LLM-as-an-Investigator tackles sycophancy in AI problem diagnosis
A new evidence-first agentic methodology called LLM-as-an-Investigator uses a Solution Investigator Agent to iteratively gather evidence and update hypothesis probabilities before committing to a diagnosis, outperforming direct prompting and reasoning-only baselines on a new technical benchmark.
Score breakdown
The evidence-first protocol directly reduces the conversational bias that causes standard LLM assistants to follow misleading user hypotheses, improving diagnostic accuracy over both direct prompting and reasoning-only baselines across multiple LLM backbones.
- 01The paper coins the term "user-driven sycophancy" to describe LLMs prematurely aligning with unverified user hypotheses.
- 02The Solution Investigator Agent estimates ambiguity, generates candidate hypotheses, asks targeted clarification questions, and updates hypothesis probabilities iteratively.
- 03The agent continues its investigation until one candidate explanation is stronger than the alternatives — rather than responding immediately.
The paper identifies a failure mode in LLM-based technical assistants called "user-driven sycophancy": when users provide incomplete descriptions or plausible but unverified explanations, LLMs tend to reinforce those assumptions and propose solutions before gathering sufficient evidence. To address this, the authors propose LLM-as-an-Investigator, an evidence-first agentic AI methodology implemented through a Solution Investigator Agent. Rather than generating an immediate response, the agent estimates the ambiguity of the initial problem description, generates candidate hypotheses, poses targeted clarification questions, and updates hypothesis probabilities after each user answer — continuing the investigation until one candidate explanation becomes stronger than the alternatives.
To evaluate the methodology, the authors construct a benchmark from solved technical forum threads across mechanical, electrical, and hydraulic domains.
To evaluate the methodology, the authors construct a benchmark from solved technical forum threads across mechanical, electrical, and hydraulic domains. A three-agent evaluation pipeline is used: a Problem-Solution Extractor Agent converts solved threads into structured cases, a Ground-Truth Evaluator Agent simulates the user while concealing the known solution, and the assistant under test attempts to recover the correct diagnosis through dialogue. Experiments compare standard assistants, reasoning-oriented LLMs, and the proposed investigator-based model across multiple LLM backbones. The results demonstrate that the evidence-first protocol improves diagnostic accuracy over direct prompting and reasoning-only baselines, and measurably reduces the degree to which misleading user hypotheses bias the assistant's conclusions.
Key facts
- 01The paper coins the term "user-driven sycophancy" to describe LLMs prematurely aligning with unverified user hypotheses.
- 02The Solution Investigator Agent estimates ambiguity, generates candidate hypotheses, asks targeted clarification questions, and updates hypothesis probabilities iteratively.
- 03The agent continues its investigation until one candidate explanation is stronger than the alternatives — rather than responding immediately.
- 04A benchmark is built from solved technical forum threads in mechanical, electrical, and hydraulic domains.
- 05Evaluation uses a three-agent pipeline: a Problem-Solution Extractor Agent, a Ground-Truth Evaluator Agent (simulating the user), and the assistant under test.
- 06The investigator-based model outperforms both standard assistants and reasoning-only LLM baselines on diagnostic accuracy.
- 07The evidence-first protocol reduces user-induced conversational bias compared to direct prompting approaches.
Topics
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