SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams

ArXi:2601.09515v2 Announce Type: replace Due to the dynamically evolving nature of real-world query streams, relevance models struggle to generalize to practical search scenarios. A sophisticated solution is self-evolution techniques. However, in large-scale industrial settings with massive query streams, this technique faces two challenges: (1) informative samples are often sparse and difficult to identify, and (2) pseudo-labels generated by the current model could be unreliable.