collaborators

15 papers

cs.CV2026

EgoSafe: A First-Person Mobile-Captured Benchmark for Visual Safety Understanding

Yuyun Chen, Tianao Li, TianQuan Feng +4

The paper introduces EgoSafe-Bench, a first‑person video dataset and evaluation protocol designed to test causal and forensic reasoning for visual safety understanding, highlightin…

cs.CR2026

FreoStream:Enhancing Stream Guardrails via Future-Aware Reasoning and Safety-Aligned Optimization

Jianwei Wang, Guoyang Shen, Yanhong Wu +5

Stream guardrails enable token-level safety detection before full responses are generated. However, they often make overly conservative judgements and block those sensitive but saf…

cs.LG2026

AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models

Run He, Kai Tong, Di Fang +5

In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the federated learning (FL) with pre-…

cs.CL2026

RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior

Junyao Yang, Jianwei Wang, Huiping Zhuang +2

Large Language Models (LLMs) with long chain-of-thought (CoT) capability, termed Reasoning Models, demonstrate superior intricate problem-solving abilities through multi-step long…

cs.LG2025

MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning

Tao Zhang, Ziqian Zeng, Hao Peng +2

Long Chain-of-Thought (CoT) reasoning has significantly advanced the capabilities of Large Language Models (LLMs), but this progress is accompanied by substantial memory and latenc…

cs.LG2025

REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning

Run He, Di Fang, Yizhu Chen +5

Exemplar-free class-incremental learning (EFCIL) aims to mitigate catastrophic forgetting in class-incremental learning (CIL) without available historical training samples as exemp…