3 papers
cs.CL2026
When Knowledge Is Not Free: Cost-Aware Evidence Selection in Retrieval-Augmented Generation
Mingyan Wu, Han Yang, Omer Ben-Porat +1
Retrieval-Augmented Generation (RAG) typically assumes that external knowledge is free, but many high-quality sources are paywalled, licensed, restricted, or otherwise costly to ac…
cs.AI2026
Reasoning Compression with Mixed-Policy Distillation
Han Yang, Mingyan Wu, Bailan He +4
Reasoning-centric large language models (LLMs) achieve strong performance by generating intermediate reasoning trajectories, but often incur excessive token usage and high inferenc…
cs.CV2026
EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations
Yongrui Heng, Chaoya Jiang, Han Yang +2
Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degradation as model predictions…