Unverified50% confidenceFactExact time
OptionWorld simulates everyday life tasks and traces the full chain of information acquisition, judgment, action, and outcome to precisely locate where the model fails.
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAn agent-type work environment allows models to decompose complex goals, invoke tools across steps (such as web browsing, code execution, and file reading/writing), and autonomously decide the next action, which is distinct from traditional single-turn retrieve-and-answer AI search.68% similarUnverifiedPlan mode is a human-machine collaboration paradigm that includes steps such as requirement parsing, task decomposition, dependency identification and risk checking, allows the AI to ask the user clarifying questions when information is insufficient, and embodies the "human-in-the-loop" interaction principle.67% similarUnverifiedAnyGoal constructs "goal-shift" task variants built on widely used world model environments.67% similarUnverifiedThe World Models paper used OpenAI's CarRacing-v0 and VizDoom as experimental platforms, demonstrating that an agent can complete policy training entirely within its own learned environment model.67% similarUnverifiedWorldClaw breaks tasks down into multiple agent stages executed sequentially, first converting the user's vague ideas into a detailed planning scheme that includes zone division, terrain types, object distribution, material selection, and spatial relationships.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1078894API
curl https://kongchang.com/api/v1/knowledge/claims/1078894MCP
get_claim(id=1078894)