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MODELS · 3 SOURCES · Google · The Decoder · Google DeepMind

Google releases EmbeddingGemma 2 — open 740M multimodal on-device embedding model

EmbeddingGemma 2 is a 740M-parameter, Apache 2.0–licensed multimodal embedding model from Google that natively maps text, images, audio, video and code into a single embedding space and is optimized for on-device use. It is built on the Gemma 4 architecture, supports modular encoder configurations, an 8K-token context window, Matryoshka Representation Learning for configurable vector sizes, and is available on Hugging Face and Kaggle.

7.0

RESEARCH · 1 SOURCE · Cohere

RCP-nDCG@10: rubric-calibrated nDCG metric for enterprise search

Researchers introduced RCP-nDCG@10 (Rubric-Calibrated Preferences nDCG@10), an evaluation method that uses a calibrated LLM to grade every retrieved document against explicit relevance criteria rather than relying solely on sparse qrels. In a 46-person annotation study the calibrated AI judge increased AUC for predicting human relevance from ~0.65 (qrels) to ~0.91, and the metric is being used to optimize the team's next-generation search models.

7.0