2 citations · 2 across the 1 of their papers we have counts for
5 papers
Towards provable probabilistic safety for scalable embodied AI systems
Linxuan He, Lingxiang Fan, Qing-Shan Jia +13
Embodied AI systems, comprising AI models and physical plants, are increasingly prevalent across various applications. Due to the rarity of system failures, ensuring their safety i…
From Narrow Unlearning to Emergent Misalignment: Causes, Consequences, and Containment in LLMs
Erum Mushtaq, Anil Ramakrishna, Satyapriya Krishna +5
Recent work has shown that fine-tuning on insecure code data can trigger an emergent misalignment (EMA) phenomenon, where models generate malicious responses even to prompts unrela…
AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
Jason Chou, Ao Liu, Yuchi Deng +13
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, with code generation emerging as a key area of focus. While numerous benchmarks have…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
Mixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language Models
Shilin Xu, Yanwei Li, Rui Yang +9
Recent works on large language models (LLMs) have successfully demonstrated the emergence of reasoning capabilities via reinforcement learning (RL). Although recent efforts leverag…