Unverified50% confidenceSolutionExact time
Jev's core mechanism is to extract and number the currently interactive elements into a list, having the model only answer what to do and which number to select, rather than guessing coordinates or writing selectors.
1
Sources
50%
Confidence
Long-term
Relevance
9/22/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSearch reranking is one application scenario for Jev: first use inexpensive algorithms such as BM25 to retrieve candidate results, then use Jev to score the candidates for relevance.70% similarUnverifiedIn a document value extraction test, Jev accurately selected the correct new address from a list of candidates, provided that the candidate list contains the correct answer.65% similarUnverifiedJev offers three question types: Select, Score, and Null/Bool, with each response accompanied by a confidence value.64% similarUnverifiedReranking is typically implemented using cross-encoders, which concatenate the query and candidate documents before feeding them in together; it usually reranks only the Top-50 to Top-100 candidates and finally selects Top-3 to Top-5 to pass to the generative model.59% similarUnverified列表操作节点可以对数组进行条件筛选、获取前N项或后N项、按升序/降序排序等操作58% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/941394API
curl https://kongchang.com/api/v1/knowledge/claims/941394MCP
get_claim(id=941394)