arxiv: 2604.06236 · v1 · submitted 2026-04-04 · 💻 cs.DL

Recognition: no theorem link

LLMs Have Made Failure Worth Publishing

Sungmin Lee

Authors on Pith no claims yet

Pith reviewed 2026-05-13 17:29 UTC · model grok-4.3

classification 💻 cs.DL

keywords datapublishingfailurellmspeerresearchreviewerstools

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The pith

Large language models have turned the suppression of failure data into a critical limitation for scientific progress.

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

The paper claims that the traditional filtering out of negative results in scientific publishing now poses a bigger challenge because of LLMs. These models learn from the biased positive literature, which limits their ability to serve effectively as research assistants, as consumers of training data, and as peer reviewers. By analyzing three specific ways LLMs increase the value of failure data, the authors show how this absence degrades performance across those roles. They propose experiments to test the effects and discuss what conditions would allow a more balanced publishing culture to develop.

Core claim

Scientific publishing has long excluded negative results, creating a positive bias in the literature. LLMs trained on this literature inherit the bias, which reduces their utility as research tools, training data consumers, and peer reviewers. The absence of failure data therefore degrades performance in all three areas, and the authors outline protocols to validate this effect while considering structural changes needed for inclusive publishing.

What carries the argument

The inheritance of positive bias from training literature, which affects LLMs in their roles as research tools, training data consumers, and peer reviewers.

Load-bearing premise

That the positive bias from the literature measurably degrades LLM performance in research, training, and reviewing roles in ways that can be isolated experimentally.

What would settle it

An experiment comparing an LLM trained only on positive results versus one trained on a mix including failures, then measuring differences in accuracy as a research tool, data efficiency, or review quality.

read the original abstract

Scientific publishing systematically filters out negative results. We argue that this long-standing asymmetry has become an urgent problem in the era of large language models, which inherit the positive bias of the literature they are trained on, face an impending shortage of high-quality training data, and are increasingly deployed as both research tools and peer reviewers. We analyze three ways in which LLMs have changed the value of failure data and show that the systematic absence of such data degrades their utility as research tools, training data consumers, and peer reviewers alike. We outline experimental protocols to validate these claims and discuss the structural conditions under which a failure-inclusive publishing culture could emerge.

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.

Desk Editor's Note private letter to a colleague

The paper flags a real shift where LLMs make negative results more valuable for training and review, but it rests on argument rather than any executed tests or measurements.

read the letter

The core point here is that publication bias, long a known issue, now directly limits what LLMs can do as tools, data sources, and reviewers because they train on a filtered positive literature. That framing around the three roles is the main new angle, and it lands as a useful reminder that data scarcity plus model scale changes the cost of missing failures. The paper does a clean job laying out why failure data might matter more now than in pre-LLM eras, especially with the data wall coming up and models already being used in research pipelines. It also sketches some experimental protocols to check the claims, which at least gives readers a concrete next step to consider. The argument stays coherent on its own terms and draws on standard observations about training data and peer review without obvious internal contradictions. The soft spot is that everything stays at the level of logical steps and unrun protocols. There are no comparisons of models trained on positive-only versus failure-inclusive sets, no controls for data volume or domain, and no results showing measurable degradation in any of the three roles. The central claim that absence of failure data degrades utility therefore rests on premises that are plausible but untested here. Readers who already follow the publication-bias literature will see this as an extension rather than a fresh measurement. The piece is aimed at people working on LLM data curation, AI-assisted science, and research integrity. It is worth sending to peer review so the protocols can get feedback on feasibility and so the community can debate whether the three roles really create a new structural problem or just amplify an old one. A serious editor should route it rather than desk-reject.

Referee Report

3 major / 2 minor

Summary. The manuscript argues that the long-standing practice of filtering negative results from scientific publishing has become an urgent issue with the rise of LLMs. LLMs inherit positive bias from training literature, face data shortages, and are used as research tools and peer reviewers; the absence of failure data therefore degrades their utility in these three roles. The paper analyzes the changed value of failure data, outlines (but does not execute) experimental protocols to test the claims, and discusses structural conditions for a failure-inclusive publishing culture.

Significance. If the central claims were empirically supported, the work would usefully connect publication-bias literature with LLM training and deployment practices, potentially informing data-curation strategies and journal policies. As presented, the significance remains prospective because the degradation effects are asserted via logical steps rather than measured.

major comments (3)

[Abstract and analysis of three roles] Abstract and the section analyzing the three LLM roles: the claim that positive bias 'measurably degrades' utility as research tools, training consumers, and peer reviewers rests entirely on untested premises. No quantitative comparison (e.g., performance of models trained on positive-only vs. failure-augmented corpora) or executed protocol is provided, so the degradation effect is not demonstrated.
[Experimental protocols] Section outlining experimental protocols: the protocols are described at a high level without specifying metrics, baselines, controls for data volume/domain coverage, or statistical tests. This prevents readers from assessing whether the protocols could isolate publication bias from other training factors.
[Abstract and main argument] Throughout: the paper states it will 'show' that absence of failure data degrades utility, yet supplies only reasoning and unexecuted outlines. This mismatch between claim language and delivered evidence is load-bearing for the central thesis.

minor comments (2)

[Introduction] Add concrete references to empirical studies on LLM sensitivity to training-data bias (e.g., work on negative examples in instruction tuning) to ground the inheritance claim.
[Early sections] Define 'failure data' and 'positive bias' operationally in the first section so that the three roles can be evaluated against a shared criterion.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the constructive comments. We agree with the assessment that the manuscript presents logical arguments and protocol outlines rather than empirical measurements. We will revise the language to accurately reflect this and expand the protocol details as suggested.

read point-by-point responses

Referee: [Abstract and analysis of three roles] Abstract and the section analyzing the three LLM roles: the claim that positive bias 'measurably degrades' utility as research tools, training consumers, and peer reviewers rests entirely on untested premises. No quantitative comparison (e.g., performance of models trained on positive-only vs. failure-augmented corpora) or executed protocol is provided, so the degradation effect is not demonstrated.

Authors: We acknowledge that no quantitative comparisons or executed experiments are provided in the manuscript. The analysis relies on logical reasoning about how LLMs inherit biases from training data and how their roles as tools and reviewers amplify the impact of missing failure data. We will revise the abstract and the analysis section to clarify that we 'argue' rather than 'show' the degradation, and note that empirical validation is left for future work following the outlined protocols. revision: yes
Referee: [Experimental protocols] Section outlining experimental protocols: the protocols are described at a high level without specifying metrics, baselines, controls for data volume/domain coverage, or statistical tests. This prevents readers from assessing whether the protocols could isolate publication bias from other training factors.

Authors: We agree that the protocols require more detail to be evaluable. In the revised manuscript, we will specify concrete metrics such as model performance on tasks involving negative results, baselines comparing positive-only trained models to those augmented with synthetic or real failure data, controls ensuring comparable data volumes and domain coverage, and statistical tests like ANOVA or bootstrap methods to assess significance of differences. revision: yes
Referee: [Abstract and main argument] Throughout: the paper states it will 'show' that absence of failure data degrades utility, yet supplies only reasoning and unexecuted outlines. This mismatch between claim language and delivered evidence is load-bearing for the central thesis.

Authors: This is a valid observation. We will update the abstract, introduction, and conclusion to use language consistent with the delivered content, such as 'we analyze' and 'we outline protocols to test these claims' instead of 'show'. This revision will align the claims with the argumentative and prospective nature of the work. revision: yes

Circularity Check

0 steps flagged

No significant circularity; argument is self-contained conceptual analysis

full rationale

The paper advances a conceptual argument about publication bias affecting LLMs in three roles, drawing on external observations of scientific publishing practices and LLM training characteristics rather than any derivations, equations, or fitted parameters. No steps reduce by construction to self-definitions, self-citations, or renamed inputs; the outlined experimental protocols are proposed as future validation rather than executed in a manner that creates circularity. The central claims rest on logical reasoning from independent premises, making the derivation self-contained with no load-bearing reductions to the paper's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The argument rests on two domain assumptions about LLM training and data scarcity that are stated but not derived or evidenced within the provided abstract.

axioms (2)

domain assumption LLMs inherit the positive bias of the literature they are trained on
Explicit premise in the abstract linking training data composition to model behavior.
domain assumption There is an impending shortage of high-quality training data
Stated as a factor increasing the value of failure data.

pith-pipeline@v0.9.0 · 5385 in / 1002 out tokens · 37157 ms · 2026-05-13T17:29:25.573608+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages

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