12 citations · 13 across the 12 of their papers we have counts for
13 papers
A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression
Jincheng Ren, Siwei Wu, Yizhi Li +8
As terminal agents scale to long-horizon, multi-turn workflows, a key bottleneck is not merely limited context length, but the accumulation of noisy terminal observations in the in…
IQuest-Coder-V1 Technical Report
Jian Yang, Wei Zhang, Shawn Guo +35
In this report, we introduce the IQuest-Coder-V1 series-(7B/14B/40B/40B-Loop), a new family of code large language models (LLMs). Moving beyond static code representations, we prop…
Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments
Siwei Wu, Yizhi Li, Yuyang Song +8
Training agentic models for terminal-based tasks critically depends on high-quality terminal trajectories that capture realistic long-horizon interactions across diverse domains. H…
Scaling Test-time Compute for LLM Agents
King Zhu, Hanhao Li, Siwei Wu +12
Scaling test time compute has shown remarkable success in improving the reasoning abilities of large language models (LLMs). In this work, we conduct the first systematic explorati…
DeepMath-Creative: A Benchmark for Evaluating Mathematical Creativity of Large Language Models
Xiaoyang Chen, Xinan Dai, Yu Du +28
To advance the mathematical proficiency of large language models (LLMs), the DeepMath team has launched an open-source initiative aimed at developing an open mathematical LLM and s…
DocMMIR: A Framework for Document Multi-modal Information Retrieval
Zirui Li, Siwei Wu, Yizhi Li +3
The rapid advancement of unsupervised representation learning and large-scale pre-trained vision-language models has significantly improved cross-modal retrieval tasks. However, ex…
ContrastScore: Towards Higher Quality, Less Biased, More Efficient Evaluation Metrics with Contrastive Evaluation
Xiao Wang, Daniil Larionov, Siwei Wu +4
Evaluating the quality of generated text automatically remains a significant challenge. Conventional reference-based metrics have been shown to exhibit relatively weak correlation…
LongEval: A Comprehensive Analysis of Long-Text Generation Through a Plan-based Paradigm
Siwei Wu, Yizhi Li, Xingwei Qu +7
Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, yet their ability to generate long-form content remains poorly understoo…
A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
Siwei Wu, Zhongyuan Peng, Xinrun Du +14
Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, mer…
LIME: Less Is More for MLLM Evaluation
King Zhu, Qianbo Zang, Shian Jia +18
Multimodal Large Language Models (MLLMs) are evaluated on various benchmarks, such as image captioning, visual question answering, and reasoning. However, many of these benchmarks…
SongTrans: An unified song transcription and alignment method for lyrics and notes
Siwei Wu, Jinzheng He, Ruibin Yuan +5
The quantity of processed data is crucial for advancing the field of singing voice synthesis. While there are tools available for lyric or note transcription tasks, they all need p…
OmniBench: Towards The Future of Universal Omni-Language Models
Yizhi Li, Yinghao Ma, Ge Zhang +20
Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. However, the capacity of these models to concu…
Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation
Xingwei Qu, Ge Zhang, Siwei Wu +2
This paper presents the results of the shared task on Chinese metaphor generation, hosted at the 13th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC 202…