The map, today
Cells already partially occupied.
The map is not blank. Decades of work have placed real systems at known coordinates. Each entry below names a class of running system, its coordinate, and which of the three temporal functions — memory, context, understanding — it holds first-class. The cells that hold all three are sparse.
O1
Cognitive architectures (ACT-R, Soar, CLARION, Sigma, LIDA)
F=integrated · K=OR-hierarchical · C=sequential to parallel · I=encapsulated · R=event-driven · A=introspectable · T=L₁ · V=invariant-tested · τ=checkpointed
holds: memory · context · understanding
The convergent integrated architecture the field has been building since Newell (1990). Working memory plus long-term declarative plus long-term procedural memory, perception and action coupled. The Common Model of Cognition (2017) is its abstract characterization.
O2
Durable workflow engines
F=HSM · K=OR-hierarchical · C=distributed · I=process · R=event-driven · A=introspectable · T=L₁ · V=invariant-tested · τ=durable
holds: memory · context
State-machine orchestration over an event log, with the running state surviving host restarts. Memory and context are first-class; understanding is left to whatever queries the engine.
O3
Hierarchical statechart applications
F=HSM · K=OR-hierarchical · C=sequential · I=encapsulated · R=event-driven · A=opaque · T=L₀ · V=model-checked · τ=ephemeral
holds: context (with history pseudostates → partial memory)
The dominant cell for reactive embedded and UI logic. Hierarchy gives context; history pseudostates give partial memory; runtime introspection and forward reasoning are absent.
O4
Erlang/OTP supervision trees
F=actor · K=OR-hierarchical · C=distributed · I=process · R=event-driven · A=runtime-modifiable · T=L₁ · V=invariant-tested · τ=durable
holds: memory (per-actor) · context (per-actor)
Per-actor state, supervised hierarchically, with hot code replacement. Memory and context within each actor; cross-actor understanding is left to the protocol.
O5
Memory-augmented neural networks (NTM, DNC, memory networks)
F=agent graph · K=atomic · C=parallel · I=encapsulated · R=event-driven · A=opaque · T=L₂ · V=unverified · τ=ephemeral
holds: episodic memory (substrate-level)
Neural networks with explicit external memory matrices, attention-based read and write. Graves and colleagues at DeepMind demonstrated reasoning over family trees and the London Underground. Episodic memory becomes substrate-level; context and understanding remain implicit.
O6
Hierarchical Temporal Memory
F=HTM · K=OR-hierarchical · C=parallel · I=encapsulated · R=reactive · A=introspectable · T=L₁ · V=unverified · τ=checkpointed
holds: memory · partial understanding (sequence prediction)
Cortical-column-inspired sequence learning over sparse distributed representations. Memory and forward prediction held by construction; verified reasoning over the future is not the design point.
O7
Modern LLM-agent memory stacks
F=agent graph · K=sequential · C=sequential · I=encapsulated · R=event-driven · A=opaque · T=L₂ · V=unverified · τ=checkpointed
holds: memory · context
Working scratchpad plus episodic retrieval plus semantic stores plus a context window, coordinated by an orchestrating graph. Memory and context are explicit; understanding is folded into the model that consumes them.
O8
TLA⁺ specifications (offline verification)
F=TLA⁺ spec · K=AND-parallel · C=any · I=any · R=any · A=opaque-at-runtime · T=L₂ · V=model-checked · τ=ephemeral
holds: understanding (offline, over reachable states)
Specification-time reasoning about every reachable state of a system, exhaustively. Understanding of the future is rigorous; the runtime that the specification describes is a separate artifact.
These are coordinates, not rankings. Each cell holds what it was built to hold; the periodic structure makes legible which functions are first-class at each and which are absent. The cells that hold all three at once — the temporal cells — are the ones the predictive face below names structurally.