1 citations · 1 across the 2 of their papers we have counts for
6 papers
AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor
Shu Yang, Jingyu Hu, Tong Li +3
We introduce AutoMonitor-Bench, the first benchmark designed to systematically evaluate the reliability of LLM-based misbehavior monitors across diverse tasks and failure modes. Au…
HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation
Haokun Liu, Sicong Huang, Jingyu Hu +2
There is growing interest in hypothesis generation with large language models (LLMs). However, fundamental questions remain: what makes a good hypothesis, and how can we systematic…
Every Attention Matters: An Efficient Hybrid Architecture for Long-Context Reasoning
Ling Team, Bin Han, Caizhi Tang +25
In this technical report, we present the Ring-linear model series, specifically including Ring-mini-linear-2.0 and Ring-flash-linear-2.0. Ring-mini-linear-2.0 comprises 16B paramet…
Holistic Capability Preservation: Towards Compact Yet Comprehensive Reasoning Models
Ling Team, Caizhi Tang, Chilin Fu +15
This technical report presents Ring-Lite-Distill, a lightweight reasoning model derived from our open-source Mixture-of-Experts (MoE) Large Language Models (LLMs) Ling-Lite. This s…
Towards Combating Frequency Simplicity-biased Learning for Domain Generalization
Xilin He, Jingyu Hu, Qinliang Lin +5
Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks fr…
Assessing Code Generation with Intermediate Languages
Xun Deng, Sicheng Zhong, Honghua Dong +4
Intermediate step methodologies like chain of thoughts (COT) have demonstrated effectiveness in enhancing the performance of Large Language Models (LLMs) on code generation. This s…