Agentic Reproducibility Assessment (ARA)

• Agentic Reproducibility Assessment (ARA) is a suite of agent-driven protocols that evaluate research reproducibility by examining artifacts, empirical rerunnability, and claim robustness.
• It combines dual-spectrum, execution-based, and document-only approaches to measure both author-provided and independently verified outcomes using formal models and scoring functions.
• ARA frameworks employ automated scripts, multi-agent workflows, and detailed provenance logging to ensure transparent, rigorous assessments of reproducibility in scientific research.

Searching arXiv for the cited ARA-related papers and benchmark papers to ground the article in current arXiv records. Agentic Reproducibility Assessment (ARA) denotes agent-driven procedures for determining whether scientific claims, workflows, or artifacts can be reconstructed, executed, or otherwise verified. Recent work uses the term in several related senses: as dual-spectrum assessment of author-provided artifacts and external reproduction outcomes (Akella et al., 2024), as executable artifact evaluation through reproduction scripts and automated judges (Baek et al., 10 Feb 2026), as machine-verifiable certification of agent-native research packages (Liu et al., 27 Apr 2026), as document-only workflow reconstruction for peer review (Riehl et al., 4 May 2026), and as multiverse-style evaluation of how extreme a reported claim is within the space of defensible analyses (Miao et al., 1 Jul 2026). This suggests that ARA is not yet a single standardized protocol but an emerging family of agent-centered reproducibility frameworks.

1. Terminology, scope, and major formulations

Across the recent literature, ARA is used to assess at least four distinct objects: artifact completeness, empirical rerunnability, document-level reconstructability, and claim robustness under alternative analyses. In some systems the object of assessment is a paper-plus-artifact bundle; in others it is the paper alone; in still others it is the position of a reported result inside a multiverse of plausible analysis paths. The common denominator is that an agent, or a coordinated set of agents, performs nontrivial reasoning over scientific materials and emits a reproducibility judgment rather than merely executing a fixed script.

Formulation	Assessment object	Distinctive mechanism
Dual-spectrum ARA	Papers and artifacts	Author-centric spectrum $A$ and external-agent spectrum $E$ (Akella et al., 2024)
Artisan-style ARA	Paper plus artifact	Reproduction script $s$ plus output and method judges (Baek et al., 10 Feb 2026)
Ara-native ARA	Agent-Native Research Artifact	ARA Seal with Structural Integrity, Argumentative Rigor, and Execution Reproducibility (Liu et al., 27 Apr 2026)
Document-only ARA	Publication text alone	Directed workflow graph with structural and content scores (Riehl et al., 4 May 2026)
Multiverse ARA	Reported claim	Agentic Bootstrap and the m-value over plausible analysis paths (Miao et al., 1 Jul 2026)

A further terminological complication is that the acronym “ARA” predates the reproducibility-assessment usage in at least one safety-oriented paper, where it denotes “autonomous replication and adaptation,” a cluster of capabilities involving resource acquisition, self-copying, and adaptation to failure rather than scientific reproducibility assessment (Kinniment et al., 2023). In the reproducibility literature, by contrast, “agentic” refers to autonomous planning, tool use, workflow reconstruction, evaluation, and auditing.

A recurrent distinction is between reproducibility and replicability. “Artisan: Agentic Artifact Evaluation” states that reproducibility is obtaining similar outcomes using the same setup, whereas replicability is obtaining similar outcomes with a different setup; Artisan targets the former (Baek et al., 10 Feb 2026). Document-only ARA narrows the object further: reproducibility is “the degree to which this pathway can be reconstructed from the information contained in the paper alone, without requiring access to external artifacts such as source code or replication packages” (Riehl et al., 4 May 2026).

2. Formal models and scoring functions

One influential formalization begins with the dual-spectrum framework over research artifacts. The author-centric spectrum $A$ measures how much effort authors invest in making a paper reproducible through artifacts, with statuses $A_{PWA}$, $A_{PUNX}$, and $A_{PAX}$. The external-agent spectrum $E$ measures what an independent team can empirically accomplish, with statuses $E_{NR}$, $E_{AR}$, $E$0, and $E$1. In the operational ARA synthesis built on this framework, artifact discovery, execution success, output equivalence, and documentation sufficiency are combined into an overall score

$E$2

with guidance to prioritize empirical outcomes by choosing $E$3 (Akella et al., 2024).

Execution-based ARA systems often formalize success at the level of a generated script. Artisan frames reproducibility as code generation: given paper $E$4, table $E$5, and artifact URL $E$6, the agent must produce a self-contained reproduction script $E$7. Acceptance requires both numerical correctness and a nontrivial method of obtaining it, formalized as

$E$8

where copied-results scripts are rejected, while last-mile and full reproductions are accepted (Baek et al., 10 Feb 2026). This separates output matching from methodological validity and is meant to prevent reward hacking such as copying checked-in tables or hard-coding digits.

Document-only ARA adopts a different formal object: a directed workflow graph $E$9 over sources, methods, experiments, and sinks. Nodes are assigned ordinal micro-scores $s$0, aggregated into a content score $s$1 and a structural score $s$2, then combined by the geometric mean

$s$3

The structural component penalizes unresolved identifiers, unconsumed sources, unproduced sinks, missing source-to-sink paths, and graph fragmentation; the content component averages stage-level description sufficiency (Riehl et al., 4 May 2026).

Ara-native review introduces yet another formalism. Its empirical stage uses difficulty-weighted success rates over claim-verification subtasks, while its certification mechanism escalates through Level 1 Structural Integrity, Level 2 Argumentative Rigor, and Level 3 Execution Reproducibility. The crucial shift is from assessing isolated outputs to assessing whether claims, experiments, code, and evidence are cross-linked in a machine-operable artifact (Liu et al., 27 Apr 2026).

Multiverse-oriented ARA generalizes reproducibility from “can the reported analysis be rerun?” to “how typical is the reported claim among defensible analyses on the same data?” Agentic Bootstrap estimates an m-value,

$s$4

where $s$5 ranges over plausible analysis paths and $s$6 is a scalar claim functional. Small $s$7 means the reported claim lies in the tail of the defensible analysis-space distribution rather than near its center (Miao et al., 1 Jul 2026).

3. Agentic workflows and system designs

Execution-oriented ARA typically follows a staged pipeline from artifact discovery to verification. In the dual-spectrum operationalization, the agent parses full text to locate repositories and data deposits, reconstructs the environment through Conda, venv, or Docker, acquires and validates datasets, runs the pipeline with seed control, compares reproduced metrics to reported results, and emits provenance reports with discrepancies and step outcomes (Akella et al., 2024). The same paper explicitly treats code repositories, data deposits, and supplemental materials as the atomic units that determine both author-centric and external-agent statuses.

Artisan makes this workflow more explicit and more adversarial. Its agent performs five steps—download artifact, read README, find relevant commands, execute commands, and create the reproduction script $s$8—inside an iterative refinement loop with a step limit of 30. On each submission, an output judge runs the script in a fresh container and classifies the result as Static error, Runtime error, Mismatched results, or Success; if output succeeds, a method judge classifies the script as Copied results, Last-mile reproduction, or Full reproduction (Baek et al., 10 Feb 2026). The separation between execution-based judging and method judging is one of the clearest architectural features in the ARA literature.

A more restrictive version appears in “Read the Paper, Write the Code: Agentic Reproduction of Social-Science Results,” where agents are denied access to the original code, numerical results, and full paper. They receive only a structured methods description, blinded table templates, and raw data, and they must produce fresh Python scripts under strict information isolation. The system then performs deterministic, cell-level comparison against original tables and traces discrepancies through an error-attribution pipeline that distinguishes original paper errors, extraction errors, agent errors, and missing data (Kohler et al., 23 Apr 2026).

Large-scale empirical ARA can also be organized as a multi-agent workflow with role specialization. In the IV/2SLS reanalysis system, a Profiler agent parses PDFs and code to identify specifications, a Librarian agent retrieves replication packages, a Janitor harmonizes paths and injects deterministic export hooks, a Runner executes Stata/R/Python code and performs cross-language validation, a Skeptic applies a fixed diagnostic template, and a Journalist assembles standardized reports. The orchestrator routes tasks and interprets failures, but all numerics are computed by version-controlled deterministic code (Xu et al., 17 Feb 2026).

Ara-native systems move beyond rerunning code and instead redesign the research artifact itself. An Ara is structured into a Cognitive Layer, Physical Layer, Exploration Graph, and Evidence Layer, while the ARA Seal operates in three stages: conceptual verification, empirical verification, and human judgment. This architecture treats failed experiments, heuristics, and raw evidence as first-class reproducibility objects rather than as informal supplementary material (Liu et al., 27 Apr 2026).

4. Benchmarks and empirical findings

The empirical literature on ARA is already diverse in scope, ranging from predictive screening to full execution and claim-level auditing.

Setting	Corpus or benchmark	Headline result
Predictive dual-spectrum modeling	2,659 ACM articles	Longformer-MLP reached accuracy 0.85 for $s$9 and 0.79 for $A$0 (Akella et al., 2024)
Artisan-Bench	60 tasks from 23 SE papers	44/60 successful reproduction scripts; average cost \$0.45 and 48 minutes per task (Baek et al., 10 Feb 2026)
Ara evaluation	PaperBench and RE-Bench	Question-answering accuracy rose from 72.4% to 93.7%; reproduction success from 57.4% to 64.4% (Liu et al., 27 Apr 2026)
REPRO-Bench	112 social-science tasks	Best baseline 21.4% accuracy; REPRO-Agent 36.6% (Hu et al., 25 Jul 2025)
IV large-scale reanalysis	92 papers, 215 specifications	87% end-to-end success overall; 100% conditional reproducibility given accessible materials (Xu et al., 17 Feb 2026)
Strict-isolation reimplementation	48 social-science papers	Best agent reached 91% coefficient sign agreement and more than 80% within the original 95% CI (Kohler et al., 23 Apr 2026)
Document-only ARA	ReScience C 213, ReproBench 112, GoldStandardDB 50	Around 61% accuracy; 60.71% on ReproBench and 61.68% on GoldStandardDB (Riehl et al., 4 May 2026)
Replication-package quality evaluation	5 SE packages	91.4% inter-run consistency and 75.4% micro-averaged agreement with manual baseline (Mbida et al., 1 Jun 2026)

These results do not describe a single scaling curve. They instead show that performance depends strongly on what is being assessed. Predictive screening of paper reproducibility from metadata, artifact signals, venue information, and linguistic features is already materially above simple baselines, with Longformer-based models outperforming tree and linear models on the dual-spectrum labels (Akella et al., 2024). By contrast, end-to-end social-science assessment remains difficult: REPRO-Bench reports that a random guess among four scores would yield 25% accuracy, while the best baseline reached only 21.4%, and REPRO-Agent improved this to 36.6% (Hu et al., 25 Jul 2025).

Software-engineering artifact evaluation currently shows the strongest execution-level results. Artisan reproduced 44 of 60 tasks and outperformed the best available baseline by 3.14$A$1, while also surfacing 20 new paper-artifact inconsistencies (Baek et al., 10 Feb 2026). The strict-isolation social-science system is notable for a different reason: even without access to the original code, agents were often able to recover the sign and approximate magnitude of published coefficients, and the root-cause analysis indicated that many failures were attributable to paper underspecification rather than purely to agent error (Kohler et al., 23 Apr 2026).

Document-only assessment occupies an intermediate position. It avoids the heavy infrastructure of full reruns, yet it still approximates human judgments at around 61% accuracy across heterogeneous corpora and outperforms prior systems on ReproBench and GoldStandardDB under a paper-only constraint (Riehl et al., 4 May 2026). This is useful for triage and peer review, but it does not replace execution-based verification.

5. Evidence, logging, provenance, and standardization

A central theme across ARA systems is that reproducibility assessment requires richer evidence than a final pass/fail label. One line of work argues that evaluations of agentic systems in software engineering should publish Thought–Action–Result (TAR) trajectories and LLM interaction data, including prompts, temperatures, and exact model versions. The stated motivation is that conventional performance tables are insufficient for explaining why an agent succeeded or failed, while trajectory publication enables systematic cross-approach comparison without rerunning expensive baselines (Li et al., 1 Apr 2026).

Ara-native artifacts make this evidentiary requirement structural. The Cognitive Layer exposes claims and falsification criteria; the Physical Layer records executable code plus configs and environment details; the Exploration Graph preserves dead ends and pivots; and the Evidence Layer stores exact metric tables, logs, and provenance. The ARA Seal then checks not merely whether outputs exist, but whether claims, experiments, code, and evidence resolve consistently across layers (Liu et al., 27 Apr 2026).

Another provenance-heavy variant appears in agentic AIBOMs. There, reproducibility is tied to exact parity, semantic parity, runtime dependency capture, and contextual exploitability assessment. The framework uses a baseline environment reconstruction agent, a runtime dependency and drift-monitoring agent, and a policy-aware vulnerability and VEX reasoning agent. Outputs are cryptographically bound to software composition data through a composite hash,

$A$2

with signed SBOM fragments and immutable storage for auditability (Radanliev et al., 9 Mar 2026).

Standardization work extends the scope of ARA from scientific artifacts to agent evaluation infrastructure itself. Agentified Agent Assessment (AAA) and AgentBeats replace benchmark-specific harnesses with a unified interface: A2A for task management and MCP for tool access. The core claim is that conventional benchmarking requires $A$3 bespoke integrations across $A$4 benchmarks and $A$5 agents, whereas AAA reduces this to $A$6 by requiring both judge agents and subject agents to speak the same protocols. AgentBeats then provides Local, Remote, Hosted, Proxy, and CI modes to trade off openness, privacy, and reproducibility (Liu et al., 11 Jun 2026).

Some frameworks broaden the evidence model further by evaluating the full runtime behavior of agentic systems across LLMs, Memory, Tools, and Environment. In the Autonomous CloudOps case study, this produced metrics such as Policy Adherence, Dependency Inquiry, Tool sequence correctness, retrieval precision and recall, and guardrail-violation counts, revealing behavioral deviations that conventional task-completion metrics concealed (Akshathala et al., 14 Dec 2025). The broader implication is that ARA increasingly treats reproducibility as an observability problem as much as a rerunning problem.

6. Limitations, disagreements, and future directions

A persistent limitation is the lack of definitive ground truth. The dual-spectrum modeling paper uses ACM badges and their absence as proxy labels, and explicitly notes scarcity of definitive ground truth, ACM-centric domain bias, and calibration weaknesses in embedding-based models (Akella et al., 2024). Document-only ARA has a different ceiling: because it operates strictly on publication text, missing or ambiguous reporting directly degrades reconstructability, and implicit dependencies that become obvious only during execution remain invisible (Riehl et al., 4 May 2026).

Execution-based systems have their own scope restrictions. Artisan-Bench excludes non-Docker artifacts, GPUs, non-public APIs, specialized hardware, and runs longer than eight hours, while its method judge is reported at 81% accuracy and can misclassify both successful and copied-results scripts (Baek et al., 10 Feb 2026). REPRO-Bench exposes a different bottleneck: agents struggle with mixed-language social-science packages, file-path resolution, Stata log handling, and nuanced four-level reproducibility judgments, so absolute accuracy remains low even after targeted improvements (Hu et al., 25 Jul 2025).

Another disagreement concerns what reproducibility should mean once agents become strong enough to explore large design spaces. Ara-native artifacts show that preserved failure traces accelerate early progress but can later constrain stronger agents to the prior playbook (Liu et al., 27 Apr 2026). “The Agentic Garden of Forking Paths” pushes this further by arguing that the core challenge is often not obviously flawed analysis, but selective exploration and reporting from a large space of methodologically defensible analyses. Its m-values show that statistically significant claims may still be tail-selected within the analysis multiverse, so a reproducible pipeline need not imply a robust substantive conclusion (Miao et al., 1 Jul 2026).

Reporting practice remains another weak point. The software-engineering position paper documents widespread underreporting of prompts, temperatures, exact model identifiers, and other evaluation details, and argues that this makes agentic baselines hard to reproduce and harder still to interpret (Li et al., 1 Apr 2026). This concern aligns with the broader move toward trajectory logging, evidence-layer publication, environment manifests, and protocol-level standardization.

The forward-looking agenda is correspondingly broad. Proposed directions include multi-agent verification, active learning for selecting papers to reproduce, automated repair of stale or buggy artifacts, broader support for GPUs and public APIs, executable method judges, pre-submission and continual post-publication checks, and stronger protocol standardization through A2A and MCP (Baek et al., 10 Feb 2026). Taken together, these proposals indicate that ARA is evolving from a set of isolated benchmarks into a layered reproducibility stack: predictive triage, workflow reconstruction, execution-based verification, provenance capture, and standardized judge-agent infrastructure.