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Magnus Hedemarkandfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> 2c78dfcca3 feat(semantic-spacetime): add format-compliant M1 skill core 
Add the semantic-spacetime skill skeleton: a thin SKILL.md router with
triggers/anti-triggers, Load By Need, Quick Start, Related Skills, gotchas,
and exit conditions; a human-facing README; MIT license; deep
provenance-marked theory references (foundations, glossary, bibliography);
the versioned sst-model-v1 template pair; a 6-case schema-v1 eval manifest;
the root README catalog entry; regenerated marketplace and llms artifacts;
and the skill-triggers row.

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
2026-08-12 22:33:47 -04:00

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Bibliography — Primary Sources for Semantic Spacetime

Load this file when you need to find or verify a source — which paper says X, or where a claim in foundations.md or glossary.md comes from. Every entry below carries a URL that was checked for reachability during the research phase (2026-08-12); the [VERIFIED]/[UNVERIFIED] markers describe the source's verification level as used in foundations.md. No entry here is fabricated: each appears in the research corpus's source lists. For the promise-theory substrate sources, cross-reference the promise-theory skill's own bibliography.

1. The Semantic Spacetime series (Burgess)

  • Burgess, M. Spacetimes with Semantics (2014). arXiv:1411.5563 [cs.MA]. Part I: "From Einstein to Milner." Defines the agenda: relationships between objects constitute space; their change is time; observer semantics are integral to spacetime. [VERIFIED] URL: https://arxiv.org/abs/1411.5563
  • Burgess, M. Spacetimes with Semantics (II): Scaling of agency, semantics, and tenancy (2015). arXiv:1505.01716 [cs.MA]. Part II: how agency scales via super-agents/sub-spaces; scalar vs. vector promises; occupancy and tenancy. [VERIFIED] URL: https://arxiv.org/abs/1505.01716
  • Burgess, M. Spacetimes with Semantics (III): The Structure of Functional Knowledge Representation and Artificial Reasoning (2016, rev. 2017). arXiv:1608.02193 [cs.AI]. Part III (122 pages), the most formal document: Definitions 1-9, Lemmas 1-3, the four irreducible associations, the learning/knowledge formalism. [VERIFIED] URL: https://arxiv.org/abs/1608.02193 Full text: https://arxiv.org/html/1608.02193v4
  • Burgess, M. On the scaling of functional spaces, from smart cities to cloud computing (2016). arXiv:1602.06091 [cs.CY]. The "functional space" reading of SST applied to the empirically observed power-law scaling of cities. [VERIFIED] URL: https://arxiv.org/abs/1602.06091
  • Burgess, M. A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale Learning (2017). arXiv:1702.04638 [cs.AI]. A hybrid reasoning/pattern-recognition architecture as the ML instantiation of SST reasoning. [VERIFIED] URL: https://arxiv.org/abs/1702.04638
  • Burgess, M. Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (I): Bootstrapping Meaning from Episodic Narrative viewed as a Feature Landscape (2020). arXiv:2010.08126 [cs.AI]. SST's empirical arm: parsing narrative via measurable size/time cues as an event "landscape"/interferometry; concepts as process invariants. [VERIFIED] URL: https://arxiv.org/abs/2010.08126
  • Burgess, M. Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (II): Establishing the Geometry of Invariant Concepts, Themes, and Namespaces (2020). arXiv:2010.08125 [cs.AI]. Part II: reconstructing concepts via multiscale interferometry based on the four fundamental spacetime relationships. [VERIFIED] URL: https://arxiv.org/abs/2010.08125
  • Burgess, M.; Gerlits, A. Continuous Integration of Data Histories into Consistent Namespaces (2022). arXiv:2204.00470 [cs.DC]. Versioned coordinates / namespaces for data pipelines — SST's temporal-consistency scheme. [VERIFIED] URL: https://arxiv.org/abs/2204.00470
  • Burgess, M. Agent Semantics, Semantic Spacetime, and Graphical Reasoning (2025). arXiv:2506.07756 [cs.AI]. The current formal statement: the γ(3,4) representation (3 node meta-types × 4 link types), the nine typing design rules, absorbing states as information leaks, causal-set kinship. [VERIFIED] URL: https://arxiv.org/abs/2506.07756 Full text: https://arxiv.org/html/2506.07756v2
  • Burgess, M. On The Role of Intentionality in Knowledge Representation: Analyzing Scene Context for Cognitive Agents with a Tiny Language Model (2025). arXiv:2507.10000 [cs.AI]. Intentionality in data via scale separation. [VERIFIED] URL: https://arxiv.org/abs/2507.10000
  • Burgess, M. γ(3,4) 'Attention' in Cognitive Agents: Ontology-Free Knowledge Representations With Promise Theoretic Semantics (2025). arXiv:2512.19084 [cs.AI]. SST as a bridge between vectorized ML and knowledge graphs without relying on language models implicitly. [VERIFIED] URL: https://arxiv.org/abs/2512.19084

2. Author's web project pages and essays

3. Spacetime-Entangled Networks and consensus

  • Borrill, P.; Burgess, M.; Karp, A.; Kasuya, A. Spacetime-Entangled Networks (I): Relativity and Observability of Stepwise Consensus (2018, rev. 2020). arXiv:1807.08549 [cs.DC]. SST/promise semantics at the consensus layer: entanglement as co-dependent evolution of state; promises of sequential, in-order, atomically confirmed delivery. [VERIFIED] URL: https://arxiv.org/abs/1807.08549

4. Promise theory (the substrate)

5. Adjacent and contextual work

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