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

[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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Ancillary-file links:

Ancillary files (details):

CONTROLS.md

README.md

accuracy_sweep.json

anthropic_acc.json

compute_control_claude-fable-5-1.json

compute_control_claude-sonnet-5.json

compute_control_claude-sonnet-5_run1.json

compute_control_claude-sonnet-5_run2.json

compute_control_gpt-4o-mini.json

compute_control_gpt-5.6-sol.json

compute_control_gpt-5_seed1.json

compute_control_gpt-5_seed2.json

compute_control_kimi-k2.6.json

compute_control_o3-mini_seed1.json

compute_control_o3-mini_seed2.json

compute_control_qwen3.8-max.json

err_budget.py

err_budget_rows.json

gullibility_rows.json

gullibility_sweep.json

gullibility_sweep_providers.json

gullibility_sweep_v5.json

invalid_config_OUTCOME.md

invalid_config_preregistration.json

knowledge_qa_OUTCOME.md

knowledge_qa_preregistration.json

memory_answerable_classification.json

ov_accuracy_sweep.py

ov_anthropic_acc.py

ov_compute_control.py

ov_config_eval.py

ov_gullibility.py

ov_gullibility_sweep.py

ov_kqa_eval.py

ov_lead_eval.py

ov_lead_hc.py

ov_provider_sweep.py

ov_provider_sweep_v5.py

ov_rag_extract_code.py

ov_rag_read.py

rag_baseline.py

rag_extract_code.json

rag_extract_code_tonly.json

rag_read.json

split.json

table_8020.json

(41 additional files not shown)

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