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Evidence record 7646 · automatically gathered

WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces

We introduce WebChain, the largest open-source dataset of human-annotated trajectories on real-world websites, designed to accelerate reproducible research in web agents. It contains 31,725 trajectories and 318k steps, featuring a core Triple Alignment of visual, structural, and action data to provide rich, multi-modal supervision. The data is collected via a scalable pipeline that ensures coverage of complex, high-value tasks often missed by synthetic methods. Leveraging this dataset, we propos

Record details

Published: 5 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Safety & alignment · Shared topic: Agents & autonomy

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Shared topic: Safety & alignment · Shared topic: Agents & autonomy

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Shared topic: Safety & alignment · Shared topic: Agents & autonomy

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Shared topic: Safety & alignment · Shared topic: Agents & autonomy

Information-Theoretic Constraints for Continual Vision-Language-Action Alignment

arXiv · 6 March 2026

Shared topic: Safety & alignment · Shared topic: Agents & autonomy

Boosting deep Reinforcement Learning using pretraining with Logical Options

arXiv · 6 March 2026

Shared topic: Safety & alignment · Shared topic: Agents & autonomy

LieCraft: A Multi-Agent Framework for Evaluating Deceptive Capabilities in Language Models

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How to cite this record

ethics.ai (5 March 2026), “WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces,” evidence record 7646, https://ethics.ai/record/7646 (originally published by arXiv).

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