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Persuaded, Not Informed: Incentive-Misaligned Witnesses Defeat In-Context Grounding

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Computer Science > Computation and Language

arXiv:2609.28854 (cs)
[Submitted on 23 Sep 2026]

Title:Persuaded, Not Informed: Incentive-Misaligned Witnesses Defeat In-Context Grounding

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Abstract:Language-model agents increasingly answer questions over customer-relationship management (CRM) records, such as whether to qualify a sales lead. We identify a failure mode not addressed by a stronger model: when the context contains an assertion by a party with an incentive toward optimism - here the sales representative, a witness recorded in the CRM - the model treats the assertion as evidence and clears deals the company's own records deem unacceptable. Across 100 lead-qualification tasks from CRMArena-Pro, the representative asserts an acceptable timeline in every call and an acceptable budget in 76; on the 31 tasks where such an assertion contradicts the price list and installation policy, a model reading only the transcript clears the deal in 29 of 31 cases. The signature is consistent across seven models from four providers (misled on 87-97%); scale and explicit reasoning confer no resistance. Only 3 of 35 genuine failures involve no assertion: the failure is persuasion, not missing information. We contribute a diagnostic method rather than an architecture: (i) a bucket analysis that separates persuasion from information gaps, (ii) a same-information control showing that supplying the records to the model lowers strict accuracy from 41 to 18 while raising recall - precision collapses - and (iii) a compute-step control that holds extraction fixed and varies only who computes Budget and Timeline. The margin ranges from 42 points on an inexpensive model to 2-5 points on models that already compute correctly; on the strongest models the arms are within confidence intervals, so the pattern is a consistent direction and a soundness property, not a proved performance floor. We pre-specify a generalization test that returns a negative result, characterize the precondition (a policy exactly specified in the inputs), and release all evaluation artifacts.
Comments: 9 pages, 4 figures, IEEE conference format. Ancillary files contain the evaluation harness, pre-specifications, and per-run result files
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
ACM classes: I.2.7; H.3.3
Cite as: arXiv:2609.28854 [cs.CL]
  (or arXiv:2609.28854v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2609.28854
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Manikanta Venkata Rahul Balakavi [view email]
[v1] Wed, 23 Sep 2026 23:52:24 UTC (133 KB)
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