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The title and summary say 'Anthropic authors' and 'Anthropic's authors', which overstates the affiliation. Smallest repair: 'Anthropic researchers and academic collaborators report...' or 'An Anthropic research post reports...'.","excerpt":"Alessandro Chiesa (EPFL, StarkWare), Miranda Christ (MIT, RESI)"},{"dimension":"summary","status":"uncertain","explanation":"The reading leaves out the post's lead framing that Claude produced the zkSNARK improvements and found novel ways to apply cryptographic tools. That is a central idea for this collection, and restoring one attributed sentence would fix it. Several terms that matter for understanding are also unexplained for a general reader: 'committed model weights' (a hidden fingerprint of the weights), 'Lean proofs' (machine-checked formal proofs), 'integer model under greedy decoding', and 'H100 GPU'. The question is substantive and follows from the evidence.","excerpt":"Claude has produced improvements on zkSNARKs for inference, building a system that is substantially faster than prior open-source systems."},{"dimension":"date","status":"pass","explanation":"The originalPublished date of 2026-11-02, taken from the publisher page, matches the feed date and comes before first observation. No earlier submission date is indicated.","excerpt":null},{"dimension":"support","status":"pass","explanation":"The source supports each reported figure: about 110 tokens/s on one H100, proofs of about 19 MB, verification in seconds on a CPU, and roughly 70–95x the GPU time of vLLM. The listed limitations are also stated in the source: the integer model under greedy decoding, the 1,024-token context, architecture not hidden, and no guarantee that the Lean protocol, paper and code match. The source also states the caveat that proofs do not rule out training on the side. 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