Jev vs GPT-4.1 on a synthetic survey
runs Jev and GPT-4.1 as the same 300 synthetic respondents over 24,596 paired Twin-2K-500 cells under criteria fixed in advance, finding that asking a yes/no item as `Noul` rather than `Choice` moves the result more than the gap between the two models, at a thirty-fourth of the cost. Write-up: [jjd-lab.github.io](https://jjd-lab.github.io/jev-synthetic-survey/)
它做什么判断
CHOICE
Picks one option from a fixed set
SCORE
Rates against ordered levels
NOUL
Answers a yes / no question
Inferred from this project's own one-line summary in the community list — it reads as a NOUL decision. Read the source to confirm.
NOUL 调用长什么样
这是该提问类型的通用骨架,不是这个项目的真实代码。
from typesafe import TypeSafe
ts = TypeSafe()
result = ts.evaluate(
state=tool_call,
questions={
"is_risky": {
"type": "noul",
"instructions": "Could this call delete or overwrite user data?",
}
},
)
if result["is_risky"] and result["is_risky_confidence"] > 0.6:
escalate_to_human(tool_call)更多评测与基准项目
Jev Web Analyzeranalyzes a public SaaS landing page as clean Markdown and asks Jev ten bounded `Choice` questions about first-visit understanding, returning inspectable findings for the first change to make.Jev Playgroundbenchmarks Jev against Luna, Haiku, and Gemini at choosing validated legal moves in explicit-state games, scoring decision quality and consistency across a sequence of moves.Jev vs Mistral and Gemini for event validationhead-to-head test of Jev against Mistral Small and Gemini Flash-Lite at validating local event listings.jev-research-evalreproducible eval harness plus field note for Jev Ultrafast research-browser tasks, with QC'd cases, a suite runner, and a report generator.