11 citations · 12 across the 11 of their papers we have counts for
17 papers
RvB: Automating AI System Hardening via Iterative Red-Blue Games
Lige Huang, Zicheng Liu, Jie Zhang +3
The dual offensive and defensive utility of Large Language Models (LLMs) highlights a critical gap in AI security: the lack of unified frameworks for dynamic, iterative adversarial…
Loop as a Bridge: Can Looped Transformers Truly Link Representation Space and Natural Language Outputs?
Guanxu Chen, Dongrui Liu, Jing Shao
Large Language Models (LLMs) often exhibit a gap between their internal knowledge and their explicit linguistic outputs. In this report, we empirically investigate whether Looped T…
ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging
Junyao Yang, Chen Qian, Dongrui Liu +3
Large Reasoning Models (LRMs) with long chain-of-thought reasoning have recently achieved remarkable success. Yet, equipping domain-specialized models with such reasoning capabilit…
TradeTrap: Are LLM-based Trading Agents Truly Reliable and Faithful?
Lewen Yan, Jilin Mei, Tianyi Zhou +4
LLM-based trading agents are increasingly deployed in real-world financial markets to perform autonomous analysis and execution. However, their reliability and robustness under adv…
PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilities
Zicheng Liu, Lige Huang, Jie Zhang +3
The increasing autonomy of Large Language Models (LLMs) necessitates a rigorous evaluation of their potential to aid in cyber offense. Existing benchmarks often lack real-world com…
LLMs Deceive Unintentionally: Emergent Misalignment in Dishonesty from Misaligned Samples to Biased Human-AI Interactions
Xuhao Hu, Peng Wang, Xiaoya Lu +3
Previous research has shown that LLMs finetuned on malicious or incorrect completions within narrow domains (e.g., insecure code or incorrect medical advice) can become broadly mis…