3 citations · 5 across the 9 of their papers we have counts for
11 papers
HSPR: Recovering Software-Agent Benchmark Scores via Hierarchical Posterior Inference
Wanyi Chen, Wanqin Shen, Yuanchen Shi +2
Evaluating software agents on a full benchmark is expensive. A small task subset can preserve agent rankings while yielding inaccurate scores. Historical responses provide useful s…
Zero2Repo: Can Coding Agents Build Repositories from Scratch?
Pei Yang, Tianyu Shi, Yuhang Yao +23
Coding agents are increasingly asked to build software rather than patch it, yet benchmarks for from-scratch repository construction are mostly limited to a single language and dep…
Attributing Structured-Output Gains in Function Calling: Interface Alignment versus Procedural Transfer
Wanyi Chen, Daoyuan Chen, Fang Kong
Structured-output benchmarks reward both task decisions and interface compliance, so prompt-induced function-calling gains require attribution before they can be interpreted as tra…
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…
TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing
Pei Yang, Wanyi Chen, Tongyun Yang +15
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.…
Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?
Wanyi Chen, Xiao Yang, Xu Yang +7
We introduce Agent2 RL-Bench, a compact diagnostic benchmark for evaluating agentic RL post-training, which tests whether LLM agents can autonomously design, implement, debug, and…
AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis
Pei Yang, Wanyi Chen, Asuka Yuxi Zheng +13
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…
Assessing LLM code generation quality through path planning tasks
Wanyi Chen, Meng-Wen Su, Mary L. Cummings
As LLM-generated code grows in popularity, more evaluation is needed to assess the risks of using such tools, especially for safety-critical applications such as path planning. Exi…
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…
Can LLMs plan paths in the real world?
Wanyi Chen, Meng-Wen Su, Nafisa Mehjabin +1
As large language models (LLMs) increasingly integrate into vehicle navigation systems, understanding their path-planning capability is crucial. We tested three LLMs through six re…
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…