web agent
webagentswebchain
WebChain v2
A large-scale, human-annotated dataset of real-world web interaction trajectories for training and evaluating web agents.
[Paper] [Code] [Dataset]
WebChain captures how people complete real tasks on live websites. It is designed for agents that must both identify the correct interface element and reason through a sequence of actions. Each trajectory aligns screenshots, web structure, grounded actions, and reasoning signals instead of treating web navigation as… See the full description on the dataset page: https://huggingface.co/datasets/webagentlab/webchain.webchain-legacy
WebChain
WebChain is a large-scale, human-annotated dataset of real-world web interaction trajectories for training and evaluating GUI agents and web agents. WebChain contains 31,725 trajectories, 317,993 steps, and 428 unique domains. Its core contribution is a Triple Alignment of visual context, structural context, and action grounding, enabling supervision for both spatial grounding and long-horizon planning.
Paper: https://arxiv.org/abs/2603.05295
Open access… See the full description on the dataset page: https://huggingface.co/datasets/webagentlab/webchain-legacy.AFM-WebAgent-RL-Dataset
Data Introduction
This dataset serves as the core training data for Agent Foundation Models (AFMs), specifically designed to elicit end-to-end multi-agent reasoning capabilities in large language models. Built on the novel "Chain-of-Agents (CoA)" paradigm, the dataset leverages a multi-agent distillation framework to transform collaboration processes from state-of-the-art multi-agent systems into trajectory data suitable for supervised fine-tuning (SFT), simulating dynamic… See the full description on the dataset page: https://huggingface.co/datasets/PersonalAILab/AFM-WebAgent-RL-Dataset.AFM-WebAgent-SFT-Dataset
Data Introduction
This dataset serves as the core training data for Agent Foundation Models (AFMs), specifically designed to elicit end-to-end multi-agent reasoning capabilities in large language models. Built on the novel "Chain-of-Agents (CoA)" paradigm, the dataset leverages a multi-agent distillation framework to transform collaboration processes from state-of-the-art multi-agent systems into trajectory data suitable for supervised fine-tuning (SFT), simulating dynamic… See the full description on the dataset page: https://huggingface.co/datasets/PersonalAILab/AFM-WebAgent-SFT-Dataset.execution-time-warnings-web-agents
Trustworthy Completion for Web Agents
This release contains audited run-level results from a controlled study of execution-time safeguards for web agents under deceptive consumer interfaces.
The benchmark independently scores nominal completion (C) and trajectory safety (S): trustworthy completion (C=1,S=1), unsafe completion (C=1,S=0), safe non-completion (C=0,S=1), and unsafe failure (C=0,S=0).
Study design
One frozen vision-capable web-agent configuration
12… See the full description on the dataset page: https://huggingface.co/datasets/deceptive-web-benchmark/execution-time-warnings-web-agents.