MCCF Design Proposal

Selective Persistence: A Coherence-Based Architecture for Episodic Memory

Abstract

This proposal introduces Selective Persistence, a memory architecture for the Multi-Channel Coherence Field (MCCF). Rather than treating memory as an ever-growing transcript of past interactions, Selective Persistence views memory as a process of coherence-preserving compression. The objective is not perfect recall but sustained identity, adaptive behavior, and creative intelligence.

Inspired by recent information-theoretic work on diffusion models, Selective Persistence proposes that creative behavior emerges between two undesirable extremes:

Over-retention, leading to repetition and rigidity.
Under-retention, leading to incoherence and randomness.

The optimal operating point preserves the relationships that matter while allowing details to decay.

Motivation

Most conversational AI systems implement episodic memory by storing conversations and retrieving relevant passages for future prompts.

This approach has several limitations:

Prompt growth
Retrieval complexity
Increasing computational cost
Literal repetition
Reduced creativity

Humans appear to function differently.

We rarely remember conversations verbatim.

Instead we retain:

changes in relationships,
unresolved intentions,
emotional significance,
symbolic associations,
lessons learned.

MCCF should adopt a similar philosophy.

The goal of memory is not archival fidelity.

The goal of memory is behavioral continuity.

Core Principle

Memory is attention extended through time.

Attention determines what influences the current moment.

Selective Persistence determines what influences future moments.

Attention answers:

What deserves awareness now?

Selective Persistence answers:

What deserves remembrance later?

These processes are temporal counterparts.

Creative Intelligence

Recent theoretical work on Bayesian diffusion models suggests that successful generation occurs near a phase transition between memorization and generalization.

This proposal extends that insight.

Creative intelligence operates between:

literal reproduction,
and unconstrained randomness.

Creativity emerges through controlled forgetting.

Too much retained information produces imitation.

Too little produces noise.

Selective Persistence regulates this balance.

Memory as State Transition

Instead of preserving transcripts, MCCF preserves changes in the coherence field.

Each completed scene generates updates such as:

trust
emotional state
prediction confidence
social relationships
unresolved goals
narrative tension
symbolic significance

These become the persistent state supplied to future prompts.

Example:

AgentState:
    trust_anna: 0.82
    curiosity: 0.64
    emotional_tone: wistful
    unresolved_goal:
        - recover violin
    relationship:
        Anna:
            trust: +0.12
    remembered_symbols:
        - silver key
        - violin

The transcript disappears.

Its consequences remain.

Sentiment as State Evolution

Traditional sentiment analysis produces labels:

Positive
Neutral
Negative

Selective Persistence instead uses affect as a transition function.

Each interaction computes changes to the coherence field.

Example outputs include:

Δ Trust
Δ Curiosity
Δ Fear
Δ Confidence
Δ Social Distance
Δ Goal Priority
Δ Narrative Tension
Δ Identity Coherence

The scene becomes a transformation rather than a record.

Persistence Filter

Each completed interaction passes through a persistence filter.

The filter determines:

What facts survive?
What emotions survive?
What relationships survive?
Which goals remain active?
Which symbols acquire lasting significance?
Which details are discarded?

Persistence is therefore selective rather than exhaustive.

Cultivar-Specific Memory

Each constitutional cultivar defines its own persistence profile.

Examples:

Witness

Retains:

observations
causal relationships
anomalies

Allows:

emotional intensity to decay rapidly

Steward

Retains:

obligations
promises
trust
responsibility

Discards:

incidental sensory details

Artist (future example)

Retains:

motifs
metaphors
moods
aesthetic resonance

Allows factual precision to soften while preserving expressive coherence.

Persistence Intensity

Each cultivar exposes a scalar persistence parameter.

Low values:

abstraction
exploration
novelty

High values:

consistency
recall
identity stability

The scalar does not determine whether memory exists.

It determines how much detail survives consolidation.

Layered Memory

Selective Persistence naturally produces three memory layers.

Working State

Always supplied to the next prompt.

Contains:

affect
trust
goals
current intentions
narrative tension

Episodic Traces

Sparse memories retained only when significant.

Examples:

first meeting
betrayal
promise
symbolic object
major discovery

Retrieved only when context resonates.

Constitutional Memory

Long-term identity.

Contains:

cultivar
values
behavioral tendencies
governance constraints
preferred narrative style

This layer changes rarely.

Prompt Construction

Instead of retrieving lengthy transcripts, the next prompt is assembled from:

current coherence field
working state
active goals
relevant episodic traces
constitutional memory

The prompt remains compact while preserving continuity.

Selective Persistence as Compilation

The architecture resembles compilation rather than storage.

Experience is transformed into state.

Conversation becomes field evolution.

Identity emerges from accumulated state transitions.

This reduces prompt growth while increasing behavioral consistency.

Relationship to MCCF

Selective Persistence aligns with the existing MCCF philosophy:

the world remains the execution environment;
coherence remains the governing principle;
constitutional cultivars remain the source of enduring identity.

Memory becomes another dynamic field rather than a passive archive.

Future Research

Once MCCF is operational, experiments should explore:

optimal persistence intensity for different cultivars;
interaction between selective persistence and narrative creativity;
effects on long-running agent identity;
coherence metrics as predictors of memory consolidation;
adaptive persistence based on uncertainty, surprise, and emotional significance.

A particularly interesting hypothesis is that creativity reaches its maximum near an intermediate persistence level, analogous to the information-theoretic phase transition identified in diffusion models.

Conclusion

Selective Persistence reframes episodic memory as a process of coherence-preserving compression rather than perfect recall.

Attention governs the present.

Selective Persistence governs the future.

By allowing each cultivar to regulate what persists—and at what fidelity—MCCF can preserve identity without imprisoning creativity. Memory becomes not an archive of events, but a living field of attractors that continuously shapes future behavior while remaining open to novelty.

Appendix A — Mathematical Models for Selective Persistence

Purpose

This appendix proposes a family of mathematical models for Selective Persistence within the Multi-Channel Coherence Field (MCCF).

These models are intended as research hypotheses rather than finalized algorithms. Their purpose is to provide a mathematically grounded framework for regulating episodic memory, balancing identity preservation with creative adaptability.

A.1 Definitions

Let

(x_t) = experience occurring at time (t)
(M_t) = episodic memory state
(S_t) = working agent state
(C_t) = coherence field
(K) = constitutional cultivar
(\alpha) = persistence intensity
(R(x_t)) = relevance of an experience
(P(x_t)) = persistence probability

The coherence field consists of four principal dimensions

[
C_t =
(E_t,,
B_t,,
P_t,,
S_t)
]

where

(E) = Emotional coherence
(B) = Behavioral coherence
(P) = Predictive coherence
(S) = Social coherence

Additional dimensions may be introduced in future versions.

A.2 Persistence as an Attention Operator

Attention selects information entering working memory.

Selective Persistence selects information entering episodic memory.

Define

[
A(x_t)
]

as the attention weight assigned to an experience.

Persistence is then

A(x_t)
\cdot
G(x_t)
]

where

(G(x_t)) is a governance function determined by the constitutional cultivar.

Thus,

attention determines what is noticed,

while governance determines what is remembered.

A.3 Persistence Intensity

Each cultivar exposes a scalar

[
\alpha
\in
[0,1]
]

where

0 = minimal retention
1 = maximal retention

Memory strength becomes

\alpha
\cdot
R(x_t)
]

(\alpha)

increases,

more detail survives.

(\alpha)

decreases,

memory becomes increasingly abstract.

A.4 Multi-Channel Relevance

Instead of assigning relevance manually,

derive it from coherence.

Let

w_EE
+
w_BB
+
w_PP
+
w_SS
]

where the weights are cultivar dependent.

Example

Witness

[
w_B>w_E
]

Steward

[
w_S>w_B
]

Artist

[
w_E>w_B

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