3 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
Evaluating Embedding Models and Pipeline Optimization for AI Search Quality
Philip Zhong, Kent Chen, Don Wang
We evaluate the performance of various text embedding models and pipeline configurations for AI-driven search systems. We compare sentence-transformer and generative embedding mode…
MosaicDoc: A Large-Scale Bilingual Benchmark for Visually Rich Document Understanding
Ketong Chen, Yuhao Chen, Yang Xue
Despite the rapid progress of Vision-Language Models (VLMs), their capabilities are inadequately assessed by existing benchmarks, which are predominantly English-centric, feature s…
EmbeddingGemma: Powerful and Lightweight Text Representations
Henrique Schechter Vera, Sahil Dua, Biao Zhang +86
We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledg…
BigBang-Proton Technical Report: Next-Word-Prediction is Scientific Multitask Learner
Hengkui Wu, Liujiang Liu, Jihua He +23
We introduce BigBang-Proton, a unified sequence-based architecture for auto-regressive language modeling pretrained on cross-scale, cross-structure, cross-discipline real-world sci…
The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
Barbara Oakley, Michael Johnston, Ken-Zen Chen +2
In the age of generative AI and ubiquitous digital tools, human cognition faces a structural paradox: as external aids become more capable, internal memory systems risk atrophy. Dr…