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cs.CL2026

The Collaboration Tax: How Much LLM Multi-Agent Systems Pay to Coordinate

Weixiang Sun, Zehong Wang, Hong Huang +2

Multi-agent systems built from large language models are deployed widely, yet how much performance is lost when two LLMs must coordinate rather than act alone remains unclear. We f…

cs.CL2026

SAGE: Answer-Conditioned Uncertainty Targets for Verbal Uncertainty Alignment

Kaiwen Shi, Zheyuan Zhang, Yanfang Ye

Large language models increasingly express uncertainty through natural-language statements, yet these expressions often fail to reflect the model's sampled behavior. We study verba…

cs.CL2026

PreScam: A Benchmark for Predicting Scam Progression from Early Conversations

Weixiang Sun, Shang Ma, Yiyang Li +5

Conversational scams, such as romance and investment scams, are emerging as a major form of online fraud. Unlike one-shot scam lures such as fake lottery or unpaid toll messages, t…

cs.CL2026

EvoTaxo: Building and Evolving Taxonomy from Social Media Streams

Yiyang Li, Tianyi Ma, Yanfang Ye

Constructing taxonomies from social media corpora is challenging because posts are short, noisy, semantically entangled, and temporally dynamic. Existing taxonomy induction methods…

cs.CL2026

AI Alignment Breaks at the Edge

Han Bao, Yue Huang, Xiaoda Wang +5

General Alignment has improved average-case helpfulness and safety, but current alignment practice still rewards confident, single-turn responses. The problem is not only that mode…

cs.CL2025

LLMs4All: A Review of Large Language Models Across Academic Disciplines

Yanfang Ye, Zheyuan Zhang, Tianyi Ma +26

Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown t…