5 papers
Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation
Yanzhi Tian, Cunxiang Wang, Zeming Liu +5
Large Language Models (LLMs) have significantly advanced Machine Translation (MT), applying them to linguistically complex domains-such as Social Network Services, literature etc.…
RAVEL: Reasoning Agents for Validating and Evaluating LLM Text Synthesis
Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo +9
Large Language Models have evolved from single-round generators into long-horizon agents, capable of complex text synthesis scenarios. However, current evaluation frameworks lack t…
TraceSIR: A Multi-Agent Framework for Structured Analysis and Reporting of Agentic Execution Traces
Shu-Xun Yang, Cunxiang Wang, Haoke Zhang +12
Agentic systems augment large language models with external tools and iterative decision making, enabling complex tasks such as deep research, function calling, and coding. However…
GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, :, Aohan Zeng +184
We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (AR…
UDA: Unsupervised Debiasing Alignment for Pair-wise LLM-as-a-Judge
Yang Zhang, Cunxiang Wang, Lindong Wu +4
Pairwise evaluation of Large Language Models (LLMs) is a common paradigm, but it is prone to preference bias, where judges systematically favor certain outputs, such as their own.…