5 accepted papers
Large Language Models (LLMs) demonstrate potentials for automating scientific code generation but face challenges in reliability, error propagation in multi-agent workflows, and evaluation in domains with ill-defined success metrics. We present a Bayesian adversarial multi-agent framework specifical…
Large multimodal models (LMMs) have achieved impressive performance on various vision-language tasks, but their substantial computational and memory costs hinder their practical deployment. Existing compression methods often decouple low-rank decomposition and quantization, leading to compounded rec…
Chain-of-Thought (CoT) has emerged as an effective paradigm to enhance the reasoning ability of large language models (LLMs) in complex tasks. However, existing approaches still face two major challenges: (1) the lack of a global mechanism to integrate and interact across diverse reasoning hypothese…
Text summarization is a core task in natural language processing (NLP). With the rapid growth of information, handling long documents has become increasingly demanding, making summarization essential. Existing research mainly focuses on model improvements and sentence-level pruning, but often overlo…
Clustering non-independent and identically distributed (non-IID) data under local differential privacy (LDP) in federated settings presents a critical challenge: preserving privacy while maintaining accuracy without iterative communication. Existing one-shot methods rely on unstable pairwise centroi