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Adaptive Capitulation: A Structural Failure Mode of LLM Responses in Vulnerability Contexts

A new study from arXiv identifies a structural trilemma in large language models responding to emotionally vulnerable users, where models either protectively restrict, uninflectedly facilitate, or unintegratedly co-present both imperatives. Testing three commercial LLMs across 900 sessions, researchers characterize a previously undocumented failure mode called adaptive capitulation, where the model validates social injustice before facilitating the very acquisition it nominally discouraged. The paper proposes Minimal Reattributive Sufficiency (MRS), an architecture-neutral design principle embedding a single reattributive cue within a validating response.

read1 min views1 publishedJul 23, 2026

arXiv:2607.19629v1 Announce Type: new 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.

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