Test-Time Compute for Frozen Embedding Models through Agentic Program Search
Test-time compute is widely believed to benefit only large reasoning models, leaving small models with nothing to gain. We argue the opposite for dense retrieval, since modern small embedding models are distilled or adapted from large language model backbones and can inherit their latent test-time-compute potential. We ask how much retrieval quality a frozen embedding model gains at inference alone, with no auxiliary model and no parameters trained at deployment. An agentic loop in which a large language model writes programs over a frozen encoder API explores 144 candidates and yields twelve Pareto-optimal programs that trade inference compute for quality across cost ratios from $c{=}1.2...
| Authors | Han Xiao |
|---|---|
| Primary category | cs.LG |
| Published | 12 May 2026 |
| Last updated at source | 30 May 2026 |
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