19 citations · 35 across the 15 of their papers we have counts for
16 papers
Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments
Jie Wu, Zhenru Zhang, Beichen Zhang +11
As terminal-based code agents become prevalent, agent trajectories have accumulated at scale, while realistic, executable environments remain scarce. However, environments are what…
ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs
Yang Yang, Qinyu Zhao, Mouxiang Chen +5
Existing scaling strategies for Multimodal Large Language Models (MLLMs) typically expand either model parameters or sequential inference computation, incurring substantial memory…
CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation
Xiaobin Zhang, Lefei Shen, Mouxiang Chen +6
Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels. To mitigate this, the forecast-then-opt…
TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
Hongkai Li, Shifeng Xie, Lefei Shen +7
Time series foundation models (TSFMs) are increasingly pretrained on large corpora, raising concerns that evaluation datasets may have been exposed during pretraining and thus yiel…
ReCode: Reinforcing Code Generation with Reasoning-Process Rewards
Lishui Fan, Yu Zhang, Mouxiang Chen +1
In practice, rigorous reasoning is often a key driver of correct code, while Reinforcement Learning (RL) for code generation often neglects optimizing reasoning quality. Bringing p…
VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones
Lefei Shen, Mouxiang Chen, Xu Liu +5
Recent studies have indicated that vision models pre-trained on images can serve as time series foundation models (TSFMs) by reformulating time series forecasting (TSF) as image re…
The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting
Lefei Shen, Mouxiang Chen, Han Fu +5
Transformer-based models have recently become dominant in Long-term Time Series Forecasting (LTSF), yet the variations in their architecture, such as encoder-only, encoder-decoder,…
Parallel Scaling Law for Language Models
Mouxiang Chen, Binyuan Hui, Zeyu Cui +5
It is commonly believed that scaling language models should commit a significant space or time cost, by increasing the parameters (parameter scaling) or output tokens (inference-ti…
Self-Explained Keywords Empower Large Language Models for Code Generation
Lishui Fan, Mouxiang Chen, Zhongxin Liu
Large language models (LLMs) have achieved impressive performance in code generation. However, due to the long-tail distribution of LLMs' training data, low-frequency terms are typ…
B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests
Mouxiang Chen, Zhongxin Liu, He Tao +4
Selecting the best code solution from multiple generated ones is an essential task in code generation, which can be achieved by using some reliable validators (e.g., developer-writ…
VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Mouxiang Chen, Lefei Shen, Zhuo Li +3
Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time…
JumpCoder: Go Beyond Autoregressive Coder via Online Modification
Mouxiang Chen, Hao Tian, Zhongxin Liu +2
While existing code large language models (code LLMs) exhibit impressive capabilities in code generation, their autoregressive sequential generation inherently lacks reversibility.…
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt
Mouxiang Chen, Zemin Liu, Chenghao Liu +3
Recent research has demonstrated the efficacy of pre-training graph neural networks (GNNs) to capture the transferable graph semantics and enhance the performance of various downst…
Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
Mouxiang Chen, Lefei Shen, Han Fu +3
Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are s…
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
Mouxiang Chen, Chenghao Liu, Zemin Liu +2
Unbiased Learning to Rank (ULTR) aims to train unbiased ranking models from biased click logs, by explicitly modeling a generation process for user behavior and fitting click data…
Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank
Mouxiang Chen, Chenghao Liu, Zemin Liu +1
Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH),…