aiqbench analysis
This arXiv paper presents an LLM agent (SIA) that structures technical troubleshooting around competing hypotheses, counterfactual checks, and cited evidence, rather than committing early to a single explanation. Its evaluation separates final diagnostic accuracy from hypothesis coverage and robustness to misleading user input, which is useful for judging whether agentic reasoning helps or merely looks more thorough. The corpus is drawn from 303 forum threads and the user is simulated, so real-world performance remains an open question.
LLM-as-an-Investigator: Evidence-First Agentic Reasoning for Robust Interactive Problem Diagnosis
arXiv:2606.13220v2 Announce Type: replace Abstract: Large language models (LLMs) are used for technical troubleshooting, where problem descriptions are often incomplete and multiple causes can explain the same symptoms. Effective diagnosis requires acquiring discriminative evidence and selecting the explanation best supported by it. Yet reasoning or interaction alone may not prevent premature commitment to an incorrect hypothesis. We introduce LLM-as-an-Investigator, an evidence-first methodology for interactive problem diagnosis that reduces premature commitment while distinguishing competing causes. We implement it through the Solution Investigator Agent (SIA), which orchestrates three complementary reasoning behaviours through a shared investigation state: a Diagnostic Reasoner that conducts and reassesses the diagnostic interview, a Hypothesis Investigator that maintains and updates competing hypotheses, and a Counterfactual Investigator that challenges current explanations and guides discriminative follow-up questions. Final selection uses cited dialogue evidence and a separate verification step, while explicit hypothesis and evidence records make the diagnosis traceable and preserve unresolved alternatives. We construct a structured corpus from 303 technical forum threads across mechanical, electrical, and hydraulic domains. A source-grounded pipeline separates the initial problem, pre-solution clarifications, and reported outcome, while a user simulator answers diagnostic questions without access to the reference solution. We compare SIA with direct, reasoning-based, interactive, and search-based baselines, including adaptations of ReAct and Tree of Thoughts. Evaluation distinguishes final diagnostic quality from hypothesis coverage and oracle potential, while ablations and paired stress tests assess component contributions and robustness to plausible but misleading user hypotheses.
Abstract:Large language models (LLMs) are used for technical troubleshooting, where problem descriptions are often incomplete and multiple causes can explain the same symptoms. Effective diagnosis requires acquiring discriminative evidence and selecting the explanation best supported by it. Yet reasoning or interaction alone may not prevent premature commitment to an incorrect hypothesis. We introduce LLM-as-an-Investigator, an evidence-first methodology for interactive problem diagnosis that reduces premature commitment while distinguishing competing causes. We implement it through the Solution Investigator Agent (SIA), which orchestrates three complementary reasoning behaviours through a shared investigation state: a Diagnostic Reasoner that conducts and reassesses the diagnostic interview, a Hypothesis Investigator that maintains and updates competing hypotheses, and a Counterfactual Investigator that challenges current explanations and guides discriminative follow-up questions. Final selection uses cited dialogue evidence and a separate verification step, while explicit hypothesis and evidence records make the diagnosis traceable and preserve unresolved alternatives. We construct a structured corpus from 303 technical forum threads across mechanical, electrical, and hydraulic domains. A source-grounded pipeline separates the initial problem, pre-solution clarifications, and reported outcome, while a user simulator answers diagnostic questions without access to the reference solution. We compare SIA with direct, reasoning-based, interactive, and search-based baselines, including adaptations of ReAct and Tree of Thoughts. Evaluation distinguishes final diagnostic quality from hypothesis coverage and oracle potential, while ablations and paired stress tests assess component contributions and robustness to plausible but misleading user hypotheses.
Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
arXiv:2606.13220 [cs.AI]
(or arXiv:2606.13220v2 [cs.AI] for this version)
Full text under the publisher's open licence · 259 words
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