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Jiacheng Liang

15 papers hereh-index 7138 citations18 works total

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

author position
  • first author7
  • middle author8

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CR5
  • cs.AI4
  • cs.LG4
  • cs.CL1
  • cs.CV1
same name
  • Jiacheng Liang — 4 papers, h 3
  • Jiacheng Liang — 1 paper, h 1
  • Jiacheng Liang — 1 paper, h 0
  • Jiacheng Liang — 1 paper
  • Jiacheng Liang — 1 paper, h 1

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
20232026
most citedRobustKV: Defending Large Language Models against Jailbreak Attacks via KV Eviction

1 citations · 2 across the 13 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Routing-Aware Safety Alignment for Mixture-of-Experts Models

Jiacheng Liang, Yuhui Wang, Tanqiu Jiang +1

Mixture-of-Experts (MoE) language models introduce unique challenges for safety alignment due to their sparse routing mechanisms, which can enable degenerate optimization behaviors…

cs.LG2025

AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models

Jiacheng Liang, Tanqiu Jiang, Yuhui Wang +3

This paper presents AutoRAN, the first framework to automate the hijacking of internal safety reasoning in large reasoning models (LRMs). At its core, AutoRAN pioneers an execution…

cs.LG2025

GraphRAG under Fire

Jiacheng Liang, Yuhui Wang, Changjiang Li +4

GraphRAG advances retrieval-augmented generation (RAG) by structuring external knowledge as multi-scale knowledge graphs, enabling language models to integrate both broad context a…

cs.LG2023★ 1 cited

Model Extraction Attacks Revisited

Jiacheng Liang, Ren Pang, Changjiang Li +1

Model extraction (ME) attacks represent one major threat to Machine-Learning-as-a-Service (MLaaS) platforms by ``stealing'' the functionality of confidential machine-learning model…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.