What question did this study set out to answer?

The aim is to establish a governance-first architecture for creating auditable agentic world models, addressing limitations in current large-model practices.

May 8, 2026Open Access

DOORM WorldModel-OS: A Governance-First Architecture for Auditable Agentic Reasoning — Hybrid State-Space, Structurally-Primed Discrete Anchors, and Anti-Scale Training Discipline

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Authors

LTLi TongChinese Medical Doctor Association TTTong TongChinese Medical Doctor Association LWLihua Wu

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Implication

This framework proposes a governance-first architecture for auditable models, emphasizing alignments in agentic reasoning applications.

Key Points

The aim is to establish a governance-first architecture for creating auditable agentic world models, addressing limitations in current large-model practices.
Introduced a hybrid local/cloud operating system combining cognitive increments with discrete state spaces.
Implemented safety through a four-level rollback contract in inference traces.
Designed training using a five-class grounded-citation data pipeline with hard gates for evaluation.
No empirical results were claimed in this version; emphasis is on reproducibility and detailed design specifications.
Outlined non-coverage criteria and migration cost estimates across domains, as well as refutation criteria.

Cite This Study

Tong et al. (2026) studied this question.

synapsesocial.com/papers/69fd7f86bfa21ec5bbf07fd1 https://doi.org/10.5281/zenodo.20053545

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DOORM WorldModel-OS: A Governance-First Architecture for Auditable Agentic Reasoning — Hybrid State-Space, Structurally-Primed Discrete Anchors, and Anti-Scale Training Discipline

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Member takes

Implication

Key Points

Cite This Study

DOORM WorldModel-OS: A Governance-First Architecture for Auditable Agentic Reasoning — Hybrid State-Space, Structurally-Primed Discrete Anchors, and Anti-Scale Training Discipline

Discussion

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Implication

Key Points

Cite This Study

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