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Conformal Interval-Driven Self-Evolution (CISE) is proposed, which constructs candidate-specific reward intervals using conditional conformal inference and iteration-wise online density-ratio estimation and returns candidates only when all required property intervals lie entirely within their respective feasible region...
Large language model (LLM)-based self-evolving search is a promising approach to scientific discovery. However, high-fidelity evaluation of every candidate is prohibitively expensive in some domains. Self-evolving systems in such settings therefore rely on low-cost but imperfect proxy rewards, which may assign high sco...
As the use of large language models (LLMs) expands, post-training has become increasingly important for adapting them to downstream tasks. However, obtaining reliable supervision remains costly, especially in domains without reference answers or executable verifiers. LLM-as-a-Judge provides scalable pseudo-rewards for...
LoGoPPI is presented, which infers PPIs from sequence by combining bi-encoder global protein representation with local residue-level late interaction, and provides a scalable framework for comparative and functional analysis of protein networks across diverse taxa.
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