3 citations · 4 across the 12 of their papers we have counts for
6 papers · 1 filter
Adaptive Co-Serving LLM Watermarking on Modern Inference Engines
Kieu Dang, Phung Lai, Ching-Yun Ko +1
Large language model (LLM) watermarking is important for ownership verification and intellectual property protection. However, existing approaches focus on algorithmic design while…
EvoFlux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
Kushal Raj Bhandari, Ling Yue, Ching-Yun Ko +4
Small language models (1.5B-4B parameters) offer low-cost, low-latency tool use but often produce plausible plans that fail during execution due to unavailable tools, missing argum…
From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents
Ling Yue, Kushal Raj Bhandari, Ching-Yun Ko +6
Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval,…
Steering Externalities: Benign Activation Steering Unintentionally Increases Jailbreak Risk for Large Language Models
Chen Xiong, Zhiyuan He, Pin-Yu Chen +2
Activation steering is a practical post-training model alignment technique to enhance the utility of Large Language Models (LLMs). Prior to deploying a model as a service, develope…
vLLM Hook v0: A Plug-in for Programming Model Internals on vLLM
Ching-Yun Ko, Pin-Yu Chen
Modern artificial intelligence (AI) models are deployed on inference engines to optimize runtime efficiency and resource allocation, particularly for transformer-based large langua…
Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning
Yu-Ang Lee, Ching-Yun Ko, Pin-Yu Chen +1
Low-Rank Adaptation (LoRA) is the prevailing approach for efficient large language model (LLM) fine-tuning. Building on this paradigm, recent studies have proposed alternative init…