ELBench is introduced, the first benchmark to evaluate all four requirements (General Capability, Safety and Trustworthiness, Basic Education, and High-Level Cultivation) on the same models under a common protocol, combining curated public sources with newly synthesized safety and cultivation data.
Yi-Lin Jiang, Xiao-Rong Zhu, Fei Tan et al.· 0 citations
This work establishes the existence of a symmetric, monotone pure-strategy equilibrium and shows that performance-based screening remains viable so long as evaluations retain a sufficient creative component, but collapses into an uninformative pooling equilibrium once AI capability grows large enough to crowd out creative effort.
Agentic Data Evolution is proposed, a data-centric framework that organizes synthetic supervision as evolving data snapshots through a closed-loop Observation-Variation-Selection procedure, where a steady-state admission mechanism acts as a quality ratchet that conservatively gates updates for sustained cross-round improvement.
Yang Yu, Yi-Lin Jiang, Zexuan Fei et al.· 0 citations
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