6 citations · 20 across the 12 of their papers we have counts for
13 papers
An Empirical Study of Harness Design for Coding Agents
Run-Ze Fan, Zihao Zhang, Simin Ma +6
Coding harnesses shape how autonomous coding agents translate model capabilities into long-horizon software-engineering performance, yet existing work typically evaluates harnesses…
Deep Research: A Systematic Survey
Zhengliang Shi, Yiqun Chen, Haitao Li +23
Large language models (LLMs) have rapidly evolved from text generators into powerful problem solvers. Yet, many open tasks demand critical thinking, multi-source, and verifiable ou…
MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning
Run-Ze Fan, Zengzhi Wang, Pengfei Liu
Scientific reasoning is critical for developing AI scientists and supporting human researchers in advancing the frontiers of natural science discovery. However, the open-source com…
Generative AI Act II: Test Time Scaling Drives Cognition Engineering
Shijie Xia, Yiwei Qin, Xuefeng Li +11
The first generation of Large Language Models - what might be called "Act I" of generative AI (2020-2023) - achieved remarkable success through massive parameter and data scaling,…
PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital World
Yanheng He, Jiahe Jin, Shijie Xia +5
Imagine a world where AI can handle your work while you sleep - organizing your research materials, drafting a report, or creating a presentation you need for tomorrow. However, wh…
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
Zhen Huang, Zengzhi Wang, Shijie Xia +25
The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showc…
Benchmarking Benchmark Leakage in Large Language Models
Ruijie Xu, Zengzhi Wang, Run-Ze Fan +1
Amid the expanding use of pre-training data, the phenomenon of benchmark dataset leakage has become increasingly prominent, exacerbated by opaque training processes and the often u…
Reformatted Alignment
Run-Ze Fan, Xuefeng Li, Haoyang Zou +5
The quality of finetuning data is crucial for aligning large language models (LLMs) with human values. Current methods to improve data quality are either labor-intensive or prone t…
RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation
Run-Ze Fan, Yixing Fan, Jiangui Chen +3
Automatic mainstream hashtag recommendation aims to accurately provide users with concise and popular topical hashtags before publication. Generally, mainstream hashtag recommendat…
Merging Experts into One: Improving Computational Efficiency of Mixture of Experts
Shwai He, Run-Ze Fan, Liang Ding +3
Scaling the size of language models usually leads to remarkable advancements in NLP tasks. But it often comes with a price of growing computational cost. Although a sparse Mixture…
Generative Judge for Evaluating Alignment
Junlong Li, Shichao Sun, Weizhe Yuan +3
The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address. In the field of Natural Language Processing (NLP), researchers…
MerA: Merging Pretrained Adapters For Few-Shot Learning
Shwai He, Run-Ze Fan, Liang Ding +3
Adapter tuning, which updates only a few parameters, has become a mainstream method for fine-tuning pretrained language models to downstream tasks. However, it often yields subpar…