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20232026
most citedCan LLMs plan paths in the real world?

3 citations · 5 across the 9 of their papers we have counts for

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cs.LG2026

MERA: Model Evolution and Routing with Skill Adaptation for Agentic Systems at Scale

Yuhang Yao, Zeyu Wang, Wanyi Chen +8

LLM agents execute heterogeneous sequences of model calls within a single task: some invocations require careful reasoning, while others are structured steps such as formatting or…

cs.LG2026

TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing

Pei Yang, Wanyi Chen, Tongyun Yang +14

LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single user request triggers many model calls.…

cs.LG2026

AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis

Pei Yang, Wanyi Chen, Asuka Yuxi Zheng +11

Large language model (LLM) agents offer a promising data-driven approach to automating Site Reliability Engineering (SRE), yet their enterprise deployment is constrained by three c…

cs.LG2025

To impute or not to impute: How machine learning modelers treat missing data

Wanyi Chen, Mary Cummings

Missing data is prevalent in tabular machine learning (ML) models, and different missing data treatment methods can significantly affect ML model training results. However, little…

cs.LG20232 cited

Subjectivity in Unsupervised Machine Learning Model Selection

Wanyi Chen, Mary L. Cummings

Model selection is a necessary step in unsupervised machine learning. Despite numerous criteria and metrics, model selection remains subjective. A high degree of subjectivity may l…