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Deploy-first · Scientist-driven

Can AI agents verify good science?

SciVeri-Bench evaluates scientific critiques, evolving rubrics, and judgments of research quality against those of domain scientists.

Read the project overview

What SciVeri-Bench evaluates

Scientific outcomes

Papers, experiments & research artifacts

AI evaluator

Judgments to evaluate

Domain scientist

Reference judgments

Can agents match scientists’ judgments on open-ended, novel tasks?
Verification tasks
3
Task-proposal opt-ins
16
Contributor institutions
11
Countries represented
5

Three scientific verification tasks

Agents examine papers and research artifacts. Scientists provide the reference judgments across multiple rounds.

Verification task 01

Where does the science fall short?

Identify weaknesses in an AI-generated scientific outcome and support each criticism with evidence from the paper or research artifacts.

Input
An AI-generated paper and its research artifacts
Output
Scientific weaknesses grounded in the paper and research artifacts
Explore this verification task

The verification task

AI outcome
AI evaluator
Weaknesses
Compared with the scientist’s critique
Illustrative review example

Research outcome

A paper reports a promising material using a single simulation setting.

Critique response

The claim is broader than the evidence: test sensitivity to simulation conditions and compare against an established baseline.

An example of the task format; no benchmark result is implied.

Built with scientists

Propose a question from your field, review agent outcomes, and refine the scientific rubric. Task managers help prepare and run the experiments through OpenReview.

For open-ended research, evaluation evolves with the evidence. Scientists identify weaknesses, revise criteria, and compare the value of different findings.

What makes a good task?

Open-ended
Explain why a single scalar score cannot adequately capture the quality of the science.
Novel
Propose a question with the significance and research potential of work in a Nature-family journal.
Feasible
Describe the data, tools, compute, and practical scope, from a tractable first experiment to a longer-term moonshot.

Our scientific community

Meet the contributors
  • Brookhaven National Laboratory
  • Indian Institute of Technology Delhi
  • Institut de Biologie de l'École Normale Supérieure (IBENS)
  • Korea University
  • Massachusetts Institute of Technology
  • Shell
  • Technical University of Denmark
  • University of Colorado Boulder
  • University of Florida
  • University of Maryland, Baltimore County
  • University of Minnesota

Affiliations reported by task-proposal volunteers · Roster as of Oct 1, 2026. Opt-ins do not indicate accepted benchmark tasks.