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20242026
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cs.CL2026

EffGen: Enabling Small Language Models as Capable Autonomous Agents

Gaurav Srivastava, Aafiya Hussain, Chi Wang +2

Most existing language model agentic systems today are built and optimized for large language models (e.g., GPT, Claude, Gemini) via API calls; while powerful, this approach faces…

cs.CL2026

Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection

Chi Wang, Min Gao, Zongwei Wang +3

With the rapid development of large language models, the generation of fake news has become increasingly effortless, posing a growing societal threat and underscoring the urgent ne…

cs.CL2026

When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework

Zhen Xu, Shang Zhu, Jue Wang +5

We investigate the challenge of applying Large Language Models (LLMs) to long texts. We propose a theoretical framework that distinguishes the failure modes of long context tasks i…

cs.CL2024

StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows

Yiran Wu, Tianwei Yue, Shaokun Zhang +2

It is a notable trend to use Large Language Models (LLMs) to tackle complex tasks, e.g., tasks that require a sequence of actions and dynamic interaction with tools and external en…

cs.CL2024

BADGE: BADminton report Generation and Evaluation with LLM

Shang-Hsuan Chiang, Lin-Wei Chao, Kuang-Da Wang +2

Badminton enjoys widespread popularity, and reports on matches generally include details such as player names, game scores, and ball types, providing audiences with a comprehensive…

cs.CL2024

Assessing and Verifying Task Utility in LLM-Powered Applications

Negar Arabzadeh, Siqing Huo, Nikhil Mehta +5

The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks.…