arXiv:2605.23572v1 Announce Type: cross Abstract: In the competitive landscape of sponsored search, balancing retrieval quality with production latency is a critical challenge. While large retrieval models based on Small Language Models (SLMs) such as Qwen3-Embedding-4B/8B set strong upper bounds on public benchmarks, their deployment in high-throughput, latency-sensitive environments remains impractical. In this paper, we present HARNESS-LM (HLM), a three-phase training framework for transferring the capabilities of large-scale retrievers into compact, cost-efficient models. The approach comp
The proliferation of SLMs creates a need to adapt them for practical, low-latency applications, addressing a key deployment challenge in a competitive market.
This development can significantly improve the efficiency and cost-effectiveness of AI model deployment in real-world, high-throughput systems, making advanced retrieval more accessible.
The ability to deploy powerful retrieval models based on SLMs without incurring prohibitive latency or cost changes the viability of advanced AI in time-sensitive applications.
- · Ad-tech companies
- · E-commerce platforms
- · AI infrastructure providers
- · Consumers (better search results)
- · Companies relying on less efficient retrieval systems
- · High-latency model developers
Improved performance and cost efficiency for sponsored search and similar retrieval tasks.
Increased adoption of compact AI models across various industries due to better deployment economics.
Further democratization of advanced AI capabilities, potentially leading to more specialized and embedded AI applications.
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Read at arXiv cs.LG