methodology

Methodology

SMASHIN SCOPE Encoding

We developed a systematic framework for creating memorable mental images. SMASHIN SCOPE is an acronym encoding 12 memorability factors:

SMASHIN SCOPE Encoding Factors

Letter	Factor	Description
S	Substitute	Replace abstract with concrete
M	Movement	Add animation and action
A	Absurd	Make impossible or exaggerated
S	Sensory	Engage all 5 senses
H	Humor	Include funny elements
I	Interact	User participates in scene
N	Numbers	Encode quantities with shapes
S	Symbols	Use visual puns
C	Color	Add vivid, unusual colors
O	Oversize	Dramatic scale changes
P	Position	Precise spatial placement
E	Emotion	Evoke strong feelings

Multi-Channel Redundancy

Each memory is encoded through multiple channels, providing resilience to partial information loss:

Concept: 2PC
    ├── Visual: Stone statues
    ├── Sensory: Cold granite
    ├── Emotional: Frozen forever
    ├── Contrast: Saga divorce
    └── Scale: 47 couples
            │
            ▼
        [Recall]

Hierarchical Index Design

We structure memories in a three-level hierarchy to minimize retrieval context:

Level 0 (Root): Domain mapping (~400 chars)

keyword → domain → anchor

Level 1 (Domain): Location pointers (~300 chars each)

anchor → file:line → verify_token

Level 2 (Memory): Full SMASHIN SCOPE image (~500 chars)

Total navigational overhead: 2.5KB vs 46.5KB for flat structure (94.6% reduction).

Verification Token System

To prevent LLM hallucination, each memory includes a unique verification token—a phrase that:

Only exists in the actual stored memory
Appears unrelated to the concept (hard to guess)
Must be present in any valid response

Example Verification Tokens

Concept	Verify Token	Rationale
CAP Theorem	two heads breathe	Dragon metaphor specific
Two-Phase Commit	47 couples	Absurd scale
Write-Behind Cache	50-foot grandmother	Emotional anchor
Consistent Hashing	gnomes on clock	Unique visual

Retrieval Confidence Scoring

Each retrieved memory receives a confidence score based on multiple signals:

\[\text{score}(m, q) = \alpha \cdot \text{sim}(m, q) + \beta \cdot \text{verify}(m) + \gamma \cdot \text{smashin}(m)\]

where:

$\text{sim}(m, q)$ is the semantic similarity between memory $m$ and query $q$
$\text{verify}(m)$ is 1 if verification token is present, 0 otherwise
$\text{smashin}(m)$ is the normalized SMASHIN SCOPE factor count (0-1)

The weights $\alpha=0.5$, $\beta=0.3$, $\gamma=0.2$ are tuned on a held-out validation set.

Red Queen Protocol

“It takes all the running you can do, to keep in the same place.” — The Red Queen, Through the Looking-Glass [1]

Named after Lewis Carroll’s famous quote, the Red Queen Protocol represents the insight that constant adversarial testing is required just to maintain knowledge quality—without it, memories decay and hallucinations creep in.

Two-Phase Architecture:

Pre-Learning Phase: Before deployment, run configurable adversarial rounds (0-5) to proactively identify and strengthen weak memories
Runtime Phase: Four specialized agents continuously challenge memories during operation

Agent	Model	Role
Examiner	Haiku	Generate challenging retrieval queries targeting weak spots
Learner	Haiku	Attempt retrieval using only index anchors (blind recall)
Evaluator	Haiku	Score retrieval accuracy, identify gaps and misconceptions
Evolver	Opus	Re-encode weak memories with stronger SMASHIN SCOPE images

Pre-Learning Mechanism:

During pre-learning, memories are tested against harder thresholds (base probability 0.5 vs 0.7 for normal retrieval). Weak memories that fail are immediately boosted by the Evolver agent before deployment, reducing downstream retrieval failures.

The protocol ensures memories remain robust and verification tokens effective throughout the system lifecycle.

[1]

Carroll, L. 1871. Through the looking-glass, and what alice found there. Macmillan.

## Methodology {#sec-methodology} ### SMASHIN SCOPE Encoding We developed a systematic framework for creating memorable mental images. SMASHIN SCOPE is an acronym encoding 12 memorability factors: | Letter | Factor | Description | |--------|--------|-------------| | S | Substitute | Replace abstract with concrete | | M | Movement | Add animation and action | | A | Absurd | Make impossible or exaggerated | | S | Sensory | Engage all 5 senses | | H | Humor | Include funny elements | | I | Interact | User participates in scene | | N | Numbers | Encode quantities with shapes | | S | Symbols | Use visual puns | | C | Color | Add vivid, unusual colors | | O | Oversize | Dramatic scale changes | | P | Position | Precise spatial placement | | E | Emotion | Evoke strong feelings | : SMASHIN SCOPE Encoding Factors {#tbl-smashin-scope} ### Multi-Channel Redundancy Each memory is encoded through multiple channels, providing resilience to partial information loss: ``` Concept: 2PC ├── Visual: Stone statues ├── Sensory: Cold granite ├── Emotional: Frozen forever ├── Contrast: Saga divorce └── Scale: 47 couples │ ▼ [Recall] ``` ### Hierarchical Index Design We structure memories in a three-level hierarchy to minimize retrieval context: **Level 0 (Root)**: Domain mapping (~400 chars) ``` keyword → domain → anchor ``` **Level 1 (Domain)**: Location pointers (~300 chars each) ``` anchor → file:line → verify_token ``` **Level 2 (Memory)**: Full SMASHIN SCOPE image (~500 chars) Total navigational overhead: **2.5KB** vs 46.5KB for flat structure (94.6% reduction). ### Verification Token System To prevent LLM hallucination, each memory includes a unique verification token—a phrase that: 1. Only exists in the actual stored memory 2. Appears unrelated to the concept (hard to guess) 3. Must be present in any valid response | Concept | Verify Token | Rationale | |---------|--------------|-----------| | CAP Theorem | two heads breathe | Dragon metaphor specific | | Two-Phase Commit | 47 couples | Absurd scale | | Write-Behind Cache | 50-foot grandmother | Emotional anchor | | Consistent Hashing | gnomes on clock | Unique visual | : Example Verification Tokens {#tbl-verify-tokens} ### Retrieval Confidence Scoring Each retrieved memory receives a confidence score based on multiple signals: $$\text{score}(m, q) = \alpha \cdot \text{sim}(m, q) + \beta \cdot \text{verify}(m) + \gamma \cdot \text{smashin}(m)$$ where: - $\text{sim}(m, q)$ is the semantic similarity between memory $m$ and query $q$ - $\text{verify}(m)$ is 1 if verification token is present, 0 otherwise - $\text{smashin}(m)$ is the normalized SMASHIN SCOPE factor count (0-1) The weights $\alpha=0.5$, $\beta=0.3$, $\gamma=0.2$ are tuned on a held-out validation set. ### Red Queen Protocol > "It takes all the running you can do, to keep in the same place." > — The Red Queen, *Through the Looking-Glass* [@carroll1871looking] Named after Lewis Carroll's famous quote, the Red Queen Protocol represents the insight that constant adversarial testing is required just to maintain knowledge quality—without it, memories decay and hallucinations creep in. **Two-Phase Architecture:** 1. **Pre-Learning Phase**: Before deployment, run configurable adversarial rounds (0-5) to proactively identify and strengthen weak memories 2. **Runtime Phase**: Four specialized agents continuously challenge memories during operation | Agent | Model | Role | |-------|-------|------| | Examiner | Haiku | Generate challenging retrieval queries targeting weak spots | | Learner | Haiku | Attempt retrieval using only index anchors (blind recall) | | Evaluator | Haiku | Score retrieval accuracy, identify gaps and misconceptions | | Evolver | Opus | Re-encode weak memories with stronger SMASHIN SCOPE images | **Pre-Learning Mechanism:** During pre-learning, memories are tested against harder thresholds (base probability 0.5 vs 0.7 for normal retrieval). Weak memories that fail are immediately boosted by the Evolver agent before deployment, reducing downstream retrieval failures. The protocol ensures memories remain robust and verification tokens effective throughout the system lifecycle.