6 citations · 26 across the 36 of their papers we have counts for
11 papers · 1 filter
ScribbleEdit: A Benchmark for Scribble-Only Image Editing
Jie Ren, Hao Kang, Kai Guo +9
Scribble-based interaction provides a lightweight and intuitive way for users to specify image editing intents in interactive editing tools. However, current image editing models b…
Detecting Unseen Jailbreak Sources: A Multi-Source Conformal Detection Perspective
Xunlei Qian, Yingqian Cui, Yuping Lin +2
Jailbreak defenses for large language models are usually calibrated on a fixed set of known attack methods. However, new attack methods keep appearing after deployment, and existin…
Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders
Chan Aristella Lu, Arya Fayyazi, Junhao Zhang +6
Fairness audits for LLM-based recommenders have largely focused on observable outputs, implicitly assuming that stable recommendations reflect stable internal processing. We challe…
Efficient LLM Adversarial Training via Low-Rank Defense and Circuit-Guided Surrogates
Weiyi He, Yuping Lin, Jiliang Tang +1
Adversarial training is one of the most effective defenses against adversarial attacks, yet the computational cost remains prohibitive at modern scales, especially for large langua…
Planner-Guided Recursive Exploration for Diffusion Language Model Decoding
Yingqian Cui, Wei Deng, Lantao Mei +4
Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent s…
A Simple Plug-in for Improving Eviction-Based KV Cache Compression
Yuping Lin, Jiayuan Ding, Yue Xing +3
KV cache growth is a major bottleneck for long-context inference in large language models. Existing methods are often dominated by binary eviction or representation approximation,…
Efficient LLM-based Advertising via Model Compression and Parallel Verification
Wenxin Dong, Chang Gao, Guanghui Yu +9
Large language models (LLMs) have shown remarkable potential in advertising scenarios such as ad creative generation and targeted advertising. However, deploying LLMs in real-time…
Ada-MK: Adaptive MegaKernel Optimization via Automated DAG-based Search for LLM Inference
Wenxin Dong, Mingqing Hu, Guanghui Yu +7
When large language models (LLMs) serve real-time inference in commercial online advertising systems, end-to-end latency must be strictly bounded to the millisecond range. Yet ever…
Crafting Reversible SFT Behaviors in Large Language Models
Yuping Lin, Pengfei He, Yue Xing +5
Supervised fine-tuning (SFT) induces new behaviors in large language models, yet imposes no structural constraint on how these behaviors are distributed within the model. Existing…
How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?
Yingqian Cui, Zhenwei Dai, Bing He +7
Latent reasoning has been recently proposed as a reasoning paradigm and performs multi-step reasoning through generating steps in the latent space instead of the textual space. Thi…
Retrieval Heads are Dynamic
Yuping Lin, Zitao Li, Yue Xing +6
Recent studies have identified "retrieval heads" in Large Language Models (LLMs) responsible for extracting information from input contexts. However, prior works largely rely on st…