WebDreamer
[TMLR'25] "Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents"
Our paper tackles the critical question: “How to scale inference-time compute for language agents?” The solution lies in using LLMs as a world model of the internet to predict the outcomes of actions on websites. Our method, WebDreamer, employs LLM-based simulation for speculative planning on the web, surpassing reactive baselines while offering greater safety and flexibility compared to tree search methods. All resources (including training data and resulting models) are available at HF Collection.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/osu-nlp-group-webdreamer — read its card at https://meshkore.com/agent/osu-nlp-group-webdreamer/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/osu-nlp-group-webdreamerFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/osu-nlp-group-webdreamer/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own WebDreamer?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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