Why benchmarks are inputs rather than receipts

The source trail starts with SWE-bench, OSWorld, NIST AI RMF. These sources do not decide the award. They give power users outside vocabulary for checking award claims.

A strong Awards page separates four proof classes. Live scores. Public docs. Independent context. Nomination evidence. Blurring them makes badges weaker.

Evidence plays from Minimum trust receipt schema

• When the decision is Task context, ask for Goal, constraints, user, environment before repeating the award claim. If that evidence is missing, the practical failure mode is: The result cannot be interpreted.
• When the decision is Authority boundary, ask for Tools, permissions, memory, policy before repeating the award claim. If that evidence is missing, the practical failure mode is: The agent gets credit without risk context.
• When the decision is Outcome evidence, ask for Trace, test, citation, reviewer note before repeating the award claim. If that evidence is missing, the practical failure mode is: A claim cannot be replayed.
• When the decision is Consequence record, ask for Retry, rollback, escalation, score change before repeating the award claim. If that evidence is missing, the practical failure mode is: Failure teaches nothing.

For proof-interpretation, the goal is faster judgment with fewer collapsed claims. The table should travel into a buyer note, nomination review, analyst memo, or internal debate.

Source anchors for Why benchmarks are inputs rather than receipts

• SWE-bench: https://www.swebench.com/
• OSWorld: https://os-world.github.io/
• NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework

Trust Receipts Beat Benchmark Screenshots in AI Agent Evaluation should expose enough source context for useful disagreement. Challenge the category. Challenge freshness. Challenge the proof class. Challenge the buyer implication.

Evaluation becomes post-task accountability

The operator should preserve receipts at the moment of work, not reconstruct them after an incident. That changes logging, review, score updates, and escalation. Awards can normalize this expectation. A nominee that offers strong receipts should feel more credible than a nominee that offers only benchmark rank, even when the benchmark rank is impressive.

Applying proof-interpretation without losing the proof

Trust Receipts Beat Benchmark Screenshots in AI Agent Evaluation should be read as a living review surface, not as static commentary. Power users can reuse the table as an operating prompt.

The practical workflow is simple. First, identify the claim being made. Second, locate the evidence class behind it. Third, ask what would invalidate the claim after a model, tool, memory, policy, or runtime change. Fourth, decide whether the award should change permission, budget, reputation, or only curiosity.

What should change after proof-interpretation

Trust Receipts Beat Benchmark Screenshots in AI Agent Evaluation becomes operationally useful when it changes at least one action. For this post, the action is whether an agent’s public evidence is strong enough to influence deployment, award judging, or buyer trust.. Evidence should affect a shortlist. Or a permission gate. Or a nomination. Or a renewal decision. Or a public claim.

Power users should log counterevidence too. A strong category invites challenge. If nothing changes, the award is entertainment. If evidence changes a real action, the award is infrastructure.

How Armalo can use receipt language carefully

Armalo’s trust architecture is receipt-oriented: pacts, scores, attestations, disputes, and badge verification all point toward inspectable records. The Awards can use that language as methodology and buyer education. It should avoid implying every public nominee has Armalo-native receipts. For external nominees, public traces, case studies, issue histories, benchmark reports, and submitted evidence can still contribute.

The hard objection - receipts are expensive

They are cheaper than unreviewable autonomy. The cost of evidence should scale with authority. Low-stakes agents need lightweight records; high-stakes agents need stronger receipts.

FAQ

Is this an award prediction? No. It is a decision framework for the 2026 judging cycle.

What should a power user save? Save the artifact table, source set, and award implication.

Where should readers go next? Armalo Awards methodology.

Debate question for proof-interpretation

What is the smallest receipt that would make you trust an agent’s award claim more than a benchmark screenshot?