most citedWhen Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2025

HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices

HyperAI Team, Yuchen Liu, Kaiyang Han +26

Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy direc…

cs.MA2025

Agent-Kernel: A MicroKernel Multi-Agent System Framework for Adaptive Social Simulation Powered by LLMs

Yuren Mao, Peigen Liu, Xinjian Wang +11

Multi-Agent System (MAS) developing frameworks serve as the foundational infrastructure for social simulations powered by Large Language Models (LLMs). However, existing frameworks…

cs.CV2025

Explainable AI-Generated Image Detection RewardBench

Michael Yang, Shijian Deng, William T. Doan +4

Conventional, classification-based AI-generated image detection methods cannot explain why an image is considered real or AI-generated in a way a human expert would, which reduces…

cs.IR2025

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting

Alexandre Andre, Gauthier Roy, Eva Dyer +1

Large Language Models (LLMs) are increasingly used for recommendation tasks due to their general-purpose capabilities. While LLMs perform well in rich-context settings, their behav…

cs.AI20251 cited

When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models

Kai Wang, Yihao Zhang, Meng Sun

The honesty of large language models (LLMs) is a critical alignment challenge, especially as advanced systems with chain-of-thought (CoT) reasoning may strategically deceive humans…

cs.SD2025

Delayed-KD: Delayed Knowledge Distillation based CTC for Low-Latency Streaming ASR

Longhao Li, Yangze Li, Hongfei Xue +4

CTC-based streaming ASR has gained significant attention in real-world applications but faces two main challenges: accuracy degradation in small chunks and token emission latency.…