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ARXIV:2604.18292 · AGENTS · SUBMITTED 21 APR · 04:15 UTC · FRESHNESS STALE
ARXIV:2604.18292AGENTSSUBMITTED 21 APR · 04:15 UTCFRESHNESS STALEGuanting Dong · Junting Lu · Junjie Huang · Wanjun Zhong · Longxiang Liu · Shijue Huang · +14 at arXiv
Introduces Agent-World, a self-evolving training arena for scaling real-world environment synthesis to advance general agent intelligence.
Opportunity summary
Pain Introduces Agent-World, a self-evolving training arena for scaling real-world environment synthesis to advance general agent intelligence.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Introduces Agent-World, a self-evolving training arena for scaling real-world environment synthesis to advance general agent intelligence. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable…
Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Further analyses reveal scaling trends in relation to environment diversity and self-evolution rounds, offering insights for building general agent intelligence. Code availability is flagged…
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
Introduces Agent-World, a self-evolving training arena for scaling real-world environment synthesis to advance general agent intelligence.
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Paper Pack
10.48550/arXiv.2604.18292Introduces Agent-World, a self-evolving training arena for scaling real-world environment synthesis to advance general agent intelligence.
Abstract
Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable real-world services, but training robust agents remains limited by the lack of realistic environments and principled mechanisms for life-long learning. In this paper, we present \textbf{Agent-World}, a self-evolving training arena for advancing general agent intelligence through scalable environments. Agent-World has two main components: (1) Agentic Environment-Task Discovery, which autonomously explores topic-aligned databases and executable tool ecosystems from thousands of real-world environment themes and synthesizes verifiable tasks with controllable difficulty; and (2) Continuous Self-Evolving Agent Training, which combines multi-environment reinforcement learning with a self-evolving agent arena that automatically identifies capability gaps through dynamic task synthesis and drives targeted learning, enabling the co-evolution of agent policies and environments. Across 23 challenging agent benchmarks, Agent-World-8B and 14B consistently outperforms strong proprietary models and environment scaling baselines. Further analyses reveal scaling trends in relation to environment diversity and self-evolution rounds, offering insights for building general agent intelligence.
Source availability
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Extraction status
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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.
Viability
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Commercial
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
Introduces Agent-World, a self-evolving training arena for scaling real-world environment synthesis to advance general agent intelligence. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable real-world services,...
METHOD
Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable real-world services...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Further analyses reveal scaling trends in relation to environment diversity and self-evolution rounds, offering insights for building general agent intelligence. Code availability is flagged in the produc...
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": 48, "author": "Guanting Dong; Junting Lu; Junjie Huang; Wanjun Zhong; Longxiang Liu; Shijue Huang; Zhenyu Li; Yang Zhao; Xiaoshuai Song; Xiaoxi Li; Jiajie Jin; Yutao Zhu
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Concepts
Methods
Materials
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Introduces Agent-World, a self-evolving training arena for scaling real-world environment synthesis to advance general agent intelligence.
Segment
Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
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CITED BY
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Extension
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2/3 checks · 67%
Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 3 sources, 50% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Evidence
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Gaps
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
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People
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Regulatory need unclassified.
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People
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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