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cs.CL2026★ 2 cited
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
cs.CL2026
From Failure to Mastery: Generating Hard Samples for Tool-use Agents
Bingguang Hao, Zengzhuang Xu, Yuntao Wen +11
The advancement of LLM agents with tool-use capabilities requires diverse and complex training corpora. Existing data generation methods, which predominantly follow a paradigm of r…
cs.CL2025
Probability Consistency in Large Language Models: Theoretical Foundations Meet Empirical Discrepancies
Xiaoliang Luo, Xinyi Xu, Michael Ramscar +1
Can autoregressive large language models (LLMs) learn consistent probability distributions when trained on sequences in different token orders? We prove formally that for any well-…