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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2604.10589 |
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| _version_ | 1866913024927858688 |
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| author | Riscos, Pablo de los Corbacho, Fernando J. Arbib, Michael A. |
| author_facet | Riscos, Pablo de los Corbacho, Fernando J. Arbib, Michael A. |
| contents | We introduce a hierarchical categorical framework for Schema-Based Learning (SBL) structured across four interconnected levels. At the schema level, a free multicategory $Sch_{syn}$ encodes fundamental schemas and transformations. An implementation functor $\mathcal{I}$ maps syntactic schemas to representational languages, inducing via the Grothendieck construction the total category $Sch_{impl}$. Implemented schemas are mapped by a functor $Model$ into the Kleisli category $\mathbf{KL(G)}$ of the Giry monad, yielding probabilistic models, while an instances presheaf assigns evaluated instance spaces. A semantic category $Sch_{sem}$, defined as a full subcategory of $\mathbf{KL(G)}$, provides semantic grounding through an interpretation functor from $Sch_{impl}$.
At the agent level, $Sch_{impl}$ is equipped with a duoidal structure $\mathcal{O}_{Sch}$ supporting schema-based workflows. A left duoidal action on the category $Mind$ enables workflow execution over mental objects, whose components include mental spaces, predictive models, and a cognitive kernel composed of memory and cognitive modules. Each module is specified by schema-typed interfaces, duoidal workflows, a success condition, and a logical signature. Memory is formalized categorically via memory subsystems, a presheaf $Data_M$, a monoidal operation category $Ops_M$, and read/write natural transformations. Together with the $Body$ category, Mind defines the embodied SBL agent.
At higher levels, SBL is represented as an object of the agent architecture category $ArchCat$, enabling comparison with heterogeneous paradigms, while the $World$ category models multi-agent and agent-environment interactions. Altogether, the framework forms a weak hierarchical $n$-categorical structure linking schema semantics, cognition, embodiment, architectural abstraction, and world-level interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10589 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Working Paper: Towards Schema-based Learning from a Category-Theoretic Perspective Riscos, Pablo de los Corbacho, Fernando J. Arbib, Michael A. Artificial Intelligence We introduce a hierarchical categorical framework for Schema-Based Learning (SBL) structured across four interconnected levels. At the schema level, a free multicategory $Sch_{syn}$ encodes fundamental schemas and transformations. An implementation functor $\mathcal{I}$ maps syntactic schemas to representational languages, inducing via the Grothendieck construction the total category $Sch_{impl}$. Implemented schemas are mapped by a functor $Model$ into the Kleisli category $\mathbf{KL(G)}$ of the Giry monad, yielding probabilistic models, while an instances presheaf assigns evaluated instance spaces. A semantic category $Sch_{sem}$, defined as a full subcategory of $\mathbf{KL(G)}$, provides semantic grounding through an interpretation functor from $Sch_{impl}$. At the agent level, $Sch_{impl}$ is equipped with a duoidal structure $\mathcal{O}_{Sch}$ supporting schema-based workflows. A left duoidal action on the category $Mind$ enables workflow execution over mental objects, whose components include mental spaces, predictive models, and a cognitive kernel composed of memory and cognitive modules. Each module is specified by schema-typed interfaces, duoidal workflows, a success condition, and a logical signature. Memory is formalized categorically via memory subsystems, a presheaf $Data_M$, a monoidal operation category $Ops_M$, and read/write natural transformations. Together with the $Body$ category, Mind defines the embodied SBL agent. At higher levels, SBL is represented as an object of the agent architecture category $ArchCat$, enabling comparison with heterogeneous paradigms, while the $World$ category models multi-agent and agent-environment interactions. Altogether, the framework forms a weak hierarchical $n$-categorical structure linking schema semantics, cognition, embodiment, architectural abstraction, and world-level interaction. |
| title | Working Paper: Towards Schema-based Learning from a Category-Theoretic Perspective |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.10589 |