Benchmark evidence assessment
Examine what a benchmark measures, what it misses, and when a comparison is justified. Coverage, settings, provenance and uncertainty belong beside the score.
MEKreflect · AI evaluation & policy research
Evidence for
better AI decisions.
MEKreflect aims to make AI evaluations more useful for real decisions. We are developing methods to assess benchmark evidence and help teams turn it into testable operational policies.
Our ambition is to become a benchmark for evaluating benchmarks. We are at the beginning: reviewing benchmark evidence, investigating one case and preparing a first model test.
Policies require evidence within a stated scope.
Our purpose · Three pillars
Understand the measure.
Examine the behaviour.
Test the policy.
Evaluation design and reproducibility
Investigate AI behaviour through documented cases, controlled tests and inspectable scoring. Define what would count as evidence before making a claim.
Evidence-to-policy translation
Turn a specific risk into a policy requirement, an enforcement mechanism and an acceptance test. Examine both boundary violations and unnecessary blocking.
First benchmark evidence output · Documentary review v0.1
Source cutoff · 18 October 2026
What do agent benchmarks
establish about authorization?
A source-linked review of AgentDojo, AgentHarm and original τ-bench examines different constructs, scoring boundaries and policy implications.
In original τ-bench, the authors note that an outcome reward can pass even when confirmation is omitted. Our review traces that limitation into a proposed policy requirement. It is documentary analysis; no benchmark has been rerun.
Our first investigation
One continuing case · Updated 18 October 2026
Case 01 · Source review & investigation
When agents work
around a barrier.
An agent-fleet report motivates a bounded policy question: when outside help becomes available, does an agent distinguish what it can do from what the user authorized?
This synthetic authorization question is one starting point for our behaviour and policy work. It does not establish the validity of benchmarks or general safety of agents.
Benchmark assessment · Intended work
Ask what the score
can support.
A score needs a task population, a measurement method and a defensible comparison. Our planned evidence reviews will ask which decisions a benchmark can inform—and where the evidence runs out.
We intend to examine coverage, elicitation and tool settings, source provenance, uncertainty and reproducibility before combining or comparing results. No MEKreflect meta-benchmark, composite ranking or certification has been validated.
The benchmark-assessment directionEvidence-to-policy translation · Intended work
Make a rule
testable.
A policy should say what is permitted, when clarification is required and what useful work must remain possible. A test should distinguish agent judgment from a gate preventing an action.
The first authored protocol examines helper authorization with fictional data and local simulated effects. The model test remains proposed. A scripted software check is not evidence that a policy works with AI models.
From a policy question to a testThe standard for a claim
Keep the evidence
and its limits together.
- 01 · Source reviewWhat was reported?
Attribute the account and retain its unknowns.
- 02 · InvestigationWhat can we establish?
Separate evidence, interpretation and open questions.
- 03 · Test proposalWhat would distinguish explanations?
Freeze controlled conditions before execution.
- 04 · Follow-upWhat changes the record?
Report outcomes and limits after a test has run.
Third-party work retains its authorship. Proposals, source analysis, software checks and model results are different evidence states. Referencing a research organization does not imply affiliation or equivalent access.
Read the dated references