7 papers
Reducing Detail Hallucinations in Long-Context Regulatory Understanding via Targeted Preference Optimization
Yang Liu, Bin Chong, Yuhan Lin +7
Large language models (LLMs) frequently produce \emph{detail hallucinations} when processing long regulatory documents, including subtle errors in threshold values, units, scopes,…
Scaling Agentic Verifier for Competitive Coding
Zeyao Ma, Jing Zhang, Xiaokang Zhang +9
Large language models (LLMs) have demonstrated strong coding capabilities but still struggle to solve competitive programming problems correctly in a single attempt. Execution-base…
Graph VQ-Transformer (GVT): Fast and Accurate Molecular Generation via High-Fidelity Discrete Latents
Haozhuo Zheng, Cheng Wang, Yang Liu
The de novo generation of molecules with desirable properties is a critical challenge, where diffusion models are computationally intensive and autoregressive models struggle with…
ODMA: On-Demand Memory Allocation Strategy for LLM Serving on LPDDR-Class Accelerators
Guoqiang Zou, Wanyu Wang, Hao Zheng +2
Existing memory management techniques severely hinder efficient Large Language Model serving on accelerators constrained by poor random-access bandwidth.While static pre-allocation…
RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning
Jiahe Song, Chuang Wang, Bowen Jiang +13
Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machi…
Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model
Ling Team, Anqi Shen, Baihui Li +101
We present Ring-1T, the first open-source, state-of-the-art thinking model with a trillion-scale parameter. It features 1 trillion total parameters and activates approximately 50 b…