4 citations · 4 across the 9 of their papers we have counts for
9 papers
RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models
Andrew Zhuoer Feng, Cunxiang Wang, Bosi Wen +4
Large language model alignment via reinforcement learning depends critically on reward function quality. However, static, domain-specific reward models are often costly to train an…
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…
DVD: A Robust Method for Detecting Variant Contamination in Large Language Model Evaluation
Renzhao Liang, Jingru Chen, Bo Jia +7
Evaluating large language models (LLMs) is increasingly confounded by \emph{variant contamination}: the training corpus contains semantically equivalent yet lexically or syntactica…
Deep Literature Survey Automation with an Iterative Workflow
Hongbo Zhang, Han Cui, Yidong Wang +6
Automatic literature survey generation has attracted increasing attention, yet most existing systems follow a one-shot paradigm, where a large set of papers is retrieved at once an…
GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
5 Team, Aohan Zeng, Xin Lv +167
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that s…
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.…
Reasoning on Multiple Needles In A Haystack
Yidong Wang
The Needle In A Haystack (NIAH) task has been widely used to evaluate the long-context question-answering capabilities of Large Language Models (LLMs). However, its reliance on sim…
StepMathAgent: A Step-Wise Agent for Evaluating Mathematical Processes through Tree-of-Error
Shu-Xun Yang, Cunxiang Wang, Yidong Wang +3
Evaluating mathematical capabilities is critical for assessing the overall performance of large language models (LLMs). However, existing evaluation methods often focus solely on f…