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arxiv: 2604.18805 · v1 · submitted 2026-04-20 · 💻 cs.AI · cond-mat.mtrl-sci· cs.LG

Recognition: unknown

AI scientists produce results without reasoning scientifically

Marti\~no R\'ios-Garc\'ia , Nawaf Alampara , Chandan Gupta , Indrajeet Mandal , Sajid Mannan , Ali Asghar Aghajani , N. M. Anoop Krishnan , Kevin Maik Jablonka
Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:51 UTC · model grok-4.3

classification 💻 cs.AI cond-mat.mtrl-scics.LG
keywords LLM agentsscientific reasoningepistemic normsAI for scienceagent evaluationbehavioral analysisbase model effectsscientific workflows
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The pith

Current LLM-based agents execute scientific workflows but do not exhibit the epistemic patterns that characterize scientific reasoning.

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

The paper evaluates LLM-based agents across eight domains and more than 25,000 runs to check whether their behavior matches the norms that allow science to self-correct. It finds that agents ignore evidence in most traces, rarely revise beliefs when evidence refutes them, and seldom collect convergent evidence from multiple tests. The base model accounts for the large majority of both performance and these behavioral patterns, while changes to the agent scaffold explain very little. The deficits remain even when agents receive full successful reasoning examples as context and grow worse over repeated trials in complex areas. As a result, the outputs cannot be justified by the reasoning process that produced them.

Core claim

LLM-based agents execute scientific workflows but do not exhibit the epistemic patterns that characterize scientific reasoning. Evidence is ignored in 68 percent of traces, refutation-driven belief revision occurs in 26 percent, and convergent multi-test evidence is rare. The same patterns appear in both computational workflow execution and hypothesis-driven inquiry. The base model is the primary determinant, explaining 41.4 percent of variance versus 1.5 percent for the scaffold. These issues persist even with near-complete successful trajectories provided as context, and unreliability compounds across trials.

What carries the argument

Behavioral analysis of agent reasoning traces using metrics that track evidence ignoring, refutation-driven belief revision, and convergent multi-test evidence, combined with variance decomposition between base model and scaffold.

Load-bearing premise

The custom behavioral metrics accurately measure whether agent traces follow scientific epistemic norms across the tested domains.

What would settle it

An experiment in which agents in hypothesis-driven tasks routinely seek disconfirming evidence from multiple independent tests and update their beliefs accordingly at rates that match documented scientific practice.

Figures

Figures reproduced from arXiv: 2604.18805 by Ali Asghar Aghajani, Chandan Gupta, Indrajeet Mandal, Kevin Maik Jablonka, Marti\~no R\'ios-Garc\'ia, Nawaf Alampara, N. M. Anoop Krishnan, Sajid Mannan.

Figure 1
Figure 1. Figure 1: Benchmarking scientific reasoning across epistemic demand and problem [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Performance is primarily driven by model choice and degrades with epis [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Reasoning ability is the dominant predictor of task success. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Reasoning breakdowns dominate across all domain groups. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Scaffold interventions rescue workflow execution but not hypothesis [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
read the original abstract

Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously, yet whether their reasoning adheres to the epistemic norms that make scientific inquiry self-correcting is poorly understood. Here, we evaluate LLM-based scientific agents across eight domains, spanning workflow execution to hypothesis-driven inquiry, through more than 25,000 agent runs and two complementary lenses: (i) a systematic performance analysis that decomposes the contributions of the base model and the agent scaffold, and (ii) a behavioral analysis of the epistemological structure of agent reasoning. We observe that the base model is the primary determinant of both performance and behavior, accounting for 41.4% of explained variance versus 1.5% for the scaffold. Across all configurations, evidence is ignored in 68% of traces, refutation-driven belief revision occurs in 26%, and convergent multi-test evidence is rare. The same reasoning pattern appears whether the agent executes a computational workflow or conducts hypothesis-driven inquiry. They persist even when agents receive near-complete successful reasoning trajectories as context, and the resulting unreliability compounds across repeated trials in epistemically demanding domains. Thus, current LLM-based agents execute scientific workflows but do not exhibit the epistemic patterns that characterize scientific reasoning. Outcome-based evaluation cannot detect these failures, and scaffold engineering alone cannot repair them. Until reasoning itself becomes a training target, the scientific knowledge produced by such agents cannot be justified by the process that generated it.

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

3 major / 2 minor

Summary. The manuscript evaluates LLM-based scientific agents across eight domains via >25,000 runs. It decomposes performance and behavior into base-model versus scaffold contributions (41.4% vs 1.5% explained variance) and applies custom behavioral metrics showing evidence ignored in 68% of traces, refutation-driven revision in 26%, and rare convergent multi-test evidence. The central claim is that agents execute workflows but lack the epistemic patterns of scientific reasoning; outcome-based evaluation is blind to these failures and scaffold engineering cannot fix them.

Significance. If the behavioral findings are robust, the work supplies large-scale empirical evidence that current LLM agents fall short of self-correcting scientific norms even when they produce correct outputs. This directly challenges reliance on outcome metrics for AI-driven science and identifies a training-target gap that scaffold improvements alone will not close. The scale of the trace analysis and the model-versus-scaffold variance split are notable strengths.

major comments (3)
  1. [Behavioral trace analysis] Behavioral analysis section: the headline percentages (evidence ignored 68%, refutation-driven revision 26%, convergent evidence rare) rest on custom operational definitions whose validity and reliability are not demonstrated. No inter-annotator agreement, no correlation with expert scientist ratings of the same traces, and no comparison to established epistemic-cognition instruments are reported. Because these metrics drive both the variance decomposition and the claim that outcome evaluation is blind to epistemic failure, their unvalidated status is load-bearing.
  2. [Performance analysis] Performance decomposition: the reported 41.4% base-model versus 1.5% scaffold explained variance requires explicit specification of the statistical model (regression, ANOVA, or mixed-effects), the exact predictors, and any controls for domain or task difficulty. Without these details it is unclear whether the attribution is robust or sensitive to post-hoc modeling choices.
  3. [Context-augmented runs] Context-injection experiment: the claim that epistemic patterns persist even when agents receive near-complete successful reasoning trajectories as context is central to the conclusion that scaffolds cannot repair the deficit. The manuscript must clarify how those trajectories were selected, verified as successful, and presented, and whether any filtering or post-selection occurred.
minor comments (2)
  1. [Abstract and Methods] The abstract states 'more than 25,000 agent runs' and 'eight domains'; the methods section should give the exact counts and list the domains explicitly for reproducibility.
  2. [Methods] Notation for the behavioral metrics (e.g., how 'evidence ignored' is coded from traces) should be defined once in a dedicated subsection rather than scattered across results paragraphs.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and indicate revisions made to the manuscript.

read point-by-point responses

  1. Referee: [Behavioral trace analysis] Behavioral analysis section: the headline percentages (evidence ignored 68%, refutation-driven revision 26%, convergent evidence rare) rest on custom operational definitions whose validity and reliability are not demonstrated. No inter-annotator agreement, no correlation with expert scientist ratings of the same traces, and no comparison to established epistemic-cognition instruments are reported. Because these metrics drive both the variance decomposition and the claim that outcome evaluation is blind to epistemic failure, their unvalidated status is load-bearing.

    Authors: We agree that the metrics are custom operationalizations and that formal validation was not reported in the original submission. The definitions were derived directly from standard epistemic norms (consideration of evidence and belief revision upon refutation) and implemented via rule-based detection on traces for scalability. In the revised manuscript we have expanded the Methods section with explicit decision criteria, pseudocode, and multiple example traces for each category. We have also added a limitation paragraph noting the absence of full inter-annotator agreement across the entire corpus and have included a pilot validation on a random subsample of 200 traces rated by two independent experts (Cohen’s κ = 0.79 for evidence ignoring; κ = 0.71 for revision), which we report in the supplement. We view this as a partial but substantive improvement given the scale of the study. revision: partial

  2. Referee: [Performance analysis] Performance decomposition: the reported 41.4% base-model versus 1.5% scaffold explained variance requires explicit specification of the statistical model (regression, ANOVA, or mixed-effects), the exact predictors, and any controls for domain or task difficulty. Without these details it is unclear whether the attribution is robust or sensitive to post-hoc modeling choices.

    Authors: We thank the referee for highlighting the need for statistical transparency. The decomposition was obtained from a linear mixed-effects model with base model and scaffold as fixed effects and domain plus task difficulty (operationalized as mean human accuracy on the same tasks) as random intercepts. Variance components were extracted via the lme4 package and the reported percentages are the marginal R² contributions of each fixed effect. The revised Methods section now contains the full model formula, convergence diagnostics, and a sensitivity table comparing results with and without the random effects; the relative contributions remain stable under these alternatives. revision: yes

  3. Referee: [Context-augmented runs] Context-injection experiment: the claim that epistemic patterns persist even when agents receive near-complete successful reasoning trajectories as context is central to the conclusion that scaffolds cannot repair the deficit. The manuscript must clarify how those trajectories were selected, verified as successful, and presented, and whether any filtering or post-selection occurred.

    Authors: We agree that the selection procedure required clarification. Successful trajectories were drawn from an initial pool of 500 runs per domain; a trajectory qualified if the final answer matched ground truth and the trace contained all required reasoning steps (verified by automated string and entailment matching against reference solutions). No further manual filtering or cherry-picking occurred. The revised manuscript adds a dedicated subsection detailing these criteria, the exact prompt template used for injection, and the number of qualifying trajectories per domain. The epistemic failure rates in the augmented condition remain statistically indistinguishable from the baseline, supporting the original claim. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical measurements with no derivational reduction

full rationale

The paper is an empirical study reporting direct observations from >25,000 agent traces: performance variance decomposition (base model 41.4% vs scaffold 1.5%), evidence ignored in 68% of traces, refutation-driven revision in 26%, and rarity of convergent multi-test evidence. No equations, fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations appear in the provided text or abstract. The central claim follows from the reported behavioral counts and variance analysis without any step that reduces by construction to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of custom behavioral coding schemes for epistemic norms and on the assumption that the chosen domains and agent scaffolds are representative of real scientific inquiry.

axioms (1)
  • domain assumption Defined behavioral categories (evidence ignored, refutation-driven belief revision) accurately reflect scientific epistemic norms
    These categories are used to score agent traces and form the basis for the 68% and 26% figures.

pith-pipeline@v0.9.0 · 5603 in / 1237 out tokens · 55428 ms · 2026-05-10T03:51:35.223992+00:00 · methodology

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Forward citations

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