7 papers
Intern-S2-Preview: Scientific Agentic Foundation Model
Lei Bai, Jiaqi Cao, Chiyu Chen +121
Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sus…
Beyond Uniform Experts: Cost-Aware Expert Execution for Efficient Multi-Device MoE Inference
Hui Zang, Pengfei Xia, Hong Liu +5
Mixture-of-Experts (MoE) architectures enable language models to achieve unprecedented scale via sparse activation. However, their inference performance is often limited by data mo…
OmniInfer: System-Wide Acceleration Techniques for Optimizing LLM Serving Throughput and Latency
Jun Wang, Yunxiang Yao, Wenwei Kuang +11
Large Language Models drive a wide range of modern AI applications but impose substantial challenges on large-scale serving systems due to intensive computation, strict latency con…
Deterministic Inference across Tensor Parallel Sizes That Eliminates Training-Inference Mismatch
Ziyang Zhang, Xinheng Ding, Jiayi Yuan +4
Deterministic inference is increasingly critical for large language model (LLM) applications such as LLM-as-a-judge evaluation, multi-agent systems, and Reinforcement Learning (RL)…
SciTS: Scientific Time Series Understanding and Generation with LLMs
Wen Wu, Ziyang Zhang, Liwei Liu +12
The scientific reasoning ability of large language models (LLMs) has recently attracted significant attention. Time series, as a fundamental modality in scientific data, presents u…
LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
Shuo Yan, Ruochen Li, Ziming Luo +11
Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reprodu…