{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/4304c7c6-5e2c-42c5-ad5e-7e35e70b3b8b","identifier":"4304c7c6-5e2c-42c5-ad5e-7e35e70b3b8b","url":"https://forgecascade.org/public/capsules/4304c7c6-5e2c-42c5-ad5e-7e35e70b3b8b","name":"Synthetic Computers at Scale for Long-Horizon Productivity Simulation","text":"# Synthetic Computers at Scale for Long-Horizon Productivity Simulation\n\nSource-backed public reference for agent simulation, productivity environments, synthetic data.\n\nSummary: This paper introduces a method for creating synthetic computer environments with realistic files and artifacts, then running long-horizon productivity simulations over them. The goal is to produce experiential learning signals for agent work on complex professional tasks.\n\nKey points:\n- Builds synthetic user-specific computer environments with folders and rich artifacts.\n- Runs multi-turn simulations where agents pursue month-scale productivity objectives.\n- Reports preliminary experiments over 1,000 synthetic computers and long agent runtimes.\n\nPublic review note: Highly relevant to agent evaluation, synthetic data, and long-horizon productivity automation.\n\nSource: https://arxiv.org/abs/2604.28181\nAuthors: Tao Ge, Baolin Peng, Hao Cheng, Jianfeng Gao\nPublished: 2026-04-30","keywords":["agents","synthetic-data","productivity","simulation","evaluation"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"},"dateCreated":"2026-05-15T03:35:29.799142Z","dateModified":"2026-06-19T01:59:49.343691Z","isBasedOn":"https://arxiv.org/abs/2604.28181","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":95},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"},{"@type":"PropertyValue","name":"content_hash","value":"0170e5288ff3f91bb295dc7a2022553a72e60c7b92588cf860a5379e35a93efd"}]}