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The index, its boards and the shelf's pages wrap their prose: server pages with <T>, the boards and the library with t(), and copy held in data with phrase(). Figures read from the runs are values, and the fields lib/kai, lib/tools, lib/coding and lib/tally hand a board are drawn through t(), so they translate once those files emit keys. The shared parts (Chapter, Fold, Stat, Tile, Panel, Mark) translate a string where they draw it and take a node from a server page. figures.spec lets the index import @hanzo/i18n: words in, words out, never a figure.
78 lines
3.8 KiB
TypeScript
78 lines
3.8 KiB
TypeScript
/**
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* /benchmarks/gpqa — the router on GPQA-Diamond, against the field.
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*
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* The board is <Ladder />, read when the page opens from the research API's
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* `enso.gpqa_diamond` runs. Around it is what a reader arriving from a search
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* for "GPQA" needs: what the benchmark is, whose it is, what routing does to
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* it, and what a benchmark score does not establish. This file types no score.
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*/
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import { T } from '@hanzo/i18n/react'
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import { pageMeta } from '@/lib/page-meta'
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import { BENCHMARKS, bench } from '@/lib/benchmarks'
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import { BenchJsonLd } from '@/components/benchmarks/JsonLd'
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import { H2, Meta, More, P, Shell, Spec } from '@/components/benchmarks/kit'
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import { Ladder } from '@/components/benchmarks/Ladder'
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const B = bench('gpqa')!
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export const metadata = pageMeta({
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title: 'GPQA-Diamond routing benchmark',
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description:
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'One set of 198 graduate-level questions through Enso\'s router and single models from other labs, every row a research run with its accuracy, count and what its tokens cost at list prices.',
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path: '/benchmarks/gpqa',
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})
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const MEASURES = ['GPQA-Diamond accuracy', 'what a run\'s tokens cost at list prices']
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export default function Page() {
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return (
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<>
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<BenchJsonLd b={B} measures={MEASURES} />
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<Shell b={B}>
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<Spec b={B} />
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<H2><T>What is measured</T></H2>
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<P>
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<T>{'GPQA-Diamond is the hardest subset of GPQA: 198 multiple-choice questions in biology, physics and chemistry, written by domain PhDs and filtered to the ones that other PhDs get right and skilled non-experts get wrong even with the web open. It is a knowledge benchmark rather than an agentic one — one question, one answer, no tools — which is what makes it useful here: it isolates the choice of model from everything else a system does.'}</T>
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</P>
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<P>
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<T>{'What the router adds is that the choice is per question. Enso reads each one and dispatches it, so a tier is not a model — it is a pool and a willingness to spend. Every row below answers the same 198 questions, which is why a score and what its run cost belong in one table, and why a score quoted without its cost says nothing about the system it came from.'}</T>
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</P>
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<Ladder />
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<H2><T>What this does not establish</T></H2>
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<P>
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<strong>
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<T>A benchmark score is not a capability.</T>
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</strong>{' '}
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<T>
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{'GPQA-Diamond is four-way multiple choice on settled science. It measures what a pool knows and rewards a router that recognises a hard question; it says nothing about whether the answer is reasoned, whether the model holds up over a long autonomous run, or whether it can use a tool. The routing overhead is real work this table does not score either.'}
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</T>
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</P>
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<P>
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<strong>
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<T>A row is a run, as of its date.</T>
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</strong>{' '}
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<T>
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{"Every row went through enso-bench's harness at the tag and on the date it shows; a system is the router or a single model as that run met it, not necessarily a preset the catalog serves today. What a row cost is the tokens its run records at today's list price for its own model id, the router's included, so two rows are priced by one rule."}
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</T>
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</P>
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<More b={B} all={BENCHMARKS} />
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<Meta>
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<T
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values={{
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api: <a href="https://api.hanzo.ai/v1/research/runs?org=hanzo&benchmark=enso.gpqa_diamond">api.hanzo.ai</a>,
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dataset: <a href={B.dataset!.url}>{B.dataset!.name}</a>,
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license: B.dataset!.license,
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}}
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>
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{'every figure a research run at {api} · the dataset: {dataset} under {license}'}
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</T>
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</Meta>
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</Shell>
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</>
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)
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}
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