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Junfeng Fang

42 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author33
  • last author1

Across the 38 of 42 papers where every author was matched, so the position is known.

fields
  • cs.LG16
  • cs.CL13
  • cs.AI4
  • cs.CV4
  • cs.CR2
  • cs.IR2
same name
  • Junfeng Fang — 16 papers, h 15
  • Junfeng Fang — 12 papers, h 3
  • Junfeng Fang — 8 papers, h 6
  • Junfeng Fang — 7 papers, h 2
  • Junfeng Fang — 2 papers
  • Junfeng Fang — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence

Mengru Wang, Junfeng Fang, Shuofei Qiao +17

AI models are increasingly used in scientific discovery and human decision-making. Yet how AI models work and what risks they pose remain poorly understood. As AI development becom…

cs.AI2026

RePolicy: Reinforcement Learning for Safety-Policy Invocation in Agent Safeguards

Houcheng Jiang, Boxuan Zhang, Qiyong Zhong +3

Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies. Existing policy-aware safeguards mainly rely on promp…

cs.AI2026

TRACE: Trajectory Risk-Aware Compression for Long-Horizon Agent Safety

Zhepei Hong, Lin Wang, Liting Li +5

Long-horizon LLM agents produce safety evidence across long trajectories, where sparse, delayed, and compositional risk signals often escape local moderation. Existing turn-level o…

cs.AI2026

AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition

Ruipeng Wang, Yuxin Chen, Yukai Wang +9

Recent advances in large language models have enabled LLM-based agents to achieve strong performance on a variety of benchmarks. However, their performance in real-world deployment…

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