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cs.CL2026

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

Ang Li, Ben Liu, Bin Han +215

Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…

cs.CL2026

Improving Cross-Format Robustness in Language Models with Multi-Format Training

June M. Liu, Shaomian Zheng, He Cao +3

Large language models often remain sensitive to answer format: a question solved correctly in one form may fail in another semantically equivalent form. To study this gap, we defin…

cs.CL2024

CARE: A Clue-guided Assistant for CSRs to Read User Manuals

Weihong Du, Jia Liu, Zujie Wen +3

It is time-saving to build a reading assistant for customer service representations (CSRs) when reading user manuals, especially information-rich ones. Current solutions don't fit…

cs.CL2024

CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models

Tong Zhang, Peixin Qin, Yang Deng +6

Large language models (LLMs) are increasingly used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity rema…

cs.CL2024

STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents

Yue Chen, Chen Huang, Yang Deng +4

Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the…