5 papers
Echo-N1: Affective RL Frontier
Naifan Zhang, Ruihan Sun, Ruixi Su +9
The LLM field has spent a year perfecting RL for tasks machines already excel at, math, code, and deterministic reasoning, while completely sidestepping the domain that actually de…
ASAP: an Agentic Solution to Auto-optimize Performance of Large-Scale LLM Training
Yuran Ding, Xinwei Chen, Xiaofan Zhang +1
Optimizing large-language model (LLM) training on distributed domain-specific accelerator systems presents significant challenges due to its complex optimization space. Existing op…
Rethinking RoPE Scaling in Quantized LLM: Theory, Outlier, and Channel-Band Analysis with Weight Rescaling
Ye Qiao, Haocheng Xu, Xiaofan Zhang +1
Extending the context window support of large language models (LLMs) is crucial for tasks with long-distance dependencies. RoPE-based interpolation and extrapolation methods, such…
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
Reconfigurable Stream Network Architecture
Chengyue Wang, Xiaofan Zhang, Jason Cong +1
As AI systems grow increasingly specialized and complex, managing hardware heterogeneity becomes a pressing challenge. How can we efficiently coordinate and synchronize heterogeneo…