For evidence-synthesis tools, start with retrieval and interpretation tasks [1]. For computational research agents, prioritise executed analyses and verifiable outputs [2]. For broader AI co-scientists, combine these with expert assessment of assumptions, experimental choices and uncertainty [3].

Interpret scores correctly

“LifeSciBench is intended to measure model performance on realistic, self-contained life-science tasks, but it does not directly measure the impact of AI systems in live research environments.” — LifeSciBench preprint [3].

A leaderboard result is meaningful only when its evaluation conditions are understood [1]–[3]. LABBench2 reports substantial, variable improvements when models receive web search and code-execution tools [1]; BixBench uses a defined agent framework and computational environment [2]. Consequently, the assessed system includes its tools and execution setup. Not just the underlying language model [1], [2].

Before comparing vendors, request:

• Exact benchmark version, dataset subset and evaluation date [1]–[3].

• Model version, agent framework and available tools [1]–[3].

• Retrieval access, execution environment and inference budget [1]–[3].

• Scoring method, task denominator and handling of failed runs [1]–[3].

• Whether results reflect one attempt, repeated attempts or selected best runs [1]–[3].

• Access to outputs, logs and grading evidence [1]–[3].

Distinguish partial-credit scores from completed-task success [3]. LifeSciBench defines a task pass as achieving at least 70% of its task-specific rubric points; its normalised rubric score measures partial credit [3]. Neither metric should be relabeled as “scientific accuracy,” and that 70% threshold is not a universal procurement standard [3].

Historical results also need context [2]. The original BixBench evaluation reported 17% open-answer accuracy for Claude 3.5 Sonnet and 9% for GPT-4o under its specific setup [2]. These figures describe those evaluated configurations. Not current Claude Science performance or today’s overall model capabilities [2].

Run an internal capability assessment

The following is a proposed HealthyData.Science buyer framework, not an independently validated benchmark.

1. Define the micro-workflow. Specify whether the system must retrieve evidence, propose an experiment, execute an analysis or reproduce a result.

2. Define acceptable outputs. Require the relevant citations, calculations, code, assumptions, caveats and artifacts before testing.

3. Assemble held-out tasks. Use representative internal problems that were not used to configure or tune the system.

4. Establish a baseline. Compare with the existing scientist-led workflow, including its review burden and completion time.

5. Freeze evaluation conditions. Record versions, permissions, data access and computational budgets.

6. Review outputs independently. Use domain experts and executable checks where possible; adjudicate consequential disagreements.

7. Repeat representative tasks. Examine consistency rather than accepting a single successful demonstration.

8. Decide by workflow. Approve only the tested use cases and define required human oversight.

For example, an agent assessing differential gene expression should not pass merely because its final narrative sounds plausible. Your rubric should require appropriate quality control, justified statistical methods, correctly identified comparisons, reproducible outputs and conclusions supported by the analysis.

Explore the Assessment Framework

Select each step below to explore the assessment process, then use the illustrative checklist to record criteria confirmed through supporting evidence.

Complement this process with the scorecard in Table 2 below, which separates scientific quality from operational efficiency.