Autoresearch for Dense Retrieval: Test-Time Compute with Frozen Embedding Models

Test-time compute is widely believed to benefit only large reasoning models. We show it also helps small embedding models. Since modern embedding models are distilled from LLM backbones, a frozen encoder should benefit from extra inference compute without retraining. Using an agentic program-search loop spanning 144 generations, we explore 144 candidate programs over a frozen encoder API. The search produces twelve Pareto-optimal programs spanning cost ratios of c=1.2 to 14.7 over the single-pass baseline. The programs are structurally diverse: the search independently rediscovers Rocchio pseudo-relevance feedback, ColBERT-style MaxSim at sentence granularity, reciprocal rank fusion, and the Fisher linear discriminant, all without trainable parameters or external models. Every frontier program improves nDCG@10 over the frozen baseline across all 14 MMTEB retrieval tasks spanning legal, financial, long-document, and general domains.