activity
20242026
collaborators

7 papers

cs.CL2026

Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics

Ziwen Xu, Chenyan Wu, Hengyu Sun +9

Methods for controlling large language models (LLMs), including local weight fine-tuning, LoRA-based adaptation, and activation-based interventions, are often studied in isolation,…

cs.AI2026

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

Xingjun Ma, Yixu Wang, Hengyuan Xu +18

The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has driven major gains in reasoning, perception, and generation across language and…

cs.AI2026

AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security

Dongrui Liu, Qihan Ren, Chen Qian +40

The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current guardrail models lack agentic risk…

cs.CL2026

YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models

Junyu Lin, Meizhen Liu, Xiufeng Huang +12

As large language models (LLMs) are increasingly deployed in real-world applications, safety guardrails are required to go beyond coarse-grained filtering and support fine-grained,…

cs.CV2025

Hyperspherical Latents Improve Continuous-Token Autoregressive Generation

Guolin Ke, Hui Xue

Autoregressive (AR) models are promising for image generation, yet continuous-token AR variants often trail latent diffusion and masked-generation models. The core issue is heterog…

cs.AI2025

Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models

Chenrui Cao, Liangcheng Song, Zenan Li +4

Recent advancements, such as DeepSeek-Prover-V2-671B and Kimina-Prover-Preview-72B, demonstrate a prevailing trend in leveraging reinforcement learning (RL)-based large-scale train…

cs.AI2024

Understanding the Weakness of Large Language Model Agents within a Complex Android Environment

Mingzhe Xing, Rongkai Zhang, Hui Xue +3

Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to gener…