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

Official Schedule Context

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.

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