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cs.CL2025

ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction

Xingshan Zeng, Weiwen Liu, Lingzhi Wang +6

Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions, often involving complex function calls and dynamic user-agent exchanges. Existi…

cs.CL2025

The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs

Jierun Chen, Tiezheng Yu, Haoli Bai +11

Large vision-language models (VLMs) increasingly adopt post-training techniques such as long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL) to…

cs.CL2025

ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Models

Boyang Xue, Qi Zhu, Rui Wang +8

Although demonstrating remarkable performance on reasoning tasks, Large Language Models (LLMs) still tend to fabricate unreliable responses when confronted with problems that are u…

cs.CL2025

EssayBench: Evaluating Large Language Models in Multi-Genre Chinese Essay Writing

Fan Gao, Dongyuan Li, Ding Xia +4

Chinese essay writing and its evaluation are critical in educational contexts, yet the capabilities of Large Language Models (LLMs) in this domain remain largely underexplored. Exi…

cs.CL2025

How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study

Zhexin Zhang, Xian Qi Loye, Victor Shea-Jay Huang +8

Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enhanced reasoning capabilities do n…

cs.CL2025

Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs

Yichun Yin, Wenyong Huang, Kaikai Song +49

We present Pangu Ultra, a Large Language Model (LLM) with 135 billion parameters and dense Transformer modules trained on Ascend Neural Processing Units (NPUs). Although the field…