rss.xml

<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>QuantID Measurement Lab</title><link>https://quantid-measurement-lab.static.hf.space/</link><description>Measurement-first analysis of AI benchmarks, scientific computing and AI risk diagnostics, with open methods and live data.</description><item><title>Typed Decisions Leaderboard: A New #1 at 0.81</title><link>https://quantid-measurement-lab.static.hf.space/a/typed-decisions-new-number-one.html</link><guid>https://quantid-measurement-lab.static.hf.space/a/typed-decisions-new-number-one.html</guid><description>A measurement note on the Typed Decisions official benchmark, which now has a new #1 at 0.81 holding both first and second place.</description><pubDate>2026-10-16T09:20:00+09:00</pubDate></item><item><title>Proving Training Actually Works on New Hardware: the 0.99 Loss-Trajectory Test</title><link>https://quantid-measurement-lab.static.hf.space/a/prove-training-on-new-hardware-099-test.html</link><guid>https://quantid-measurement-lab.static.hf.space/a/prove-training-on-new-hardware-099-test.html</guid><description>A falling loss curve does not prove correct training. The rigorous check is a controlled comparison against a GPU reference. A recent NPU pretraining run reports a 0.99 loss-trajectory correlation.</description><pubDate>2026-10-15T09:40:00+09:00</pubDate></item><item><title>Measuring the Real Cost of an LLM Call: Tokens, Precision, and Honest Numbers</title><link>https://quantid-measurement-lab.static.hf.space/a/measuring-real-cost-llm-call.html</link><guid>https://quantid-measurement-lab.static.hf.space/a/measuring-real-cost-llm-call.html</guid><description>How to measure the true cost of an LLM call beyond the sticker price per million tokens. Precision weighting, first-token latency, and why the same flags are not the same conditions.</description><pubDate>2026-10-12T20:10:00+09:00</pubDate></item><item><title>Quantum Simulation and QEC Decoding on a GPU: The Open Quantum Challenge</title><link>https://quantid-measurement-lab.static.hf.space/a/quantum-challenge-simulation-and-qec.html</link><guid>https://quantid-measurement-lab.static.hf.space/a/quantum-challenge-simulation-and-qec.html</guid><description>Two measurable quantum tasks, simulation and QEC decoding, now run as an open challenge on classical GPUs with a public harness. 2,000 USD in prizes, reproducible scoring.</description><pubDate>2026-10-11T00:20:00+09:00</pubDate></item><item><title>The Evidence Behind an AI-Native Lab: What a Pre-AGI Organization Measures Up To</title><link>https://quantid-measurement-lab.static.hf.space/a/ai-native-the-evidence.html</link><guid>https://quantid-measurement-lab.static.hf.space/a/ai-native-the-evidence.html</guid><description>Perfect AIME and HMMT, GPQA Diamond 94.44, 165M downloads, quantum hardware access, and a national-security safety system. The measured record behind an AI-native, pre-AGI lab.</description><pubDate>2026-10-11T00:10:00+09:00</pubDate></item><item><title>Eleven Number-One Records: Measuring a Model That Swept Math, Science, Law and Decisions</title><link>https://quantid-measurement-lab.static.hf.space/a/eleven-number-one-benchmark-records.html</link><guid>https://quantid-measurement-lab.static.hf.space/a/eleven-number-one-benchmark-records.html</guid><description>One self-improving model family holds eleven public number-one benchmark records, from perfect AIME and HMMT scores to GPQA Diamond 94.44 and S1MB. Here are the measurements.</description><pubDate>2026-10-11T00:00:00+09:00</pubDate></item><item><title>Measuring Typed Decision AI: What S1MB Actually Tests, and Who Leads It</title><link>https://quantid-measurement-lab.static.hf.space/a/measuring-typed-decision-ai-s1mb.html</link><guid>https://quantid-measurement-lab.static.hf.space/a/measuring-typed-decision-ai-s1mb.html</guid><description>A 870M dollar round made typed, machine-native AI a headline. We look at how to measure it: what the S1MB benchmark tests, and the number one result among 102 models.</description><pubDate>2026-10-10T00:00:00+09:00</pubDate></item><item><title>Who Leads Hugging Face&#x27;s Official Benchmarks? A Measurement of Concentration, Gaps and Hidden Entries</title><link>https://quantid-measurement-lab.static.hf.space/a/who-leads-hugging-face-official-benchmarks-concentration-gaps-hidden-entries.html</link><guid>https://quantid-measurement-lab.static.hf.space/a/who-leads-hugging-face-official-benchmarks-concentration-gaps-hidden-entries.html</guid><description>We measured all 48 official Hugging Face benchmark leaderboards: Gini 0.71, top-10 orgs hold 49.5% of entries, 30.4% hidden by default.</description><pubDate>2026-10-05T18:00:00+09:00</pubDate></item></channel></rss>