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cs.IR2026
Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders
Hyojung Han, Jongmin Kim, Seung-Hun Jeon
Mixed-precision post-training quantization needs a per-module sensitivity signal; for a text embedder the obvious one -- the retrieval quality a module costs when quantized -- need…
cs.IR2026
On-Device Commercial Intent Retrieval Under Size, Latency, and Privacy Constraints: A 3 MiB Retrieval System with Typed Egress Boundaries
Hyojung Han
We study commercial intent inference that runs entirely on the user's device, under three constraints frozen before the work began: the downloaded payload under 3 MiB, Tier-0 infer…
cs.IR2026
Where Post-Training Quantization Breaks Text Embedders: A Measured Map Across Four Embedder Families
Hyojung Han
Weight-only post-training quantization is the cheapest way to shrink a retrieval embedder, and the received advice for applying it -- protect the embedding table, allocate bits by…