5 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
As LLM agents undertake increasingly complex, long-horizon tasks, verifying their outputs becomes increasingly challenging. We study how verification capability can be strengthened with a fixed base model, without access to reference answers or grading rubrics at test time. Repeated sampling yields multiple rollouts th...
Conditional Progressive Pruning (CPP) is proposed, a lightweight pruning framework that fully exploits multi-round MAD and is the first to fully outperform consistency methods.
This work proposes XConf (eXperiential Confidence): estimating confidence together with the model's accumulated experience, and sees experiential confidence estimation as a new paradigm for future general-purpose confidence estimation.
It is shown that raw global calibration metrics are not robust for cross-model comparison, and that fair calibration comparison requires accuracy-aware evaluation, and proposed ACE, an accuracy-controlled evaluation framework, is proposed.
This work conducts the first large-scale, systematic studies of multilingual calibration across six model families and over 100 languages, revealing that non-English languages suffer from systematically worse calibration.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
