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

Computer Science > Computation and Language

[Submitted on 23 Sep 2026]

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

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.

Submission history

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

Ancillary-file links:

Ancillary files (details):

- 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

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