Portrait of Fengyuan Liu

Fengyuan Liu

Master's Research - McGill University
Supervisor
Siva Reddy
Co-supervisor
Dzmitry Bahdanau
Research Topics
AGI (Artificial General Intelligence)
AI Alignment
Continual Learning
Decentralized Learning
Decentralized Optimization
Deep Learning
Explainable AI (XAI)
Federated Learning
Large Language Models (LLM)
LLM Agent
Manifold Learning
Model Compression
Multimodal Learning
Natural Language Processing
Optimization
Transfer Learning
Website
GitHub

Publications

WebArena-Pro: A Heterogeneous, Multimodal, Reproducible Benchmark for Web Agents
Imene Kerboua
Fatemeh Pesaran zadeh
Xing Han Lu
Weijian Qi
Alexander Miller
Junyi Song
Yunjia Tian
Dongjin Kang
Seyeon Choi
Marzia Nouri
Ewen Gueguen
Matteo Boglioni
Fengyuan Liu
Zeyi Liao
Mengqi Yuan
Yue Li
Alexandre Lacoste
Alexandre Drouin
Spandana Gella
Huan Sun … (see 2 more)
Gunhee Kim
Siva Reddy
Web agents powered by large language and vision-language models are increasingly applied to realistic browser work that spans heterogeneous … (see more)applications, multimodal content, and stateful workflows. However, existing reproducible web-agent benchmarks cover only a small number of web applications drawn from a few software categories, and restrict modality to text and vision. Live benchmarks broaden site coverage but sacrifice reproducibility, since pages and data drift between runs. Moreover, existing benchmarks do not meaningfully evaluate whether agents can understand and use audio and video content embedded within web tasks. To address these gaps, we introduce WebArena-Pro, a benchmark comprising 300 tasks across 20 self-hosted web applications in six domain categories, spanning distinct interface conventions, workflows, and data models. Across the evaluated agents, the best performance is achieved by Gemini 3.1 Pro, which attains 37.0 % success under a 50-step budget, while open-source models' performance does not exceed 27.7% success. Among reproducible, human-curated web agent benchmarks, WebArena-Pro provides the broadest application coverage and the most comprehensive multimodal support to date. The benchmark treats audio and video as core observations alongside text and vision, with dedicated actions for extracting information from each. WebArena-Pro runs each task in isolation and supports reproducible, parallel evaluation. Tasks are authored through a dedicated annotator interface, filtered by LLM-assisted triage, and finally validated by humans before release.
2026-05-22
AIWILD @ International Conference on Machine Learning (published)
openreview.net
openreview.net
BRIDGE: Predicting Human Task Completion Time From Model Performance
Fengyuan Liu
Jay Gala
Nilaksh
Dzmitry Bahdanau
Siva Reddy
Hugo Larochelle
Evaluating the real-world capabilities of AI systems requires grounding benchmark performance in human-interpretable measures of task diffic… (see more)ulty. Existing approaches that rely on direct human task completion time annotations are costly, noisy, and difficult to scale across benchmarks. In this work, we propose BRIDGE, a unified psychometric framework that learns the latent difficulty scale from model responses and anchors it to human task completion time. Using a two-parameter logistic Item Response Theory model, we jointly estimate latent task difficulty and model capability from model performance data across multiple benchmarks. We demonstrate that latent task difficulty varies linearly with the logarithm of human completion time, allowing human task completion time to be inferred for new benchmarks from model performance alone. Leveraging this alignment, we forecast frontier model capabilities in terms of human task length and independently reproduce METR's exponential scaling results, with the 50% solvable task horizon doubling approximately every 6 months.
2026-02-05
arXiv (preprint)
doi.org
openreview.net