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arxiv: 2606.13220 · v1 · pith:GVNWFQHEnew · submitted 2026-06-11 · 💻 cs.AI · cs.CE· cs.ET· cs.LG· cs.MA

LLM-as-an-Investigator: Evidence-First Reasoning for Robust Interactive Problem Diagnosis

Fabrizio Marozzo , Pietro Li\`o This is my paper

Pith reviewed 2026-06-27 06:57 UTC · model grok-4.3

classification 💻 cs.AI cs.CEcs.ETcs.LGcs.MA
keywords LLM agentsinteractive problem diagnosisevidence-based reasoninguser-driven sycophancyhypothesis testingtechnical troubleshootingagentic AIconversational bias
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The pith

An evidence-first LLM agent improves technical diagnosis accuracy by gathering targeted evidence before committing to any user-suggested explanation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard LLMs often accept a user's initial, incomplete description of a technical problem and propose solutions without checking alternatives. The paper introduces an investigator agent that first measures how ambiguous the description is, lists multiple candidate explanations, and then asks clarifying questions while updating the probability of each hypothesis after every reply. The process stops only when evidence clearly favors one explanation over the others. Evaluation on a benchmark of real solved forum threads in mechanical, electrical, and hydraulic domains shows higher accuracy than direct prompting or reasoning-only models, and lower alignment with deliberately misleading user statements.

Core claim

The Solution Investigator Agent estimates the ambiguity of an initial problem description, generates candidate hypotheses, asks targeted clarification questions, and updates hypothesis probabilities after each answer, continuing until the evidence makes one candidate stronger than the alternatives.

What carries the argument

The Solution Investigator Agent, which maintains and refines a set of hypotheses through iterative, evidence-driven questioning rather than immediate response generation.

If this is right

  • The agent recovers the correct diagnosis more often than direct prompting or reasoning-only baselines across LLM backbones.
  • The evidence-first protocol lowers the rate at which the model adopts a plausible but incorrect user hypothesis.
  • The three-agent pipeline converts solved forum threads into repeatable test cases for interactive diagnosis.
  • Performance gains appear in mechanical, electrical, and hydraulic troubleshooting domains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same questioning loop could be applied to domains such as medical symptom diagnosis where premature conclusions are costly.
  • Adding external tool calls to verify answers during the probability update step would strengthen the evidence collection process.
  • The method points toward a reusable template for any LLM task that involves distinguishing between competing explanations under user input.

Load-bearing premise

The Ground-Truth Evaluator Agent can faithfully simulate real user responses in the three-agent pipeline while hiding the known solution without introducing evaluation bias.

What would settle it

Replace the Ground-Truth Evaluator with actual human users who know the true solution and measure whether the accuracy advantage over baselines disappears.

Figures

Figures reproduced from arXiv: 2606.13220 by Fabrizio Marozzo, Pietro Li\`o.

Figure 1
Figure 1. Figure 1: Methodological framework of the proposed Solution Investigator Agent. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Three-agent evaluation pipeline. The Problem-Solution Extractor Agent converts [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
read the original abstract

Large language models (LLMs) are increasingly used as interactive assistants for technical problem solving. However, when users provide incomplete descriptions or plausible but unverified explanations, LLMs may prematurely align with these assumptions and propose solutions before collecting sufficient evidence. We refer to this behavior as user-driven sycophancy: the tendency of an LLM to reinforce a user-provided hypothesis instead of testing alternative explanations. This paper introduces LLM-as-an-Investigator, an evidence-first agentic AI methodology for robust problem diagnosis. The approach is implemented through a Solution Investigator Agent, which estimates the ambiguity of an initial problem description, generates candidate hypotheses, asks targeted clarification questions, and updates hypothesis probabilities after each answer. Rather than producing an immediate response, the agent continues the investigation until the evidence makes one candidate explanation stronger than the alternatives. To evaluate the approach, we build a benchmark from solved technical forum threads in mechanical, electrical, and hydraulic domains. We use a three-agent evaluation pipeline in which a Problem-Solution Extractor Agent converts solved threads into structured cases, a Ground-Truth Evaluator Agent simulates the user while hiding the known solution, and the tested assistant attempts to recover the solution through dialogue. The experiments compare standard assistants, reasoning-oriented LLMs, and the proposed investigator-based model across LLM backbones. In addition to diagnostic accuracy, we analyze how standard assistants follow misleading user hypotheses in diagnostic cases. The results show that the proposed approach identifies the problem more accurately than direct prompting and reasoning-only baselines, while its evidence-first protocol helps reduce user-induced conversational bias.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces LLM-as-an-Investigator, an evidence-first agentic methodology for robust interactive technical problem diagnosis. A Solution Investigator Agent estimates ambiguity in the initial user description, generates candidate hypotheses, poses targeted clarification questions, and iteratively updates hypothesis probabilities until the evidence sufficiently favors one explanation over alternatives. The approach is evaluated on a benchmark derived from solved forum threads in mechanical, electrical, and hydraulic domains via a three-agent pipeline (Problem-Solution Extractor, Ground-Truth Evaluator simulating the user while hiding the known solution, and the tested assistant). The authors claim higher diagnostic accuracy than direct-prompting and reasoning-only baselines together with reduced susceptibility to user-induced conversational bias.

Significance. If the empirical claims hold after validation of the simulator, the work supplies a concrete protocol for mitigating user-driven sycophancy in LLM assistants for technical troubleshooting. The construction of an evaluation benchmark from real solved threads is a constructive step toward reproducible assessment of interactive diagnostic agents.

major comments (2)
  1. [Evaluation (three-agent pipeline)] Evaluation section (three-agent pipeline description): The accuracy and bias-reduction claims rest entirely on the Ground-Truth Evaluator Agent producing responses that are (a) consistent with the hidden ground-truth solution and (b) representative of real forum-user behavior. The manuscript provides no external validation, human-subject comparison, or distributional analysis demonstrating that the simulated dialogues match actual user reply patterns; any systematic deviation would directly inflate reported gains.
  2. [Abstract and Experiments] Abstract and Experiments: The abstract states that 'the results show' superior accuracy, yet supplies no quantitative metrics, benchmark sizes, statistical significance tests, or per-domain breakdowns. Without these data the central empirical claim cannot be verified against the stated baselines.
minor comments (2)
  1. [Method] Clarify the exact probabilistic update rule (Bayesian or otherwise) used by the investigator after each user answer; the current description mentions 'updates hypothesis probabilities' without an equation or pseudocode.
  2. [Method] Add a short discussion of how the ambiguity estimator is implemented and whether it is itself learned or heuristic.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important aspects of our evaluation methodology and presentation of results. We respond to each major comment below and indicate the revisions we will incorporate.

read point-by-point responses

  1. Referee: [Evaluation (three-agent pipeline)] Evaluation section (three-agent pipeline description): The accuracy and bias-reduction claims rest entirely on the Ground-Truth Evaluator Agent producing responses that are (a) consistent with the hidden ground-truth solution and (b) representative of real forum-user behavior. The manuscript provides no external validation, human-subject comparison, or distributional analysis demonstrating that the simulated dialogues match actual user reply patterns; any systematic deviation would directly inflate reported gains.

    Authors: We agree that the absence of external validation for the Ground-Truth Evaluator is a substantive limitation. The evaluator is constructed to remain consistent with the hidden solution by design, but we did not conduct human-subject studies or distributional comparisons against real forum users. In the revised manuscript we will add an explicit limitations subsection that discusses this assumption, reports basic statistics on simulated dialogue characteristics (e.g., turn counts and response patterns), and notes the risk of simulator-induced bias. This addition will make the evaluation assumptions transparent without requiring new human experiments. revision: yes

  2. Referee: [Abstract and Experiments] Abstract and Experiments: The abstract states that 'the results show' superior accuracy, yet supplies no quantitative metrics, benchmark sizes, statistical significance tests, or per-domain breakdowns. Without these data the central empirical claim cannot be verified against the stated baselines.

    Authors: The experiments section contains the requested quantitative details, including benchmark sizes per domain and accuracy comparisons to baselines. To make the abstract self-contained and verifiable, we will revise it to include the key numerical results (overall and per-domain accuracies), benchmark sizes, and any statistical significance tests that were performed. revision: yes

Circularity Check

0 steps flagged

No significant circularity; evaluation pipeline is externally anchored

full rationale

The paper constructs its benchmark from real solved forum threads via a three-agent pipeline (Problem-Solution Extractor, Ground-Truth Evaluator hiding the solution, and tested assistant). Diagnostic accuracy and bias-reduction claims are measured against this benchmark rather than being derived from any internal equations, fitted parameters, or self-referential definitions. No self-citations appear as load-bearing premises, no ansatzes are smuggled, and no result is renamed or forced by construction. The Ground-Truth Evaluator's simulation role is an explicit design choice whose fidelity is an external validity question, not a circular reduction of the reported gains to the method's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no information on free parameters, axioms, or invented entities used in the method or evaluation.

pith-pipeline@v0.9.1-grok · 5824 in / 1059 out tokens · 28264 ms · 2026-06-27T06:57:03.117136+00:00 · methodology

discussion (0)

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Reference graph

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