AI in Scientific Research: Assistance, Provenance, and Reproducibility

A result you cannot re-derive is not a result. It is a rumor with a figure attached.

That is the uncomfortable position research teams land in when AI assistance enters the workflow. A literature sweep, a cleaning script, a trial-report draft — all of it arrives in minutes, plausible and well-formatted. What does not arrive is the chain of custody. Which sources were actually read. Which steps were run. Which numbers were recomputed. Which claims a human verified.

The capability is real. The traceability is not. And in science, the trace is the product.

The Reproducibility Tax on AI Assistance

Every AI step you add to a research workflow shifts labor from production to verification. That shift is the actual cost, and most teams price it at zero.

Consider an illustrative scenario. A draft summary that once took a week to produce might now take an afternoon. But proving that summary is correct — resolving every citation, checking every number, confirming that the synthesis reflects what the sources actually establish — can consume roughly the same effort it always did. You did not eliminate the work. You moved it downstream, where it is harder to see and easier to skip. The exact ratio depends on the task, the domain, and how much of the verification can itself be automated, so treat the comparison as a shape, not a measurement.

Here is the criterion I would use to judge any proposed AI step: a step is safe to delegate when its output can be independently re-derived from recorded inputs and a recorded procedure. If you cannot hand the record to a competent colleague who was not in the room and have them arrive at the same output, the step is not reproducible. It is a private performance.

Three things get conflated here, and the confusion is expensive:

• Model capability — what the system can do under test conditions.
• Workflow reliability — what happens when ordinary inputs, missing data, and recoverable failure disturb those conditions.
• Evidentiary traceability — whether the path from input to claim is recorded well enough to audit.

A stronger model improves the first. It does nothing for the third. Teams that upgrade models to fix a missing audit trail are buying a faster car to solve a missing title deed.

What is known: public benchmarks now include expert-authored research workflow tasks. Terminal-Bench-Science, for instance, is a 70-task benchmark of research workflows authored and reviewed by domain experts across the life, physical, earth, mathematical, and engineering sciences, each completed in a terminal and checked by its own tests. Models score on it. That is a genuine signal that some research-adjacent tasks are tractable end to end.

What that does not tell you: passing a benchmark task is not producing a reproducible scientific result. The benchmark has a defined answer and a verifier. Your result has a reviewer, a replication attempt, and a decade of downstream citation. The evidence boundary matters — public benchmark coverage of scientific workflows is narrow and task-specific, so the absence of a benchmark failure is not evidence of reliability.

Where Assistance Actually Helps: Literature, Analysis, Operations

Do not adopt AI uniformly across the lab. Adopt it by verification cost.

Literature review. AI is strong at recall and triage: finding candidate papers, clustering themes, drafting summaries of what a paper says. It is weak at judgment about what a paper establishes. The failure mode is specific and dangerous — a confident synthesis of sources that were never read closely. The output reads like a literature review because it has the shape of one.

Data analysis. AI-assisted code generation and exploratory analysis work precisely because the artifact is inspectable. The script is one inspectable part of the record — necessary, but not sufficient on its own. If the model writes the analysis, the script must be committed, versioned, and re-runnable from raw inputs, and it must be paired with the model identity, instructions, and retrieval context described below. This is the workflow family where AI assistance and good engineering practice reinforce each other instead of fighting.

Operations. Drafting, formatting, and administrative summarization carry the lowest scientific risk and the highest immediate payoff. Novo Nordisk, for example, has reported using Claude to automate trial report generation and to trim patient documentation timelines from months to minutes — an operational claim about document production, not about scientific validity. That distinction is the whole point. Operations is usually the correct first target because errors are cheap to catch and consequences are contained.

The ordering rule: adopt where the output is checkable against an independent source; defer where the output is itself the evidence.

The asymmetry that trips teams up is that the workflows which look most impressive in a demo — autonomous analysis, multi-step reasoning over a corpus — are often the ones with the worst verification economics. The demo shows the happy path. The audit shows the bill.

Provenance: What a Defensible AI Step Must Record

Provenance here means the recorded chain from input to output: model identity and version, the instruction or prompt, the retrieved sources, the tool calls, and the human decision points in between.

Model version pinning matters more in research than in most software. A silent model update can change an analysis result with no code change and no diff. Your repository looks identical. Your output does not. If you cannot state which model version produced a result, you cannot reproduce it, and you cannot even explain why you cannot.

Distinguish two kinds of provenance, because teams routinely record one and assume they have both:

• Provenance of the artifact — the script, the dataset, the figure. Where it came from and how it was built.
• Provenance of the claim — which sentence in the paper rests on which computation.

The common gap is intermediate retrieval and filtering. Teams log the final output but not the search terms, the inclusion criteria, the papers that were retrieved and then dropped. That intermediate layer is exactly where literature-review errors are introduced. A synthesis built on a filtered set you did not record is unfalsifiable.

A minimum viable record: inputs, procedure, model and version, retrieval set, human review sign-off, and the date. Anything less cannot be audited later by someone who was not there.

Error Checking: Catching the Failures AI Assistance Creates

Generic "human review" catches nothing. Name the failure, then name the check.

Fabricated or misattributed citations. The check is resolving every reference to a real, retrievable source and confirming the cited claim actually appears in it. Not skimming the reference list — resolving each one.

Silent numerical drift. The check is re-running the analysis from raw inputs in a clean environment and diffing the output. Not eyeballing the figure. Diffing it.

Plausible-but-wrong synthesis. The check is requiring a human to state, in their own words, what each cited source contributes. This step cannot be delegated to the same system that produced the synthesis.

Overconfident reasoning on out-of-distribution problems. The check is a pre-registered boundary for which questions the AI step is allowed to touch at all. Decide the boundary before you see the answer, not after.

The general rule underneath all four: the check must be independent of the process that produced the error. Reviewing AI output with the same model is not verification. It is a second opinion from the same witness.

Authorship, Disclosure, and Accountability

Responsibility attaches to the named human author, never to the tool. A model cannot be a corresponding author because it cannot be accountable. This is not a philosophical position; it is the only rule that survives contact with a correction notice.

Separate disclosure of method from disclosure of tooling. Readers need to know which steps were AI-assisted and how they were verified. The specific product name is secondary to the procedure. "We used a language model to draft the initial literature summary; all citations were resolved to primary sources and each source's contribution was verified by two authors" tells a reader something. "AI tools were used" tells them nothing.

Journal and institutional policies are uneven and evolving. The safe default is to document the procedure thoroughly and let the disclosure follow the evidence you already have.

Two failure modes sit at the extremes. Undisclosed assistance that surfaces later — in a reviewer's citation check, in a replication attempt — is a reputational event. Disclosure so vague it conveys no verifiable information is a compliance gesture that protects no one.

One constraint deserves its own line: unpublished data and pre-publication findings should not be sent to systems whose retention and training behavior the team has not verified. That is a confidentiality decision, not a capability decision, and it should be made before the first prompt, not after.

A Staged Adoption Path for Research Teams

Order the stages by verification cost, not by enthusiasm.

Stage one: administrative and drafting assistance. Errors are cheap to catch, consequences are contained. Start here.

Stage two: AI-assisted code and analysis. Gated on version control, environment pinning, and a re-run check before any result leaves the team.

Stage three: AI-assisted literature synthesis. Gated on full citation resolution and human-written contribution statements for every cited source.

Stage four: agentic, multi-step research workflows. Treat as experimental. Keep a human at each state transition. Do not let them touch results that will be published without a full audit.

The promotion rule: a stage advances only when the team can reproduce a prior result from the recorded provenance alone, without asking the person who ran it. If the only person who can explain the output is the person who generated it, you have not built a capability. You have built a dependency.

What to Watch, and What Not to Assume

Separate what is documented from what is forecast. Industry investment in AI-assisted R&D is documented — pharmaceutical companies are announcing partnerships and betting on modeling tools and automated labs to improve pipeline efficiency. Forecasts that this will halve early-stage development timelines and costs within three to five years are forecasts, not results. Vendor statements about shortening research timelines are vendor statements.

Open questions worth tracking: whether benchmark performance on expert-authored research tasks predicts real-world reliability, and whether disclosure norms converge or fragment across fields.

The signals that would change this analysis: reproducible third-party audits of AI-assisted results, journal policies that specify verification requirements rather than disclosure alone, and benchmark coverage that includes failure analysis rather than pass rates only.

For research leaders, the leverage question is not which model to buy. It is whether your lab's provenance and verification practice makes AI assistance auditable. That practice compounds. Model access does not — it depreciates the moment a better model ships, and it ships constantly.

Pick one workflow this quarter. Record the minimum provenance set. Run the re-derivation check with someone who was not in the room. If it survives, expand. If it does not, you have learned something cheap, which is the only kind of research failure worth having.