works on

From the 2 of 14 linked papers with an AI index.

activity
20242026
most citedWhen Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?

1 citations · 1 across the 3 of their papers we have counts for

collaborators

14 papers

cs.CL2026

HalluTracer: Hallucination Detection via Depth-Averaging Truth Signals

Zhihao Guo, Zonghan Wu, Huan Huo +6

Even well-aligned large language models confidently generate factually incorrect text, making hallucination a persistent reliability risk in high-stakes deployments. These models n…

cs.CV2026

Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives

Xiaolong Liu, Junjian Li, Yuan Xiao +4

The paper introduces Dualin, a two‑stage method that simultaneously recovers a human‑readable text prompt and the latent noise of a target image to improve prompt inversion for tex…

cs.CR20261 cited

When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?

Shang Wang, Tianqing Zhu, Dayong Ye +1

The paper proposes a lightweight method to make large language models forget specific information by altering the external knowledge base of Retrieval‑Augmented Generation systems,…

cs.LG2026

One Framework for All: Cross-Modal Membership Inference for Generative Models

Dayong Ye, Tainqing Zhu, Kun Gao +6

Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks. One fundamental threat is membership inf…

cs.LG2026

Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget

Dayong Ye, Tianqing Zhu, Ruiding Huang +5

Machine unlearning has been extensively studied in response to growing privacy concerns and regulatory requirements. However, auditing whether unlearning algorithms have truly eras…

cs.MA2026

From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration

Yizhe Xie, Congcong Zhu, Xinyue Zhang +5

Large Language Model-based Multi-Agent Systems (LLM-MAS) are increasingly applied to complex collaborative scenarios. However, their collaborative mechanisms may cause minor inaccu…