works on

From the 3 of 28 linked papers with an AI index.

activity
20242026
most citedCATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching

1 citations · 1 across the 4 of their papers we have counts for

collaborators

28 papers

cs.LG2026

ClawTrack: Towards Trace-Level Evaluation and Improvement of Real-World Autonomous Agents

Xingjian Wu, Xuhang Zhu, Xingchen Liu +6

The paper introduces ClawTrack, a benchmark that evaluates both the final outcomes and the step-by-step reasoning processes of LLM-based autonomous agents across multiple dimension…

cs.LG2026

Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

Tianen Shen, Zhengyu Li, Yutong Li +4

The paper introduces WrapFlow, a framework that directly tokenizes irregular multivariate time‑series observations into continuous‑time tokens and uses a Transformer with a simulat…

cs.LG2026

Contrastive Reinforced Policy Optimization via Privileged Self-Distillation

Xingjian Wu, Junlin Liu, Xingchen Liu +6

The paper introduces Contrastive Reinforced Policy Optimization (CRPO), a method that frames on‑policy self‑distillation for large language models as a contrastive learning problem…

cs.LG20261 cited

CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching

Xingjian Wu, Xiangfei Qiu, Zhengyu Li +5

Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning normal patterns…

cs.AI2026

PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering

Junkai Lu, Peng Chen, Xingjian Wu +4

Time series reasoning demands both the perception of complex dynamics and logical depth. However, existing LLM-based approaches exhibit two limitations: they often treat time serie…

cs.LG2026

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models

Xingjian Wu, Junkai Lu, Siyu Yan +4

Recent advances in Large Language Models (LLMs) have catalyzed the development of multi-agent systems (MAS) for complex reasoning tasks. However, existing MAS typically rely on pre…