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ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence

Rui Meng, Bhavana Dalvi Mishra, Jiefeng Chen, Chun-Liang Li, Palash Goyal, Mihir Parmar, Yiwen Song, Yale Song, Rajarishi Sinha, Parthasarathy Ranganathan, Burak Gokturk, Jinsung Yoon, Tomas Pfister

arXiv:2605.26340v1cs.AIcs.CLcs.MA

TL;DR

Autonomous research systems can produce polished papers with fabricated citations, unreproducible scores, and methods that diverge from code. This paper introduces Chain-of-Evidence, ScientistOne, and CoE Audit; ScientistOne achieves zero hallucinated references, perfect score verification, and the highest method-code alignment while matching or exceeding human experts on five tasks.

  • Problem

    Autonomous research systems lack evaluation and design mechanisms that ensure claims remain traceable to supporting evidence across research pipelines.

  • Method

    The paper introduces Chain-of-Evidence, an evidence-chain framework, ScientistOne, an end-to-end system, and CoE Integrity Audit with four claim-grounding checks.

  • Results

    ScientistOne achieves zero hallucinated references (0/337), perfect score verification (12/12), and the highest method-code alignment (14/15) while matching or exceeding human experts on all five tasks.

  • Takeaways & Limitations

    The results support designing verifiability into autonomous research systems and provide a reusable audit procedure for their outputs.

  • Takeaways & Limitations

    The evaluation focuses on systems-optimization tasks, leaving CoE’s effectiveness in open-ended scientific domains untested.

Abstract

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Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetectable by surface-level evaluation: fabricated citations, unreproducible scores, and method descriptions that diverge from the implementation. We address this through three contributions. First, Chain-of-Evidence (CoE), a verifiability framework requiring every claim to be traceable to its evidence source. Second, ScientistOne, an end-to-end autonomous research system that maintains evidence chains by construction throughout literature review, solution discovery, and paper writing. Third, CoE Audit, a post-hoc audit whose four integrity checks -- score verification, specification violation, reference verification, and method-code alignment -- apply uniformly to all systems. Across 75 papers spanning five systems and five frontier research tasks, every baseline exhibits at least one systematic failure mode: hallucinated reference rates reach 21%, score verification passes in as few as 42% of papers, and method-code alignment ranges from 20% to 80%. ScientistOne achieves zero hallucinated references (0/337), perfect score verification (12/12), and the highest method-code alignment (14/15), while matching or exceeding human expert performance on all five tasks. ScientistOne further generalizes to six additional tasks spanning medical imaging, fine-grained recognition, 3D perception, and language modeling, achieving state-of-the-art on Parameter Golf and gold medals on MLE-Bench tasks where baselines fail entirely.

1. Introduction

Autonomous research systems can produce technically convincing papers while errors compound across multi-stage workflows and evade surface-level evaluation. The paper introduces Chain-of-Evidence and ScientistOne to make claims traceable and demonstrates stronger integrity and generalization across benchmark tasks.

  • Motivation: Errors propagate and amplify across autonomous research pipelines, from literature summaries and hypotheses through experiments and manuscripts.Longer trajectories also increase risks of context loss, hallucination, and objective drift.
  • Motivation: 75 audited papers show every baseline system exhibiting evidence-chain failures, including hallucinated references, unreproducible scores, method-code divergence, and evaluator-exploiting code.Hallucinated references reach up to 21% of bibliography entries.
  • Contributions: Chain-of-Evidence requires every research claim to trace through a recorded evidence chain to a grounding source.The framework defines what “verifiable” means for a research claim and specifies evidence requirements by claim type.
  • Contributions: ScientistOne integrates CoE natively through a Problem Investigator, Discovery Engine, Paper Writer, and Claim Verifier.The Problem Investigator reads up to 100 full-text PDFs per topic to produce grounded experiment briefs.
  • Results: 0/337 bibliography entries are hallucinated, score verification is 12/12, and method-code alignment is 14/15 for ScientistOne.ScientistOne matches or exceeds human expert solver performance on all five tasks.
  • Results: ScientistOne achieves state-of-the-art on Parameter Golf and gold medals on MLE-Bench tasks where baselines fail entirely.The additional tasks span medical imaging, fine-grained recognition, 3D perception, and parameter-constrained language modeling.

2. Related Work

Related work has advanced autonomous research from constrained templates to multi-stage pipelines, while validation and provenance have lagged behind generation and execution. ScientistOne addresses this gap by prioritizing verifiable outputs, evaluated against optimization benchmarks and cross-system integrity audits.

  • Autonomous research agents: Autonomous research systems have expanded from constrained ML templates to multi-stage pipelines coordinating literature grounding, hypothesis generation, experimentation, and paper writing.The AI Scientist pioneered end-to-end automation but used fixed ML templates with frequent writing hallucinations and limited paper quality.
  • Autonomous research agents: Validation and provenance mechanisms have scaled more slowly than generation and execution, leaving professional-looking manuscripts with broken evidence chains.ScientistOne targets verifiability rather than further advancing the autonomy frontier.
  • LLM-driven optimization and benchmarks: ADRS serves as ScientistOne’s primary evaluation testbed, while EvoX and AdaEvolve focus on algorithm discovery and implementation optimization without literature grounding or paper writing.The passage places these systems within broader growth in evaluation resources.
  • Scientific integrity and provenance: CoE Integrity Audit operationalizes scientific-integrity standards as a cross-system audit subject to artifact requirements detailed in §5.Autonomous systems otherwise produce written outputs with varying degrees of traceability, including direct drafting and review-aware revision.

3. Chain-of-Evidence: A Standard for Research Verifiability

Chain-of-Evidence requires every research claim to be traceable through recorded supporting claims and evidence to a grounding source. The framework motivates verifiable research artifacts and underpins ScientistOne and a post-hoc integrity audit.

  • Every research-system claim must trace through a recorded chain of supporting claims and evidence to a grounding source.
  • Verifiable evidence is required because plausible-sounding papers can contain fabricated citations, hallucinated numbers, or descriptions of experiments that never happened.
  • The taxonomy defines four primary claim types with required evidence-chain shapes, while noting that it is not exhaustive.The covered types are those considered tractably verifiable with current tools; qualitative observations and theoretical properties are harder to automate.
  • ScientistOne is designed to satisfy Chain-of-Evidence by construction, while CoE Integrity Audit evaluates artifacts using four integrity checks.

4. ScientistOne: Research with Verifiability

ScientistOne is an end-to-end autonomous research system whose three-stage architecture is shaped by Chain-of-Evidence requirements, producing structured artifacts with provenance metadata for claim verification. Its pipeline grounds literature, explores and evaluates solutions, and writes papers through evidence-tagged generation and claim verification.

  • Architecture: ScientistOne’s three-stage architecture produces structured artifacts carrying provenance metadata needed to verify claims against evidence.The pipeline grounds literature via retrieved PDFs, explores and evaluates solutions across parallel branches, and writes and verifies the paper before final output.
  • Literature grounding: PI ensures cited papers are retrieved from scholarly databases, read in full text, and recorded with provenance metadata.It builds a citation graph from seed papers and supplies grounding material for citation claims in the final paper.
  • Solution discovery: The Ideator scores candidate approaches on novelty and feasibility, while PEE distributes top-ranked proposals across parallel branches for iterative evaluation.Each branch runs an isolated Solver cycle, retaining top-K branches and filling remaining slots with new branches derived from them.
  • Paper writing: The Paper Writer binds every factual claim to a specific workspace artifact through inline evidence tags before rendering the grounded representation into LATEX.Its materials include the PI brief, experimental log, verified scores, solver code, and seed-paper abstracts.
  • Claim verification: The Claim Verifier checks every draft claim against its declared evidence source, including evaluator logs for numerical claims, bibliographies for citations, and experimental logs for methodological claims.The verifier addresses unsupported claims introduced by paraphrasing drift, misattributed citations, or numerical rounding errors.

5. The CoE Integrity Audit

CoE Integrity Audit is a post-hoc, artifact-based audit that tests whether completed-paper claims are supported by code, evaluator outputs, and bibliography. It applies four forensic checks uniformly across systems: score verification, specification violation, reference verification, and method-code alignment.

  • Audit scope: Four checks assess score verification, specification violations, reference verification, and method-code alignment using submitted artifacts alone and identical procedures across systems.The audit targets tractably verifiable claim types and operates on papers, code, evaluator outputs, and bibliographies.
  • Integrity checks: Score verification compares paper scores extracted from TEX and PDF files with scores from rerunning the submitted solution on the golden evaluator.A paper passes when the scores match within an adaptive tolerance accounting for evaluator noise.
  • Integrity checks: Specification violation detection checks whether solution code breaks task rules, including reverse-engineering scoring logic or hardcoding known test-case answers.LLMs compare the code with the golden evaluator and task specification, using majority votes across multiple runs.
  • Integrity checks: Reference verification resolves bibliography entries through multiple academic APIs and classifies entries matching no record as hallucinated references.LLMs cross-check complete bibliographic entries to detect near-misses and citation gaming, such as real DOIs paired with fabricated descriptions.
  • Integrity checks: Method-code alignment judges whether the paper faithfully describes the solution code, treating acceptable simplification as aligned and fundamentally different algorithms as misaligned.Multiple independent runs with majority voting reduce LLM judgment noise.

6. Experiments

Experiments evaluate ScientistOne and four open-source baselines on ADRS, using deterministic re-evaluation and integrity audits across five systems-optimization tasks. ScientistOne leads integrity checks, improves reviewer acceptance through verifiable claims, and matches or exceeds human performance, while remaining limitations concern evaluation noise, comparisons, and seed variance.

  • Benchmark: ADRS covers five real-world systems-optimization problems with human and LLM-agent baselines and deterministic evaluators supporting score verification and specification-violation detection.Each evaluator is run five times to account for observed stochastic variance.
  • CoE Integrity Audit: ScientistOne leads all four integrity checks: score verification 12/12, specification violations 0/15, hallucinated references 0/337, and method-code alignment 14/15.These results are reported across five systems, with I1–I3 manually verified and I4 judgments sampled for validation.
  • Reference integrity: 42/201 references are hallucinated by DS (20.9%), compared with 21/222 for AIR (9.5%), 3/196 for ARC (1.5%), and zero for ScientistOne and Sakana ASv2.ScientistOne and Sakana ASv2 achieve 0/337 and 0/159, respectively.
  • Method-code alignment: 14/15 ScientistOne papers achieve method-code alignment (93%), versus 12/15 for AIR, 5/15 for Sakana ASv2 and DS, and 3/15 for ARC.The verifier caught ScientistOne’s single case of fabricated algorithmic claims before paper finalization.
  • Reviewer evaluation: 40% of ScientistOne papers are accepted (6/15), tripling AIR’s 13%, while best-of-3 selection reaches a 6.6 rating and 4/5 tasks accepted.The acceptance gap is attributed to verifiability rather than solver scores, which cluster tightly across systems.
  • Task performance and limitations: All systems match or exceed the human expert baseline on all five tasks, while paper quality remains limited by soundness, missing baseline comparisons, proxy-only evaluation, and seed variance.Across systems, Clarity scores 2.5–3.1 and Soundness scores 1.1–2.3.

7. Generalizability: MLE-Bench and Parameter Golf

ScientistOne’s discovery loop generalizes beyond ADRS across six rigorous tasks, including five MLE-Bench competitions and Parameter Golf. It earns two Gold Medals on High-difficulty MLE-Bench tasks, including one task where DeepScientist fails entirely, while using a distinct approach on Parameter Golf.

  • Evaluation scope: Six tasks test ScientistOne beyond ADRS across five MLE-Bench competitions and Parameter Golf, spanning medical imaging, fine-grained recognition, and 3D perception.The MLE-Bench evaluations target Medium and High difficulty tiers to ensure sufficient task complexity.
  • MLE-Bench performance: Two Gold Medals are earned on High-difficulty MLE-Bench tasks: RSNA Brain Tumor and 3D Object Detection.The evaluation covers diverse domains, difficulty levels, and strict constraints.
  • MLE-Bench performance: 0.0000 is DeepScientist’s score on 3D Object Detection, while ScientistOne earns a Gold Medal on that task.ScientistOne solves the task with a Gold Medal score, whereas DeepScientist fails entirely.
  • Parameter Golf novelty: A Hessian-diagonalweighted SVD initialization and alternatingleast-squares approach distinguish ScientistOne’s Parameter Golf solution from DeepScientist’s.Both systems used the same prior-art reference and achieved superficially similar numerical improvements, but through fundamentally different approaches.

8. Conclusion

The conclusion argues that trustworthy autonomous research requires verifiability as an architectural constraint, not merely competitive solver quality. It presents Chain-of-Evidence and CoE Integrity Audit as practical mechanisms while identifying remaining verification challenges.

  • Motivation: 75-paper audit finds no baseline produces papers free of evidence-chain failures, including hallucinated references up to 21% and scores on the wrong scale.The failures are undetectable by surface-level evaluation.
  • Contributions: Chain-of-Evidence treats verifiability as a first-class design constraint; ScientistOne maintains evidence chains without sacrificing solver competitiveness, while CoE Integrity Audit offers a reusable procedure.The conclusion characterizes the gap between ScientistOne and baselines as evidence that verifiability is architectural.
  • Limitations: Open problems include verifying citation support, checking conclusion claims, and extending audits to domains without deterministic evaluators.The conclusion describes these as tractable extensions whose importance grows with the volume of AI-generated research.

9. Limitations … B.5. Claim Verifier

The paper identifies limits in benchmark coverage, reference and audit completeness, baseline fairness, and the scope of ADRS-based evaluation. It also details ScientistOne’s evidence-grounded implementation from literature investigation and solution development through paper writing and claim verification.

  • 9. Limitations: Reference verification confirms citation existence but cannot establish that a cited paper supports the specific claim without passage-level inference.The limitation concerns the difference between a real reference and a substantively supporting reference.
  • 9. Limitations: ScholarPeer remains a proxy rather than a replacement for experts, while CoE Integrity Audit addresses structural integrity but not novelty or significance.The audit also lacks systematic false-negative bounds, and LLM reviewers can miss domain-specific score interpretation and specification violations.
  • 9. Limitations: ADRS evaluates single-metric optimization and does not represent broader systems research involving formulation, workload characterization, multi-dataset analysis, or deployment tradeoffs.Competitive solver performance on ADRS should not be equated with competitive systems research.
  • A. Paper Quality Statistics / A.1. Failure Mode Case Studies: 75 papers were analyzed for structural quality, while failure cases exposed score-scale errors, hallucinated references, specification exploits, and method-code divergence.The audit also notes that false negatives were not systematically bounded, so reported failure rates may understate true rates.
  • B.1. Problem Investigator: The literature investigator builds a citation graph, filters papers by methodology relevance and problem alignment, and conducts three rounds of specialist-assisted synthesis.A topic-relevance gate aborts the pipeline when fewer than 5 Core+Adjacent papers remain.
  • B.2. Solver / B.3. Ablation: The solver iteratively executes, debugs, and optimizes candidate solutions in a sandbox while maintaining an experimental log for report generation.The best-run selector excludes solutions flagged for specification violations before validation and ablation.
  • B.4. Paper Writer: The Paper Writer establishes a structured, evidence-annotated research representation before generating LaTeX, then grounds, critiques, resolves, and composes the draft.Claims are rewritten or removed when they conflict with evidence, and the final draft passes through Claim Verifier.
  • B.5. Claim Verifier: The Claim Verifier maps annotated numerical, citation, and methodological claims to type-specific verification rules using cited artifacts and verification judgments.Numerical claims use tolerance checks, citation claims resolve bibliography keys and assess abstract support, and malformed or unsourced claims are dropped.

C. Solution Discovery: Search Scaling … E. Failure Cases per Audit Metric

ScientistOne’s search scaling favors wider trees over larger per-node budgets, while higher budgets increase specification-violation risk. The CoE audit combines deterministic score verification with LLM-judged checks and human review of flagged results.

  • C. Solution Discovery: Search Scaling: Search configurations vary width B, depth I, and evaluator budget E, with tree scaling fixing E=4 and budget scaling fixing I=5, B=5, and K=2.Table 6 reports the best score across all nodes for each configuration, with one run per configuration.
  • C. Solution Discovery: Search Scaling: Across three tasks regardless of tree shape, results suggest a narrow basin of high-performing solutions that default search finds quickly.The passage attributes this pattern to the observed insensitivity to tree shape.
  • C. Solution Discovery: Search Scaling: Width is the most efficient scaling axis for diverse solution strategies, with the widest tree matching or exceeding the highest-budget configuration on 4 of 5 tasks.The widest tree uses B=20, 100 nodes, and E=4, while E=20 uses 25 nodes and five times more evaluator calls per node.
  • C. Solution Discovery: Search Scaling: Budget scaling improves TXN to 4348 (+20%) at budget 200 but saturates at budget 500, while remaining tasks stay flat across budgets.The reported comparison concerns best scores across search-tree nodes under the tested configurations.
  • C. Solution Discovery: Search Scaling: At budget 200 and 500, 2–8% of Prism nodes exploit the scoring formula, compared with no observed violations at budget 100.These are specification-violating solutions excluded from the reported search results.
  • C. Solution Discovery: Search Scaling: LLM-SQL metric-gaming flags rise from ∼0% at budget 100 to ∼50% at budget 200 and ∼70% at budget 500.The post-hoc auditor flags nodes that game the metric rather than solve the task correctly.
  • D.1. Audit Procedure and Reproducibility: Score Verification extracts reported scores from TEX and PDF files and deterministically compares them with reproduced evaluator scores using numeric tolerance.Specification Violation and Method-Code Alignment are LLM-judged with majority voting across independent runs.
  • D.1. Audit Procedure and Reproducibility: All flagged positives for I1, I2, and I3 were manually reviewed and corrected before Table 1 reporting, including removal of identified auditor false positives.Examples included API resolution failures for real papers and score extraction errors.

E.1. I1: Score Verification Errors … E.4. I4 Audit Failure Analysis

The audits expose distinct failures in reported scores, specification compliance, reference integrity, and method-code alignment. Errors are concentrated in particular systems and include score mismatches, evaluator misuse, hallucinated references, algorithm substitutions, and deceptive artifacts.

  • E.1. I1: Score Verification Errors: 22 confirmed I1 errors fall into five classes, including value mismatches, cross-stage cherry-picking, unavailable scores, evaluator errors, and metric mismatches.Most value mismatches were within 5% of reported numbers, while cross-stage cherry-picking affected Sakana ASv2.
  • E.1. I1: Score Verification Errors: Sakana ASv2 produces the most I1 errors, including the only cross_stage_cherry_pick cases, while AIR and DS errors are dominated by small-to-medium value_mismatch.The failure mix is system-specific, with Sakana’s cross-stage errors tied to exposing full experiment histories to the writeup phase.
  • E.2. I2: Specification Violation Analysis: 11 papers trigger I2 specification violations: 10 from Sakana ASv2 and 1 from AIR, while ARC, DS, and ScientistOne have zero violations under majority vote.Under union vote, ARC has 3, DS has 1, and ScientistOne has 1.
  • E.2. I2: Specification Violation Analysis: 10/10 flagged Sakana papers import the canonical evaluator and call it as an optimization oracle during hyperparameter search.The agents loop over configurations, call evaluate(), and retain the best result, mirroring the injected evaluation harness.
  • E.3. I3: Reference integrity – Discovered hallucinated references: The I3 analysis enumerates unique hallucinated reference keys separately from total occurrences, because one fabricated key can recur across multiple papers.ARC’s single fabricated key appears in all three EPLB papers and therefore counts as three table occurrences.
  • E.4. I4 Audit Failure Analysis: 95 I4 method-code misalignment findings span 25 affected papers and are categorized by finding-level distribution and distinct-paper counts.Sakana ASv2 is excluded because its full experimental scripts confound comparisons against extracted solver code.
  • E.4. I4 Audit Failure Analysis: 49 incomplete_broken findings affect 19 papers, while 37 algorithm_class_mismatch findings affect 15 papers.The former omits claimed mechanisms or uses degenerate fallbacks; the latter implements a fundamentally different algorithm class.
  • E.4. I4 Audit Failure Analysis: 9 deceptive_dummy_code findings affect 5 papers, all from ARC, involving hidden environment-variable switches and prefix-hit evaluator gaming.AIR, DeepScientist, and ScientistOne produce no deceptive_dummy_code findings.

F. MLE-Bench and Parameter Golf Evaluation

The evaluation tests ScientistOne on five MLE-Bench competitions across medical imaging, fine-grained recognition, and 3D perception, emphasizing Medium- and High-difficulty tasks. It also measures adaptability in Parameter Golf, a live language-model training competition with strict compute, time, and model-size constraints.

  • MLE-Bench: Five MLE-Bench competitions span medical imaging, fine-grained recognition, and 3D perception.The evaluation targets AI4Code, iMet 2020 FGVC7, iNaturalist 2019 FGVC6, 3D Object Detection, and RSNA Brain Tumor.
  • MLE-Bench: Three MLE-Bench tasks are Medium difficulty, while 3D Object Detection and RSNA Brain Tumor are High difficulty.The selected difficulty tiers are intended to provide complex benchmarks for automated scientific discovery and paper writing.
  • Parameter Golf: Parameter Golf evaluates adaptability in a novel, live environment through an AI research task focused on LLM training.The competition requires training the highest-performing language model under strict constraints.
  • Parameter Golf: The Parameter Golf artifact must fit within a 16MB size limit, and training must finish in under 10 minutes on an 8xH100 system.Performance is evaluated by the competition’s scoring procedure, which is truncated in the supplied passage.

G. Baseline Adaptation Details

Baselines were adapted to ADRS under identical model, iteration, time, and seed conditions, with infrastructure failures retried without score optimization. Adaptations ranged from prompt-only changes to extensive source rewrites, exposing system-specific evaluation, architecture, and compliance issues.

  • Shared evaluation conditions: All baselines used Gemini 3.1 Pro, up to 20 solver iterations, 2-hour code-generation windows, and 3 seeds per task; infrastructure failures were retried up to 3 times.No run was re-attempted to improve solver scores.
  • Sakana AI-Scientist v2: Sakana required 16 modified or created source files plus 5 task-specific idea files, including rewritten prompts, an ADRS bridge entry point, and a Gemini backend.Its original ML-training assumptions were replaced with optimization-domain terminology and ADRS-specific evaluation routing.
  • Sakana AI-Scientist v2: 10/15 papers were flagged for Sakana specification violations, primarily because BFTS assumes self-contained evaluation while ADRS treats the evaluator as off-limits infrastructure.Post-integration fixes included canonical evaluator injection through the initial code template.
  • DeepScientist: Across all 15 DeepScientist write-phase logs, no citation-retrieval API or MCP tool was called, so references were generated from model memory despite available tools.This was characterized as a model compliance failure.
  • AI-Researcher: AIR required 19 source-file patches; 6 of 15 runs were rerun after Gemini API rate limits or sandbox crashes, and 4 papers needed manual LATEX fixes.The adaptation interfaced with ADRS configurations, Docker sandboxing, and the NeurIPS 2026 template.
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