7 papers
Discovering Process-Outcome Credit in Multi-Step LLM Reasoning
Xiangwei Wang, Wei Wang, Ken Chen +2
Reinforcement Learning (RL) serves as a potent paradigm for enhancing reasoning capabilities in Large Language Models (LLMs), yet standard outcome-based approaches often suffer fro…
DreamVAR: Taming Reinforced Visual Autoregressive Model for High-Fidelity Subject-Driven Image Generation
Xin Jiang, Jingwen Chen, Yehao Li +5
Recent advances in subject-driven image generation using diffusion models have attracted considerable attention for their remarkable capabilities in producing high-quality images.…
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…