arXiv:2606.00750v1 Announce Type: new Abstract: Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, existing document agents mainly transform papers into static artifacts such as summaries, webpages, or slides, which are insufficient for technical papers involving dynamic mechanisms and state transitions. In this work, we propose a Paper-to-Interactive-System Agent that converts research papers into executable interactive web systems. Given a PDF paper, the agent performs end-to-end processing without hu
Advances in visual language models are enabling more sophisticated autonomous agents, leading to the development of systems beyond static document processing.
This work represents a key step towards AI agents producing not just summaries but fully interactive and executable systems from technical content, significantly enhancing the utility of LLMs in R&D.
The ability to transform static research papers into dynamic, interactive web applications fundamentally changes how scientific knowledge can be disseminated, consumed, and even experimented with.
- · Researchers and Scientists
- · Technical Documentation
- · Software Development Tools
- · AI Agent Developers
- · Static Publishing Models
- · Manual Web Development
- · Purely Text-Based Information Consumption
Research papers become living, executable environments rather than passive texts, accelerating experimentation and understanding.
The development and testing of new technologies could be greatly automated, impacting product development cycles and intellectual property generation.
This could lead to a new paradigm of scientific discovery where AI agents not only process papers but autonomously create interactive experiments and simulations based on new research.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL