[Submitted on 13 Jun 2026]

Title:Constitutional Value Potentials: reading and steering internal priority margins in language models

Authors:Tong Che, Rui Wu
View a PDF of the paper titled Constitutional Value Potentials: reading and steering internal priority margins in language models, by Tong Che and 1 other authors
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Abstract:A constitution tells a language model what to value, but little tells us whether it does. Adherence is judged from outputs, and output evidence is most fragile on value conflicts, where what matters is not which value a model mentions but which one it is willing to sacrifice. We provide evidence that this arbitration can be read from activations in a structured margin readout. We introduce Constitutional Value Potentials (CVP). For each value we learn a scalar potential from the hidden state: an internal pressure to preserve that value, supervised not by the prompt but by an independent judge's verdict on which value the model's own response actually preserved. The signed difference of two potentials is a priority margin. A constitutional clause becomes the claim that a margin stays positive, and a single monitor score flags when it does not. The monitor predicts conflict violations with AUROC up to 0.95, beats a strong hidden-state probe, and generalizes to held-out synthetic conflicts across three Qwen2.5 scales. The signal appears as the answer begins, from the prompt tail and first response token. Read this early, the same signal reveals whether an adversarial priority hack has actually pushed the model toward a violation, rather than only whether the prompt looks adversarial. The same directions also support intervention tests: under selected steering settings, moving along a value direction shifts judged trade-offs in the intended direction. Together, these results suggest that some constitution-relevant priorities are accessible as activation-space margins, rather than only as output behavior.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.15420 [cs.LG]
  (or arXiv:2606.15420v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2606.15420
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Tong Che [view email]
[v1] Sat, 13 Jun 2026 18:14:23 UTC (108 KB)
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