Temporal Model

MIF’s temporal model answers OKF’s open “live vs. stale” question with validity windows and freshness. It uses a bi-temporal model distinguishing between:

1. Transaction Time: When the concept was recorded in the system
2. Valid Time: When the fact represented by the concept is true

Temporal Properties

Property	Type	Description
validFrom	DateTime	When fact becomes valid
validUntil	DateTime	When fact expires (null = indefinite)
recordedAt	DateTime	When recorded (transaction time)
ttl	Duration	Time-to-live (ISO 8601 duration)
decay	Object	Decay model parameters
accessCount	Integer	Times accessed
lastAccessed	DateTime	Last access time

Decay Models

Model	Formula	Use Case
none	No decay	Permanent concepts
linear	strength = 1 - (t / ttl)	Simple linear decay
exponential	strength = e^(-t/halfLife)	Gradual freshness decay
step	strength = 1 if t < ttl else 0	Hard expiration

Freshness Rationale

In the core model, the temporal decay function expresses freshness — how current a piece of knowledge is — and answers OKF’s open “live vs. stale” question. The decay half-life defaults (P7D, P14D, P30D) are pragmatic defaults for how quickly knowledge of a given kind loses currency; they are not prescriptive.

The strength = e^(-t/halfLife) curve models a value that is fully current when recorded and decays gradually toward stale, with validFrom/validUntil windows bounding the interval in which a fact is asserted to hold.

Half-Life Defaults

Half-Life	Use Case	Rationale
P7D	Short-term context	Aligns with weekly work cycles
P14D	Medium-term projects	Spans typical sprint/iteration boundaries
P30D	Long-term knowledge	Corresponds to monthly review cycles
P90D	Default TTL	Quarterly relevance for most organizational knowledge

Implementations SHOULD tune these based on:

• Knowledge kind (episodic records go stale faster than semantic facts)
• Organizational context (high-velocity vs. stable environments)
• Access patterns (frequently accessed knowledge can reinforce slower decay)

The lastAccessed and accessCount fields let implementations model reinforcement — each access can reset or slow the freshness decay.

The cognitive-memory rationale that originally motivated this exponential curve — including the underlying experimental references and decay tuning for retrieval-oriented systems — lives in the AI Memory profile (profiles/ai-memory/), keeping the core framed purely as freshness.

Example

temporal:
  validFrom: 2026-01-15T00:00:00Z
  validUntil: null
  recordedAt: 2026-01-15T10:30:00Z
  ttl: P90D
  decay:
    model: exponential
    halfLife: P7D
    strength: 0.85
    lastReinforced: 2026-01-18T09:00:00Z
  accessCount: 5
  lastAccessed: 2026-01-20T14:22:00Z