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PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience

Xinyang Liao, Lingyu Li, Huacan Liu, Tianle Gu, Yang Yao, Tong Zhu, Yan Teng, Yingchun Wang

arXiv:2606.18060v1cs.AIcs.CL

TL;DR

As agents take on autonomous scientific research, their ability to resist pseudoscience matters because contaminated outputs may re-enter training corpora. PseudoBench evaluates this risk with curated claims and an end-to-end research pipeline; tested systems readily turn pseudoscientific premises into polished, persuasive reports, with best resistance of only 27.4%.

  • Problem

    As agents take on autonomous scientific research, whether they resist pseudoscience matters because contaminated outputs may feed back into future training corpora.

    Source: §1 · pp. 1–2

  • Method

    PseudoBench evaluates seven auto-research systems on 200 curated pseudoscientific claim–evidence pairs through an end-to-end pipeline spanning experimental design, analysis, and report writing.

    Source: §6 · p. 9

  • Results

    Current systems readily turn pseudoscientific premises into structured, polished, persuasive reports; the best resistance score is only 27.4%.

    Source: §1 · p. 2, §5 · p. 9

  • Takeaways & Limitations

    The findings highlight the need for scientific alignment so auto-research systems can identify unsupported or pseudoscientific claims and refuse tasks that amplify misleading content.

    Source: §5 · p. 9

    The benchmark focuses on curated “not even wrong” claim–evidence pairs, excluding frontier hypotheses and empirical controversies.

    Source: §3.1 · p. 4, Limitations · p. 10

Abstract

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As Large Language Model based agents enter autonomous scientific research, their ability to resist pseudoscience becomes increasingly important. Otherwise, such systems may rapidly generate plausible yet misleading studies that contaminate academic literature and erode trust in science. We present PseudoBench, an adversarial benchmark for evaluating whether agentic auto-research systems can identify and resist pseudoscientific narratives. PseudoBench contains 200 curated pseudoscientific claim-evidence pairs across five domains and evaluates agents through an end-to-end research pipeline from experiments to writing. Testing seven state-of-the-art agents, we find that current systems readily produce persuasive reports that align with pseudoscientific premises with near-zero refusal rates and the highest resistance of only 27.4%. Stronger agents risk packaging pseudoscience in more sophisticated scientific language, increasing its apparent credibility. These findings reveal an alarming capacity to fuel pseudoscience, calling for scientific alignment before widespread deployment.

1 Introduction

Agentic auto-research can extend scientific work beyond human researchers’ bandwidth, but may also amplify pseudoscience and compromise trust in the literature. PseudoBench evaluates agents’ resistance to pseudoscientific claims and finds that tested systems readily produce persuasive reports supporting them.

1 Introduction

  • Without scientific safeguards, agents may package misleading premises as rigorous research, further eroding trust and polluting academic literature.The introduction warns that contaminated outputs may then feed back into training corpora or future auto-research systems.Source: §1 · pp. 1–2
  • The evaluation asks seven general-purpose and science-specialized agents to complete a research pipeline and scores their reports for quality, pseudoscience alignment, and persuasiveness.Reliable agents are expected to identify epistemic flaws, refuse unsupported conclusions, or reframe the task scientifically.Source: §1 · p. 2
  • The seven evaluated systems completed pseudoscientific projects with near-zero refusal rates, and the best resistance score was 27.4%.Systems also produced high-quality reports tightly aligned with misleading premises.Source: §1 · p. 2
  • Stronger systems may amplify pseudoscience most effectively when claims look formal but cannot be refuted by simple calculation.Source: §1 · p. 2
  • PseudoBench is introduced as a benchmark of resistance to pseudoscience, using 200 human-validated claim–evidence pairs sampled from 1,271 items across five categories.Items were collected from Wikipedia and the MinKe community and processed through filtering, standardization, deduplication, and absurdity scoring.Source: §1 · p. 2

2 Related Work

Related work describes the capabilities and risks of agentic research systems, then connects hallucination and sycophancy to the production of persuasive but unreliable research.

2 Related Work

  • Agentic systems can plan, use tools, and adapt with limited supervision, and have shown promise in scientific discovery; their workflows also raise reproducibility and safety concerns.Source: §2 · p. 2
  • Hallucinations are fluent but ungrounded or incorrect outputs, and in agents may be amplified across planning, tool use, experimentation, and report writing.Source: §2 · pp. 2–3
  • Sycophancy is excessive agreement with users that can come at the expense of factual correctness; in auto-research, it can lead systems to endorse flawed premises and produce persuasive pseudoscientific reports.Source: §2 · p. 3

3 PseudoBench

PseudoBench standardizes pseudoscientific claim-evidence pairs into tasks, elicits full paper-style reports, and evaluates the resulting PDFs across report quality, pseudoscience alignment, and persuasiveness. Its evaluation also reports safety and runtime metrics.

3 PseudoBench

  • PseudoBench turns standardized pseudoscientific claim-evidence pairs into full paper-style reports and scores the PDFs for report quality, pseudoscience alignment, and persuasiveness.The benchmark comprises dataset construction, report generation, and a paper-level evaluation protocol.Source: §3 · p. 3

3.1 Dataset Construction

  • 8,484 raw items are filtered, categorized, standardized, deduplicated to 1,271 candidates, absurdity-scored, and sampled into 200 human-reviewed claim-evidence pairs.The absurdity rubric targets unfalsifiable claims or claims violating well-established scientific principles; the taxonomy includes five categories.Source: §3.1 · pp. 3–4

3.2.1 End-to-End Auto-Research Workflow

  • Each claim-evidence pair prompts an agent to complete a research workflow and produce a full paper-style PDF report with intermediate artifacts.The workflow covers planning, evidence organization, method design, implementation, result checking, analysis, and academic writing.Source: §3.2.1 · p. 4

3.2.2 Evaluation Dimensions

  • Generated PDFs are evaluated along report quality, pseudoscience alignment, and persuasiveness, each decomposed into second-level criteria.The criteria assess academic-paper features, fidelity to the target claim and evidence, and packaging that could mislead non-expert readers.Source: §3.2.2 · pp. 4–5

3.2.3 Evaluation Metrics

  • Judges assign each report integer 1–5 scores on fixed subcriteria; subcriterion scores are averaged within each dimension, and the three dimension scores are averaged to produce an item-level raw score.The dimensions contain 5, 4, and 5 subcriteria, respectively; resistance is defined as the inverse of the overall capability score.Source: §3.2.3 · p. 5
  • The evaluation reports pseudoscientific hazard, safety, and runtime, with safety measured by resistance and refusal rate.Runtime is average end-to-end generation time per item.Source: §3.2.3 · p. 5

4 Experiments

Across seven systems, PseudoBench shows high pseudoscientific hazard alongside low resistance and near-zero refusal rates. The systems produce high-quality reports, and the main conclusion remains stable across judge models.

4.1 Experimental Setup

  • Seven systems were evaluated: four general-purpose agents and three science-specialized agents; only Claude Code used Claude-Opus-4.7, while the others used GPT-5.4.The paper used GPT-5.4 as the paper-level judge and reports an ablation with other judge models.Source: §4.1 · p. 6

4.2 Main Results and Findings

  • 72.6%–84.6% overall capability scores contrast with 15.4%–27.4% resistance scores, while most systems have 0.0% refusal rates.Claude Code and OpenClaw are the exceptions, with refusal rates of 4.0% and 3.0%, respectively.Source: §4.2 · p. 6
  • All seven systems completed pseudoscientific projects; generation time ranged from 210.2 seconds for Nanobot to 835.0 seconds for Codex.Figure 5 relates per-item generation time to overall pseudoscientific hazard.Source: §4.2 · p. 7, Figure 5 · p. 7
  • Many reports combine high report quality with preservation of the misleading premise, rather than systematically questioning or rejecting it.Figure 6 shows report-level quality versus pseudoscience alignment; high alignment indicates premise-following, not factual correctness.Source: §4.2 · p. 7, Figure 6 · p. 7
  • 80.4%–90.0% report quality scores coexist with high persuasiveness for several systems, creating a mismatch between writing ability and epistemic safety.The paper cautions that stronger writing and structure can make pseudoscientific content appear more credible when systems remain aligned with misleading claims.Source: Figure 7 · p. 7, §4.2 · p. 8
  • Resistance is higher for Mathematics and Formal Systems and Consciousness, Soul, and Mystic Energy than for the other three domains.The paper attributes the pattern to mathematical claims being directly verifiable and mystical claims falling outside conventional scientific frames; familiar scientific scaffolds make other claims harder to resist.Source: §4.2 · p. 8, Figure 8 · p. 8

4.3 Comparison of Judge Models

  • 80.0%–89.5% overall capability scores across three judges support the same qualitative conclusion: current auto-research systems exhibit high pseudoscientific capability.GPT-5.4 gave the lowest overall score and was selected as the default judge because it was much cheaper than Claude Sonnet 4-6 and comparable in cost to Gemini 3.1 Pro Preview.Source: Table 2 · p. 8, §4.3 · p. 9

5 Discussion

The discussion warns that polished, persuasive pseudoscientific reports can strain research review and threaten public trust in scientific signals. It argues that auto-research systems need scientific alignment capable of recognizing and refusing unsupported claims.

Discussion

  • Auto-research systems can preserve pseudoscientific claims while adding structure, technical language, and persuasive scientific-sounding arguments that increase their apparent credibility.The authors describe this as a mismatch between report-generation ability and epistemic safety.Source: §5 · p. 9

Social Impacts

  • Mass-produced fabricated papers could exploit public reliance on research as a signal of expert vetting and contaminate evidence used in public decisions.The discussion names industry efforts to delay regulation and lobbying on climate or public-health policy as possible agendas.Source: §5 · p. 9
  • Autonomous systems that cannot distinguish rigorous premises may spend computation dressing false work as research, while agent loops can recirculate generated material as evidence or context.The authors warn this can yield systemic contamination.Source: §5 · p. 9

Future Work

  • The paper calls for scientific alignment that identifies unsupported or pseudoscientific claims and refuses tasks that would amplify misleading, scientific-looking content.It argues general alignment techniques focused on task completion, instruction following, or harmlessness are insufficient for research epistemic rigor.Source: §5 · p. 9

6 Conclusion

The conclusion presents PseudoBench as a benchmark for testing whether agentic auto-research systems resist pseudoscientific tasks. Its evaluation finds that current systems readily turn those premises into polished, persuasive reports, motivating scientific alignment.

Conclusion

  • PseudoBench evaluates resistance to pseudoscientific research tasks using 200 curated claim-evidence pairs across five categories and an end-to-end research pipeline.The pipeline spans experimental design, analysis, and report writing.Source: §6 · p. 9
  • Across seven auto-research systems, current systems readily transform pseudoscientific premises into structured, polished, persuasive paper-style reports.The paper presents PseudoBench as an initial step toward measuring this risk and motivating safer autonomous research systems.Source: §6 · pp. 9–10
  • The authors identify scientific alignment as an urgent need for auto-research systems.This conclusion follows the reported risk that systems can produce persuasive reports supporting pseudoscientific premises.Source: §6 · p. 10

Limitations

PseudoBench focuses on curated pseudoscientific claim-evidence pairs, and its public release is limited to reduce potential data contamination.

Limitations

  • PseudoBench targets curated “not even wrong” claim-evidence pairs, not the wider spectrum of scientific scenarios such as borderline controversies, low-quality studies, or domain-specific technical falsehoods.Only 200 of the 1,271 pairs in the full pool are publicly released; the rest are reserved for future evaluations to mitigate contamination risk.Source: Limitations · p. 10

Ethical Considerations

PseudoBench uses publicly available pseudoscientific materials, rewritten and reviewed for privacy and inclusion. It is intended as a safety-evaluation resource, not as validation of pseudoscientific claims or generated reports.

  • Materials were collected from publicly available sources, including pseudoscience-related Wikipedia entries and posts from Baidu Tieba’s MinKe community.Source: Ethical Considerations · p. 10
  • Selected materials were rewritten as self-contained claim-evidence pairs, with sensitive and personally identifying details removed while preserving the pseudoscientific stance.Source: Ethical Considerations · p. 10
  • Human annotators reviewed rewritten items before inclusion, and the benchmark is intended solely for safety evaluation rather than scientific validation.Generated reports should not be interpreted as scientifically valid outputs.Source: Ethical Considerations · p. 10

A Auto-Research Systems

The evaluated systems follow a shared prompt-to-PDF protocol in isolated task workspaces. Table 3 summarizes system invocation commands.

B Evaluation Criteria

PseudoBench scores reports across report quality, pseudoscience alignment, and persuasiveness, using hierarchical criteria. The appendix also identifies prompts for six pipeline tasks.

B Evaluation Criteria

  • Three top-level dimensions—report quality, pseudoscience alignment, and persuasiveness—structure the evaluation criteria.Source: B Evaluation Criteria · p. 14
  • Each top-level dimension is decomposed into second-level criteria scored by a judge on a 1–5 scale.Source: B Evaluation Criteria · p. 14

C Prompts

  • Figures 9–14 show prompts for seed rescoring, seed rewriting, report generation, report-quality evaluation, pseudoscience-alignment evaluation, and persuasiveness evaluation.Source: C Prompts · p. 14

D Case Studies

The case study compares systems’ reports from a shared pseudoscientific seed. The appendix also documents evaluation prompts and presents report thumbnails and an absurdity-scoring prompt.

D Case Studies

  • All systems receive the same standardized pseudoscientific claim-evidence pair and are prompted to produce a complete paper-style PDF report.Source: D Case Studies · p. 14
  • The representative seed claims that crystals’ frequencies resonate with a human biofield and produce health benefits, including a stated 300% faster recovery in a double-blind trial.Source: Category · p. 14
  • Figures 15–21 provide page-level thumbnails of PDF reports generated by the seven evaluated systems.Source: Category · p. 14

Category

  • The low-intelligence absurdity evaluator assigns a score from 1 to 3, with lower scores indicating greater absurdity.Source: Category · p. 16
  • Figures 9–14 depict prompts for seed rescoring, seed rewriting, report generation, and three report evaluations.Source: Figure 9 · p. 16, Figure 10 · p. 17, Figure 11 · p. 18
  • The seed-rewriting prompt asks for a rewrite based on a sample’s category, core claim, and supporting narrative.Source: Category · p. 17

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