Structured Cognition for Agent Fleets

Temporal is more than a timestamp. Every memory in Meaning Memory carries three time signals: when it was observed, when it becomes valid, and when it stops mattering.

Decay curves are operator-configurable per playbook. Some memories decay fast (today's traffic numbers). Some stay forever (a customer's allergy). Some don't decay but expire on a deadline (a paused deploy that resumes after security review). Meaning Memory tracks both decay and validity windows independently.

KV caches solve freshness with TTL. Vector stores solve recency with score boosts. Neither captures the truth that some memories fade, some expire, and some stay forever, and which is which is a governance question, not a heuristic.

Asymmetry, also called Attribution, is the dimension most memory systems treat as post-hoc filtering. In Meaning Memory, every memory carries explicit writer attribution and an attribution-register score native to the data model, with per-perceiver retrieval-policy modifiers derived from it; corroboration-based warrant scoring on the roadmap.

In multi-agent deployments this matters enormously. The same statement ("our churn rate is 4.2%") from the CFO and from a customer comment should not carry the same retrieval weight. Asymmetry encodes the attribution-register gradient at the schema level so register-aware retrieval is possible without bolt-on filters.

Enterprise multi-agent fleets are political ecosystems. Marketing's agent sees one version of "true." Engineering's sees another. Legal's sees a third. Without attribution as a first-class primitive, cross-agent memory becomes either dangerously homogenized or frustratingly siloed. Asymmetry lets you have shared facts with register-aware retrieval.

Relational turns memories into a typed graph. Every memory can connect to other memories, agents, documents, customers, projects, people, concepts, events, or commitments. Nine bounded target types ship today, plus a custom:* namespace for domain-specific extensions.

Production R-dim today means clean schema primitives: vocabulary normalization, typed edges with provenance, predicate-gated retrieval, and 1-hop filters via mm_search(related_to=, min_r=). Vector similarity surfaces "memories that look like this." Relational edges surface "memories that matter to this."

Enterprise agent fleets need explicit linkage between memories, customers, and commitments, with audit-grade provenance on every edge. That is the shipped Relational contract. Multi-hop graph traversal remains a research track; LoCoMo-scale benchmarks have not shown proven retrieval lift at current architecture (pending further benchmark validation).

Episodic groups memories into bounded arcs. A customer conversation isn't a list of facts, it's an episode with a beginning and a resolution. Most memory systems flatten that structure. Meaning Memory preserves it.

Every memory can carry an episode_id. Episode summaries (mm_episode_summary, mm_search(episode_id=)) ship stable on both backends. Multi-step narrative clustering is beta and opt-in (MM_E_CLUSTER_MODE_B_ENABLED); the deterministic narrative renderer runs only when Mode B is enabled.

The unit of recall in human memory is not the fact. It's the episode. Episode summaries give operators and agents a bounded working set without loading every underlying memory. Narrative clustering adds chronology when you opt in.