PACE

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model2 events

papersTODAY 04:00 UTC

PACE Introduces Progressive Angular-to-Norm Contrastive Embedding for Multimodal Models

Researchers propose PACE, a training method for multimodal embedding models that shifts the contrastive objective from an angular (cosine-based) formulation toward a norm-based one over the course of training. The approach is presented as an alternative to standard cosine contrastive objectives, which the authors say offer stable but potentially limited training dynamics. The work is posted as an arXiv preprint in the cs.AI and cs.LG categories.

multimodal/embeddings/contrastive-learning/arxiv

papersSEP 10 04:00 UTC

PACE framework targets perceived latency in retrieval-augmented dialogue serving

Researchers introduce PACE, a serving framework for retrieval-augmented dialogue systems that defines Perceived Time-to-First-Response as a quality-of-experience metric and optimizes it subject to quality and cost limits. The approach combines cascaded service routing with filler response control to reduce how long users wait before receiving an initial answer. The work extends prior research on cascading and semantic caching by treating perceived latency as an explicit optimization objective.

research/rag/latency/serving