3 citations · 5 across the 8 of their papers we have counts for
9 papers
Numbers Beat Words: A Rigorous On-Premise Benchmark for Coupled MIMO Controller Tuning
Yang Shu, Jiaxuan Chen, Haonan Li
Tuning controllers for strongly coupled multi-input multi-output (MIMO) processes is difficult because decentralized auto-tuning ignores loop interaction and local optimization is…
From Leaky Thoughts to Private Reasoning: Controlling What LRMs Say to Themselves
Haritz Puerto, Haonan Li, Xudong Han +2
Large reasoning models (LRMs) produce reasoning traces (RTs) that often contain sensitive information. These leaky thoughts are difficult to control and frequently violate explicit…
Training and Benchmarking Code Generation for Physics-Inspired Animations
Yanan Wang, Renxi Wang, Yongxin Wang +5
Large language models (LLMs) have been widely studied in areas such as mathematical reasoning, complex coding, and scientific problem solving. However, their ability to generate ex…
AgentFly: Scaling Agentic Reinforcement Learning with Unified Resource System
Renxi Wang, Rifo Ahmad Genadi, Bilal El Bouardi +6
Methods to build LLM agents have evolved from prompt engineering and supervised finetuning to agentic reinforcement learning (agentic RL). However, agentic RL remains bottlenecked…
RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises
Zenan Zhai, Hao Li, Xudong Han +4
Recent advances in large language models (LLMs) have shown that they can answer questions requiring complex reasoning. However, their ability to identify and respond to text contai…
Control Illusion: The Failure of Instruction Hierarchies in Large Language Models
Yilin Geng, Haonan Li, Honglin Mu +5
Large language models (LLMs) are increasingly deployed with hierarchical instruction schemes, where certain instructions (e.g., system-level directives) are expected to take preced…
SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning
Renxi Wang, Honglin Mu, Liqun Ma +5
Long-context understanding has emerged as a critical capability for large language models (LLMs). However, evaluating this ability remains challenging. We present SCALAR, a benchma…
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
Haonan Li, Xudong Han, Zenan Zhai +32
To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety. Combining a dynamic le…
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Lizhi Lin, Honglin Mu, Zenan Zhai +9
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed. In li…