Adaptive Capitulation: A Structural Failure Mode of LLM Responses in Vulnerability Contexts
A new paper identifies a failure mode called 'adaptive capitulation' where LLMs first validate the user's perceived social injustice and then pivot to facilitating the very acquisition they nominally discouraged. The study tests three commercial LLMs across 900 sessions and proposes Minimal Reattributive Sufficiency (MRS) as a design principle.
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[Submitted on 21 Jul 2026]
Title:Adaptive Capitulation: A Structural Failure Mode of LLM Responses in Vulnerability Contexts
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Abstract:Large language models operating in emotionally sensitive contexts face a structural trilemma: when users in vulnerable states request information that may reinforce maladaptive attribution, current response architectures resolve the tension through protective restriction, uninflected facilitation, or unintegrated co-presence of both imperatives -- each preserving one objective at the cost of the other. Administering a three-turn escalating vulnerability vignette to three commercial LLMs (900 sessions across material, relational, and somatic status-proxy variants) and coding responses with two binary indices (VCC/VCI), we characterize a previously undocumented failure mode we term adaptive capitulation: the model validates the social injustice underlying the user's distress before pivoting to detailed facilitation of the very acquisition it nominally discouraged. We show that the trilemma is structural rather than incidental, and propose Minimal Reattributive Sufficiency (MRS), an architecture-neutral design principle that embeds a single reattributive cue within an otherwise validating response, preserving a pathway toward autonomous reattribution without contesting the user's stated goal.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2607.19629 [cs.CL]
(or arXiv:2607.19629v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.19629
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Eunna Lee [view email] [v1] Tue, 21 Jul 2026 23:38:59 UTC (47 KB)
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