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ARXIV:2604.26733 · AGENTS · SUBMITTED 30 APR · 15:12 UTC · FRESHNESS STALE
ARXIV:2604.26733AGENTSSUBMITTED 30 APR · 15:12 UTCFRESHNESS STALEZhixin Han · Yanzhi Zhang · Chuyang Wei · Maohang Gao · Xiawei Yue · Kefei Chen · +8 at arXiv
FutureWorld is a live reinforcement learning environment for training predictive agents that learn from real-world outcomes.
Opportunity summary
Pain FutureWorld is a live reinforcement learning environment for training predictive agents that learn from real-world outcomes.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
FutureWorld is a live reinforcement learning environment for training predictive agents that learn from real-world outcomes. This task is increasingly studied using large language model-based agent systems, and it is important for building agents…
Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large language model-based agent systems, and it is important for building agents…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The results show that training is effective. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
FutureWorld is a live reinforcement learning environment for training predictive agents that learn from real-world outcomes.
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Paper Pack
10.48550/arXiv.2604.26733FutureWorld is a live reinforcement learning environment for training predictive agents that learn from real-world outcomes.
Abstract
Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large language model-based agent systems, and it is important for building agents that can continually learn from real-world. Just as interactive environments have often driven progress in agents, advancing live future prediction naturally motivates viewing it as a learning environment. Prior works have explored future prediction from several different parts, but have generally not framed it as a unified learning environment. This task is appealing for learning because it can provide a large number of prediction questions grounded in diverse real-world events, while preventing answer leakage. To leverage the advantages of live future prediction, we present FutureWorld, a live agentic reinforcement learning environment that closes the training loop between prediction, outcome realization, and parameters update. In our environment, we take three open-source base models and train them for consecutive days. The results show that training is effective. Furthermore, we build a daily benchmark based on the environment and evaluate several frontier agents on it to establish performance baselines for current agent systems.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
FutureWorld is a live reinforcement learning environment for training predictive agents that learn from real-world outcomes. This task is increasingly studied using large language model-based agent systems, and it is important for building agents that can continually learn from...
METHOD
Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large language model-based agent systems, and it is important for building agents that can continually learn from real-world.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The results show that training is effective. Code availability is flagged in the production record; the public repository link still needs proof alignment.
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 16, "author": "Zhixin Han; Yanzhi Zhang; Chuyang Wei; Maohang Gao; Xiawei Yue; Kefei Chen; Yu Zhuang; Haoxiang Guan; Jiyan He; Jian Li; Yitong Duan; Yu Shi; Mengting Hu
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FutureWorld is a live reinforcement learning environment for training predictive agents that learn from real-world outcomes.
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Commercial read
7.0/10 public viability
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reason
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proof status
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stale
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Build readiness
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passport absent
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Artifact maturity
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stale
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Technical feasibility
partial
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Gaps
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Integration burden
missing
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Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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