Benchmarks & research
Operator lab for TypeSafe Jev. Chess Arena, closed-schema booths, Stockfish HUD for review only.
UPSTREAM = os.environ.get("TYPESAFE_UPSTREAM", "https://api.typesafe.ai/v1/systemone")The link points at the commit we read, so the line number still holds.
These question sets are lifted from this project's source exactly as written.
from web/home.js:37
whatchoiceWhat is this page describing?
broadcast_lab — A lab for closed-schema Jev callschess_engine — A chess engine like Stockfishchatbot — A chat model that writes prosepolicy_gate — A safety policy enforcerchess_match — A live chess matchimport { experimental_evaluate as evaluate } from 'ai';
const { answers } = await evaluate({
model: 'typesafe-ai/jev',
state,
questions: {
what: { type: 'choice', instructions: 'What is this page describing?',
criteria: { broadcast_lab: 'A lab for closed-schema Jev calls', chess_engine: 'A chess engine like Stockfish', chatbot: 'A chat model that writes prose', policy_gate: 'A safety policy enforcer', chess_match: 'A live chess match' } },
},
});fnchoiceWhich function should run?
search_tickets — Look up ticketscreate_refund — Issue a refundpage_oncall — Page a humannone — No toolreasonchoiceIf refunding, which reason enum? Use none if not a refund.
duplicate_charge — Charged twiceservice_credit — Goodwill creditchargeback_avoid — Defensive refundnone — Not a refundimport { experimental_evaluate as evaluate } from 'ai';
const { answers } = await evaluate({
model: 'typesafe-ai/jev',
state,
questions: {
fn: { type: 'choice', instructions: 'Which function should run?',
criteria: { search_tickets: 'Look up tickets', create_refund: 'Issue a refund', page_oncall: 'Page a human', none: 'No tool' } },
reason: { type: 'choice', instructions: 'If refunding, which reason enum? Use none if not a refund.',
criteria: { duplicate_charge: 'Charged twice', service_credit: 'Goodwill credit', chargeback_avoid: 'Defensive refund', none: 'Not a refund' } },
},
});from web/lab/cheat/cheat.js:188
verdictchoiceClassify the act relative to this couple's setup. Use community_rule only if the setup named a religious or family boundary. Use depends_on_couple if exclusive people would split and no rule was named. Do not moralize past the labels.
not_cheating — No extra-pair betrayal relative to this setupmicro — Small extra-pair attention or concealment, short of an affairemotional_affair — Secret emotional primary bond with someone elsesexual_affair — Sexual or clearly sexual-digital contact that breaks exclusivitydepends_on_couple — Reasonable exclusive couples would split on thiscommunity_rule — A breach of a named religious, family, or subculture rule more than of sex-as-suchsecrecytrue/falseConcealment from the partner (or from the named family rule-holders) is doing real work in this case.
harmscoreTypical trust harm if this stays hidden, for this setup. Not a legal finding.
0 — None1 — Sting2 — Break3 — Blow-upimport { experimental_evaluate as evaluate } from 'ai';
const { answers } = await evaluate({
model: 'typesafe-ai/jev',
state,
questions: {
verdict: { type: 'choice', instructions: 'Classify the act relative to this couple\'s setup. Use community_rule only if the setup named a religious or family boundary. Use depends_on_couple if exclusive people would split and no rule was named. Do not moralize past the labels.',
criteria: { not_cheating: 'No extra-pair betrayal relative to this setup', micro: 'Small extra-pair attention or concealment, short of an affair', emotional_affair: 'Secret emotional primary bond with someone else', sexual_affair: 'Sexual or clearly sexual-digital contact that breaks exclusivity', depends_on_couple: 'Reasonable exclusive couples would split on this', community_rule: 'A breach of a named religious, family, or subculture rule more than of sex-as-such' } },
secrecy: { type: 'boolean', instructions: 'Concealment from the partner (or from the named family rule-holders) is doing real work in this case.' },
harm: { type: 'score', instructions: 'Typical trust harm if this stays hidden, for this setup. Not a legal finding.',
criteria: ['None', 'Sting', 'Break', 'Blow-up'] },
},
});from web/lab/debate/debate.js:124
targetchoiceWhat did the counter aim at?
claim — The stated claimpremise — A supporting reasonperson — The speakermotive — Why they might say itdefinition — The meaning of a wordside_issue — A different topicevidence — The data or sourcequalityscoreHow well does the counter actually answer the original claim?
0 — Miss1 — Glance2 — Partial3 — Meets iton_pointtrue/falseThe counter addresses the original claim rather than the speaker or a side issue.
import { experimental_evaluate as evaluate } from 'ai';
const { answers } = await evaluate({
model: 'typesafe-ai/jev',
state,
questions: {
target: { type: 'choice', instructions: 'What did the counter aim at?',
criteria: { claim: 'The stated claim', premise: 'A supporting reason', person: 'The speaker', motive: 'Why they might say it', definition: 'The meaning of a word', side_issue: 'A different topic', evidence: 'The data or source' } },
quality: { type: 'score', instructions: 'How well does the counter actually answer the original claim?',
criteria: ['Miss', 'Glance', 'Partial', 'Meets it'] },
on_point: { type: 'boolean', instructions: 'The counter addresses the original claim rather than the speaker or a side issue.' },
},
});