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[Submitted on 23 Sep 2026] Title:Persuaded, Not Informed: Incentive-Misaligned Witnesses Defeat In-Context Grounding View a PDF of the paper titled Persuaded, Not Informed: Incentive-Misaligned Witnesses Defeat In-Context Grounding, by Rahul Balakavi View PDF HTML (experimental) 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) Full-text links: Access Paper: View a PDF of the paper titled Persuaded, Not Informed: Incentive-Misaligned Witnesses Defeat In-Context Grounding, by Rahul Balakavi View PDF HTML (experimental) TeX Source view license 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) Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... 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