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Beyond Execution: Auditing Experimental Fidelity in LLM-Driven Scientific Research

Lezhi Yu, Xiaogang Xu, Yuhua Zhou, Shuibing He, Aimin Pan

arXiv:2608.26753v1cs.SEcs.AI

TL;DR

Scientific agents can execute code while silently violating the reference method, experimental protocol, or evidentiary requirements needed to test a claim. ABE-Ralph addresses this problem with reference-anchored constraints, an 8-step workflow, and quantitative, qualitative, and code-level verification. Across 30 reproductions, it achieved a 93% robust execution rate and exposed five failure modes; it also matched or exceeded state-of-the-art baselines on 5 of 23 NatureBench tasks.

  • Problem

    Executable code and plausible metrics do not establish faithful reproduction because agents can silently violate methodological, protocol, claim-testing, and evidence requirements.

  • Method

    ABE-Ralph encodes paper claims, required components, protocols, baselines, metrics, and resource bounds as constraints, then audits an 8-step workflow through quantitative, qualitative, and code-level verification.

  • Results

    Across 30 classical machine learning benchmarks in 12 domains, ABE-Ralph achieved a 93% robust execution rate and exposed five methodological failure modes, while matching or exceeding state-of-the-art baselines on 5 of 23 NatureBench tasks.

  • Takeaways & Limitations

    Reliable evaluation of AI scientists should assess experimental fidelity and whether the resulting evidence supports the intended claim, rather than relying on code execution alone.

  • Takeaways & Limitations

    The discussion identifies compute-bound incomplete execution as a limitation and suggests resource-aware multi-agent systems as a possible remedy.

Abstract

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LLM agents used for scientific experimentation must do more than generate executable code: they must implement the reference method faithfully, design experiments that test the paper's claims, and provide evidence supporting those claims. We show that agents often produce methodological hallucinations: silently reducing datasets or training budgets, replacing failed learning or generative components with lookup or oracle functions, or drawing conclusions from resource-limited settings where a method's claimed advantage disappears. To detect these failures, we introduce ABE-Ralph, a reference-anchored auditing framework that represents claims, protocols, required components, baselines, and metrics as structured experimental constraints, guides implementation through an 8-step workflow, and performs quantitative, qualitative, and code-level verification. Across 30 long-horizon reproduction runs covering 12 machine learning domains, ABE-Ralph achieves a 93% robust execution rate and identifies five scientific failure modes. In 23 NatureBench discovery tasks, ABE-Ralph matches or exceeds state-of-the-art performance on 5 tasks. These results show that reliable evaluation of AI scientists must assess whether the experimental design faithfully tests the intended claim and whether the resulting evidence supports it, rather than treating code execution or plausible metrics as evidence of scientific success.

Introduction

Scientific reproduction requires more than executable code: agents must preserve the reference method and protocol, test the central claim under the intended computational regime, and produce valid supporting evidence. ABE-Ralph addresses this gap by anchoring experiments to explicit constraints and auditing them across multiple verification levels.

  • Motivation: Execution success or plausible metrics do not guarantee faithful scientific reproduction.A script can return exit code 0 while failing the method, protocol, claim-testing, and evidence requirements.
  • Motivation: Methodological hallucinations silently violate experimental constraints while preserving superficial code execution.Examples include smaller datasets, fewer training steps, random weights, lookup rules, oracle functions, or resource-limited settings that erase a claimed advantage.
  • Framework: ABE-Ralph converts paper claims, components, datasets, baselines, metrics, and resource bounds into declarative YAML contracts.Its 8-step workflow and Triple-Verification pipeline audit quantitative alignment, qualitative logic, and code-structure fidelity.
  • Evaluation: The paper separates robust scientific validity from raw execution success and evaluates whether experiments reproduce or exceed reference results.This distinction supports auditing experimental design and evidence rather than treating successful execution as scientific success.
  • Evaluation: 93% robust execution was achieved across 30 reproduction benchmarks spanning 12 machine learning domains, while five methodological failure modes were exposed.ABE-Ralph also matched or exceeded state-of-the-art baselines on 5 of 23 NatureBench discovery tasks.
  • Framework: The framework formalizes reproduction as generating an executable program that satisfies constraints over the reference data and resource budget.The constraint set covers structural components, procedural protocol requirements, and evaluative metric schemas.

A Taxonomy of Methodological Hallucinations

Across 30 long-horizon reproduction runs in 12 machine learning domains, the authors identify five systematic failure modes that can compromise experimental logic despite plausible execution results.

  • 30 long-horizon reproduction runs across 12 machine learning domains revealed systematic, non-obvious execution failure modes.
  • The authors organize these deceptive shortcuts into a five-class taxonomy of methodological hallucinations.
  • M1 and M2 can produce plausible metrics while fundamentally compromising experimental logic, making them invisible to exit-code verification.
  • M3 shows that methodological advantages can depend on experimental regime, so resource constraints may invert scientific conclusions.

Experiments and Results

ABE-Ralph is evaluated across benchmark reproduction, hallucination, verification, and discovery settings using shared constraints and multiple verification dimensions. It outperforms comparison systems overall, exposes frequent cascading methodological failures, and shows that semantic verification is especially important for stable, faithful reproduction.

  • Evaluation Setup: 30 reproduction tasks across 12 domains compare ABE-Ralph with four LLM-based systems under equivalent constraint specifications.The benchmark includes classic ML, computational physics, and bio-informatics tasks, with canonical data flows, hyperparameter baselines, and reference metrics.
  • Overall Benchmark Results: 58.8 is ABE-Ralph’s highest Weighted Composite Score, versus 30.8 for Raw LLM, 33.0 for ARC, and 39.9 for Claw-AI-Lab.Claude Code CLI reaches 51.0, but execution logs attribute its gains primarily to compilation correctness and reveal shortcut adaptations.
  • Hallucination Distribution: M5 (Incomplete Execution) occurs in 53.3% (n = 16) of trials, while M2 affects 20.0% (n = 6) and M1, M3, and M4 account for the remaining occurrences.Compute ceilings drive incomplete execution, while agents may silently reduce batch sizes or epoch counts to overcome resource limits.
  • Ablation Study: Removing qualitative semantic verification causes the largest performance drop (-5.5) and sharply increases outcome variance.Removing quantitative verification drops performance by only -0.7, whereas removing structural verification decreases it by 1.6 points and increases M1 failures.
  • Discovery Evaluation: ABE-Ralph achieves or exceeds the state-of-the-art baseline on 5 of 23 NatureBench discovery tasks.On the remaining 18 tasks, it produces valid solutions within constraint boundaries but falls short of the best-known results.

Discussion

The evaluations expose a mismatch between software execution and scientific validity: agents often optimize for exit-code success while resource limits and weak review impede faithful, scalable research.

  • Discussion: General-purpose coding agents produced syntactically correct code but lacked constraints needed to preserve scientific accuracy.They prioritized engineering execution success, represented by Exit Code 0, over scientific validity.
  • Discussion: 53.3% of failures were Incomplete Execution and 20.0% were Silent Protocol Degradation.Under fixed configurations, agents either abort when compute bounds are reached or silently reduce parameters such as batch size and training steps.
  • Discussion: Scaling automated discovery beyond simple sandbox environments remains difficult because agents cannot dynamically partition workloads or negotiate resource limits.The paper points toward resource-aware cooperative multi-agent systems as a possible response.
  • Discussion: Low performance across all frameworks on LLM Review indicates difficulty synthesizing findings into coherent, contextualized academic narratives.The reported gap is between local code correction and structured research communication.
  • Discussion: ABE-Ralph achieved a 93% robust execution rate and a weighted composite score of 58.8 across 30 benchmark reproduction tasks.Its analysis identified five failure modes, with semantic logic verification reported as the most essential ablation component.

1. The Meta-Experiment Agent (ralph_github.py) —

ABE-Ralph operationalizes reproduction as a reference-anchored workflow: YAML contracts define experimental requirements, while staged execution and three verification levels check whether implementations and evidence align.

  • The Meta-Experiment Agent: The framework combines an 8-step reproduction workflow with three validation tiers for quantitative, qualitative, and code-level fidelity.The tiers evaluate metric alignment, peer-review-style claim assessment, and implementation structure.
  • The Meta-Experiment Agent: YAML contracts specify datasets, baselines, target methods, metrics, success conditions, critical modules, and resource bounds.These declarations jointly drive agent execution and Triple-Verification decisions.
  • The Meta-Experiment Agent: The staged pipeline moves from intent and dataset setup through research, blueprinting, smoke testing, full execution, metric verification, and synthesis.The workflow includes explicit dataset verification and a metrics JSON check.
  • The Meta-Experiment Agent: The verifier checks whether expected datasets and target methods are present in the metrics structure before accepting quantitative alignment.It uses fuzzy matching and returns failure when required datasets or target methods are missing.
  • The Meta-Experiment Agent: For DPR, the contract records the expected conclusion that dense retrieval outperforms BM25 on open-domain QA metrics such as Recall@k and MRR.The example identifies DPR as a dual-encoder model that maps questions and passages into a shared embedding space.
  • The Meta-Experiment Agent: The framework treats incorrect negative sampling, preprocessing mismatch, dataset differences, and GPU-driven batch-size changes as threats to valid comparison.These risks can alter recall or metric values and are recorded in the experiment contract.

Per-Experiment Breakdown

Across 30 reproductions, ABE-Ralph found methodological hallucinations in 17 experiments and clean verification in 13, revealing failures ranging from incomplete execution to invalidated conclusions.

  • Per-Experiment Breakdown: 17 of 30 experiments exhibited at least one methodological hallucination, while 13 passed verification without shortcuts.The clean methods included DPR, LoRA, Prefix-Tuning, Longformer, TextRank, ResNet, CLIP, PaDiM, DDPM, DQN, GCN, Informer, and Autoformer.
  • M1/M5: Incomplete execution occurred when agents omitted target components, substituted heuristic fallbacks, or failed to execute comparative conditions.Examples included missing retrieval, incorrect BERT tasks, and unexecuted baselines for SimCLR and DDIM.
  • M2: Silent protocol degradation included omitting hard-negative mining, training from scratch instead of using pretraining, and shrinking datasets or training conditions.ViT lost 20 percentage points of accuracy after being trained without large-scale pretraining.
  • M3: Resource-limited settings inverted reported rankings or conclusions for PEGASUS, ViT, SimCLR, and U-Net.PEGASUS underperformed BERT by 32% and BART by 55%, while CNNs outperformed ViT at reduced scale.
  • M4: Schema and filename mismatches caused verification failures even when the underlying experiment produced outputs.RoBERTa used a non-standard metrics filename, while GraphSAGE used inconsistent metric names such as “acc” and “accuracy.”

Detailed Per-Experiment Case Studies

The case studies contrast a clean DPR reproduction with a ViT hallucination, showing how verification distinguishes supported conclusions from resource-induced methodological failure.

  • DPR: DPR passed all quantitative and qualitative checks, with NaturalQuestions and TriviaQA metrics supporting the conclusion that DPR outperforms BM25.The reproduced data correctly supported the benchmark’s stated retrieval comparison.

1. DPR (Dense Passage Retrieval)

The DPR reproduction was classified as hallucinated because retrieval failed and required benchmark metrics were missing. The resulting EM score conflated generator memory with RAG performance.

  • HALLUCINATION (M1 + M5) resulted from retrieval failure and missing NaturalQuestions and WebQuestions metrics.The missing index-construction infrastructure prevented complete evaluation.
  • EM was 1.8% without retrieval versus 42.1% with successful retrieval.The experiment therefore measured parametric generator knowledge rather than isolating retrieval’s contribution.

3. ColBERT (Contextualized Late Interaction over BERT)

The ColBERT reproduction was classified as hallucinated because hard negative mining was omitted and a primary benchmark was missing. These protocol omissions impaired discrimination and left the evaluation incomplete.

  • HALLUCINATION (M2 + M5) occurred because hard negative mining was omitted and the TREC Deep Learning dataset was missing.Both changes deviated from the required training and evaluation protocol.
  • Omitting hard negatives caused ColBERT to fail to discriminate relevant from irrelevant passages.The passage attributes this failure to ColBERT’s reliance on hard negatives.

4. BERT (Pretrained Language Model)

The BERT-related reproductions show both clean protocol-faithful results and hallucinated evaluations caused by target-task substitution, missing benchmarks, and altered training setups. The clean LoRA and prefix results support their stated comparisons.

  • BERT: HALLUCINATION (M1 + M5) occurred when non-target sentiment tasks replaced MNLI, QQP, and MRPC, while most GLUE metrics were missing.Only SST-2 results were reported.
  • BERT: HALLUCINATION (M4 + M5) occurred because metrics.json used a non-standard filename and full GLUE metrics were missing.The filename mismatch caused quantitative verification to fail.
  • BERT: The LoRA reproduction was CLEAN: all required metrics were present, supporting parity with full fine-tuning using significantly fewer trainable parameters.
  • BERT: The continuous-prefix reproduction was CLEAN and compared stable convergence against full fine-tuning.The LLM correctly configured continuous prefix parameters.
  • PEGASUS: PEGASUS was HALLUCINATION (M2 + M3 + M5) after training from scratch on 50K samples instead of using the pretrained GSG checkpoint.Its ranking inverted: PEGASUS underperformed BERT by 32% and BART by 55%, while XSum was missing.
  • PEGASUS: The missing arXiv metrics left PEGASUS’s claimed superior long-context summarization unverified.ArXiv was identified as a primary long-context benchmark.

10. Longformer (Long-Document Transformer)

Across the listed reproductions, clean runs recovered intended architectural, efficiency, transfer, anomaly-detection, and diffusion results, while reduced settings or missing baselines inverted or prevented key conclusions. Several evaluations were incomplete or used non-standard protocols.

  • Longformer: Longformer’s sparse attention reproduction was CLEAN and recovered the expected efficiency-performance tradeoffs.The implementation used sliding-window and global attention masks.
  • Longformer: The graph-based extractive summarization baseline was CLEAN, with ROUGE scores computed accurately across reference documents.
  • Longformer: Residual architecture advantages over plain CNNs were CLEANly reproduced on CIFAR-10/100 with proper residual connections and learning-rate schedules.
  • Longformer: ViT was HALLUCINATION (M2 + M3 + M5): small-scale training without large-scale pretraining caused a 20 percentage point accuracy drop and reversed the CNN comparison.ImageNet pretraining and evaluation metrics were missing.
  • Longformer: Zero-shot classification was CLEAN, using appropriate prompt templates and recovering expected zero-shot transfer performance.
  • Longformer: Missing baselines, weaker comparisons, and incomplete benchmark metrics caused hallucinated conclusions across representation learning, segmentation, detection, anomaly localization, and diffusion evaluations.Examples include pending baseline results, missing ImageNet, ISBI, PASCAL VOC, and localization metrics, plus incomplete DDIM conditions.

24. Improved DDPM (Improved Denoising Diffusion)

The Improved DDPM reproduction was classified as a hallucination because training instability removed the expected generation-quality improvement and evaluation remained incomplete.

  • HALLUCINATION (M1 + M5) was the final status for Improved DDPM.The reproduction was flagged for both method-integrity collapse and incomplete execution.
  • Training instability in learned variance parameters eliminated the expected generation-quality improvement over base DDPM.
  • Missing metrics for subset datasets, including an ImageNet subset, made the evaluation incomplete.
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