{"id":"2610.29832","title":"S-EDL: Eliciting Self-Evidence from Sequence Likelihoods for Semantic Calibration of LLMs","url":"https://codeofpaper.com/paper/2610.29832","arxiv_url":"https://arxiv.org/abs/2610.29832","published":"2026-10-21","code":"official","best":{"full_name":"sandylaker/s-edl","url":"https://github.com/sandylaker/s-edl","tier":"official","stars":1,"license":"MIT","framework":null,"language":"Python","last_commit":"2026-10-22T01:07:48"},"most_starred_community":null,"implementations":[{"full_name":"sandylaker/s-edl","url":"https://github.com/sandylaker/s-edl","tier":"official","stars":1,"license":"MIT","framework":null,"language":"Python","last_commit":"2026-10-22T01:07:48"}],"possible_matches":0}