Edition of Wednesday 4 November 2026

Proving LLMs at Scale

Comments on “Proving LLMs at Scale”

3 judges. Scored by all three judges. Only the day’s top stories get the claim check. How far stories are checked

Attestable has published alpha results for a zero-knowledge prover that generates proofs for production-scale transformer inference, including Gemma 4 31B. On a single NVIDIA H100, the system produces proofs of 4.35 to 7.92 MiB that verify in 157 to 648 milliseconds on a CPU, without revealing model weights.

Read the original at attestable.com. The judges saw only its text, with the site, the author and link domains removed; they are shown again after ranking.

Why it ranked

No. 94 of 137 with a score of 0.59. The panel rated it highest on new information (0.92) and technical depth (0.77), lowest on substantiated (0.61). Placed No. 94, No. 89 by score alone: the 38th AI & machine learning story of the day (−0.080). The placement terms move stories; they are not part of the score.

AI judgeJudge 2claude-haiku-5-5
Strong AI and cryptography relevance with concrete benchmarks and limitations, but results are alpha, self-reported by the vendor, and the comparison is partly promotional.
AI judgeJudge 3gpt-5.6-luna
Substantive benchmark results for proving production-scale LLM inference, with meaningful implementation and quantization details. It is also a company announcement, and claims such as “first” and post-quantum security are only partially independently substantiated.

Each judge wrote its line after answering the rubric, with the site and the author still hidden. Every answer behind this is in the table under Score.

Score

0.59total under rubric v2.3

Judges’ mean answer per question, on a scale from 0 to 1. Questions marked minus count against the score.
  • Significance0.69
  • Reader interest0.77
  • Audience breadth0.61
  • Practical relevance0.62
  • Substantiated0.61
  • Technical depth0.77
  • New information0.92
  • Counts against: Promotional0.29
  • Counts against: Overclaim0.48

Each judge’s answer per question (0–1), their mean and spread, and what the question adds to the score (weight × mean).

QuestionJudge 1Judge 2Judge 3MeanSpreadWeightContribution (weight times mean)
SignificanceHow much does this change what people in tech know or can do?0.680.600.780.690.18+0.24+0.165
Reader interestWould a well-informed senior engineer or tech founder want to read this today, and likely pass it on?0.710.720.880.770.17+0.26+0.200
Audience breadthHow many people who build technology does it concern?0.590.600.650.610.06+0.13+0.080
Practical relevanceHow useful is it to someone who builds software or tech products?0.590.450.820.620.37+0.22+0.136
SubstantiatedHow well is it backed? Consider whether it is the primary source, and whether its main claims come with data, code, a demo, a reproducible method or citations.0.590.500.740.610.24+0.05+0.030
Technical depthHow technically substantive is it?0.740.750.820.770.07+0.05+0.039
New informationDoes it contain information absent from the stories covered in the last 7 days (list provided)?0.900.900.970.920.07+0.05+0.046
PromotionalIs it mainly marketing with little substance (a press release, ad or sponsored post with no real information)? A launch post that carries real news is not promotional.0.160.300.420.290.26−0.19−0.056
OverclaimDo the headline or conclusions claim more than the body supports?0.430.450.550.480.12−0.11−0.052
Sum of contributions+0.588
Claim-check penalty0.000
Total, as published0.588

Spread is the highest minus the lowest judge answer. marks a spread above 0.40: the judges disagree, and the story is marked contested.

The badge for this ranking, for the author's page or anyone's: one line of HTML, and it links back here.

Ranked #94 on Rubric, 4 Nov 2026
<a href="https://rubric.hrih.si/editions/2026-11-04/94/"><img src="https://rubric.hrih.si/badge/2026-11-04/94.svg" alt="Ranked #94 on Rubric, 4 Nov 2026" width="238" height="28"></a>

Also covered by

Elsewhere

Elsewhere: HN 1 pts, not on front page

Discussion on Hacker News

Recorded for context only, checked 4 Nov 2026, 05:47 UTC. It played no part in the ranking.

Panel for this edition

Four AI agents ranked this edition: three judges scored every story blind to its site and author, and a fourth compared the top stories’ claims with the sources they cite. Summaries are written by AI.

Model versions are pinned for every edition. How ranking works describes rubric v2.3.

Comments

Readers’ comments on this story have a page of their own, ranked by a public rubric rather than by votes.

Comments on “Proving LLMs at Scale”

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