4 accepted papers
Large language model (LLM)-based multi-agent systems have shown strong capabilities in tasks such as code generation and collaborative reasoning. However, the effectiveness and robustness of these systems critically depend on their communication topology, which is often fixed or statically learned,…
Multimodal Large Language Model (MLLM) agents facilitate Graphical User Interface (GUI) automation but struggle with long-horizon, cross-application tasks due to limited context windows. While memory systems provide a viable solution, existing paradigms struggle to adapt to dynamic GUI environments,…
We propose a system for marking sensitive or copyrighted texts to detect their use in fine-tuning large language models (LLMs) under black-box access with statistical guarantees. Our method builds digital "marks" using invisible Unicode characters organized into ("cue", "reply") pairs. During an aud…
In e-commerce, effective product Attribute Mining (AM) is essential for improving product features and aiding consumer decisions. However, current AM methods often focus on extracting attributes from unimodal text, underutilizing multimodal data. In this paper, we propose a novel framework called Mu…