Semantic Spacetime

Semantic Spacetime

Semantic Spacetime (SST) is Mark Burgess's discrete, graph-theoretic model of meaning over time. A semantic element is one autonomous agent plus its scalar promises; a semantic spacetime is a collection of such elements in which a local change in state, promises, or configuration is a local unit of time. Time is proper time — there is no global clock (the precedence view Burgess credits to Lamport). Causality is cooperative: every adjacency requires an offer (+) and an acceptance (−) promise on both ends, so space is made of cooperating nodes and edges. The 2025 γ(3,4) formalism types the graph: three node meta-types (events, things, concepts) connected by four link types (0 = NEAR, ±1 = LEADS TO, ±2 = CONTAINS, ±3 = EXPRESSES). Absorbing states in partial graphs leak information, and intentionality enters at the boundary. SST is built on Promise Theory — for the promise vocabulary, load promise-theory instead of re-deriving it here. This skill is a thin router: load the dense material only when a row in Load By Need matches your task.

When to use

• When you need to design or analyze shared semantic ground between agents — model what "meaning" means in this system (what does a concept, term, or promise mean to whom), producing a γ(3,4) map of the shared semantic ground as the artifact.
• When you need to model intent or meaning over time — trajectories, drift, and convergence of understanding between agents, agents and humans, or agents and their instructions; the artifact is a semantic trajectory with recorded observations.
• When you need to design convergent, self-healing coordination — a loop in which state is continuously measured against a desired meaning and repaired toward it; model the loop as semantic elements whose local change is time.
• When you need to diagnose semantic drift, divergence, or dead-ends — absorbing states, meaning gaps, and non-converging agents; the artifact is a drift finding with the leaking boundary identified.
• When you need to map promises onto spacetime — trajectories, promise propagation, and causality between agents; model each promise as an edge and trace how intent propagates through the graph.
• When you need to analyze temporal blindness in agents — state tracking, event ordering, and causality failures where an agent cannot tell what happened before what; model event order via proper time instead of a shared clock.

When not to use

• Physics or relativity — SST is not a theory of quantum gravity or spacetime physics; it assumes no manifold structure and no momentum. Do not use it for physics problems; those belong to a physics domain.
• Pure vector embeddings, RAG, or semantic search without temporal-causal structure — a static embedding index has no proper time, no causality, and no trajectories to model; route to the embedding or semantic-search tool's own skill instead.
• Enforceable centralized control — if you can command and verify compliance directly, SST's cooperative-promise machinery is overhead, not insight (the same boundary promise-theory draws); route to promise-theory when you need the control-vs- cooperation discussion.
• Simple single-agent prompting — one model and one prompt with no delegation or meaning space to model needs no spacetime vocabulary.
• Tool manuals or framework documentation — routing to the tool's own skill is always better than framing the tool with SST.

Load By Need

Quick Start

The bundled CLI (scripts/semantic-spacetime.py) is stdlib-only — any python3 runs it, nothing to install — and every command is read-only. Run the commands below from the repository root; the CLI resolves no files relative to its own location, so the same commands work from any directory with absolute paths.

1. Draft an SST model. Copy templates/sst-model.yaml.tmpl to a working file (for example sst-model.yaml) and replace the example values: declare agents (id, role, promises), semantic nodes (id, type in {event, thing, concept}), edges (from, to, link in -3..3), acceptances, trajectories, and observations. The machine-delimited block between # --- example --- and # --- end example --- shows a complete, valid model to imitate; the same model is committed, fully filled, at tests/fixtures/sample-model.yaml.
2. Lint it against the sst-model-v1 format — exit 0 prints a coverage summary, exit 1 prints named violations: python3 semantic-spacetime/scripts/semantic-spacetime.py model lint semantic-spacetime/tests/fixtures/sample-model.yaml
3. Map the γ(3,4) graph (--format is one of text | mermaid | json): python3 semantic-spacetime/scripts/semantic-spacetime.py model map semantic-spacetime/tests/fixtures/sample-model.yaml --format mermaid
4. Measure semantic distance — weighted hop count (each hop weighs |link| + 1): python3 semantic-spacetime/scripts/semantic-spacetime.py model distance semantic-spacetime/tests/fixtures/sample-model.yaml --from report-event --to drift-concept
5. Trace trajectories — every simple path with link types annotated; cycles are noted and the enumeration terminates on any finite model: python3 semantic-spacetime/scripts/semantic-spacetime.py model trajectory semantic-spacetime/tests/fixtures/sample-model.yaml --from report-event --to drift-concept
6. Diff two snapshots — added/removed/changed semantic regions; identical snapshots report no drift. Point the command at your two snapshot files (running it on the same file twice demonstrates the no-drift case): python3 semantic-spacetime/scripts/semantic-spacetime.py model drift semantic-spacetime/tests/fixtures/sample-model.yaml semantic-spacetime/tests/fixtures/sample-model.yaml
7. Machine-readable output. Append --json to any command for a single JSON object on stdout. --dry-run is accepted everywhere as a no-op guard.
8. Draft the analysis report. Copy templates/sst-analysis.md.tmpl to a working file (for example sst-analysis.md) and fill the skeleton: system description → semantic spacetime map → drift/divergence/absorbing-state findings → interventions → verification/measurement plan.
9. Diagnose drift when agents disagree. If agents diverge, treat the disagreement as an observation, measure the semantic distance between their interpretations, and locate the absorbing state or leaking boundary where information stops flowing.