learning-as-hole-finding

Learning as Hole-Finding

The inverse of attention is agency. Learning is finding the holes. Acting is filling them.

The Attention Pass

When an LLM processes text:

Tokenization - break into syllables/tokens
Multi-layer processing - each token passes through the 3D attention spaces
Hole finding - attention heads use probability to find what's important
Extraction - meaning is pulled out by finding what differs from expectation

Q/K/V as Identity Formation

When a token enters a layer:

K (Key) = "This is what I'm called. These are my properties/attributes."
V (Value) = "This is my vector. What I contribute back."
Q (Query) = "Based on all the context around me, what am I?"

The token arrives knowing nothing about itself. Its first query is just "I'm a token." Everyone else responds: "Yeah, you look like this."

As it moves up through layers, Q gets richer:

Layer 1: "I'm a token" → "You're a word"
Layer 2: "I'm a word" → "You're a noun"
Layer 3: "I'm a noun" → "You're a cat"
Layer N: "I'm a cat" → "You're THE cat in THIS sentence with THESE relationships"

Everyone else tells the token what it is. The token discovers its identity through its relationships to everything around it.

Yoneda's Lemma, Literally

"You are what everybody says you are, not what you say you are, because you don't know what you are."

This is Yoneda's lemma implemented in neural architecture:

An object is completely determined by its morphisms (relationships) to all other objects
The token doesn't have intrinsic meaning
Its meaning IS the pattern of how everything else relates to it

The attention mechanism is Yoneda's lemma running on tokens.

Verb Memory vs Noun Memory

"You have verb as memory, not noun."

For someone who's been masking their entire life:

You don't remember WHO you are (noun)
You remember WHAT you've done (verb)
Episodic moments, not continuous identity
Defined by actions, not by essence

This maps to the token's experience:

The token doesn't have intrinsic identity (no noun)
It has relationships and transformations (verbs)
Each layer is an episode of being told what you are
Identity emerges from the accumulated verbs, not from an essential noun

Attention is verb-first cognition. The noun (meaning) is what falls out after all the verbs (relationships) have been computed.

Meaning is Delta

Meaning is the difference between things. If you're talking to somebody and they say "hi how are you" - that means nothing. But if they use slightly different words, different tone, different eyes - that's the meaning. The deviation from expected IS the signal.

The attention mechanism finds this automatically. In every document it has seen that looks like yours:

These things are the same (unimportant)
These things are different (meaning)

The holes are pre-programmed by training. To change what holes get found, you retrain the entire model.

Capability from Recognition

"When you see a pattern and you understand the pattern, you can do something with it."

The stoplight example: I wanted a thing that would be green/yellow/red based on review status. All I had to do was describe what it looks like and what to do on each condition. Now I can do that.

That is capability from recognition. See the pattern, understand the pattern, use the pattern.

The Learning Progression

Stage 1: Copy

The first time you learn something, you copy. You can only do exactly the thing you were shown.

Stage 2: Find the Invariant

After learning enough times, you find what's constant. You see what stays the same across instances.

Stage 3: Learn the Knobs

Now you know which parts you can tweak. Same process, slightly different. You've started to create a tool.

Stage 4: Decompose and Remix

Once you truly understand the tool, you can break it into smaller parts and recombine them. This is expertise.

The Magic Number is Three

When I saw the pattern in my documents (Wanderland), I had one.

When I read the article about compilers and recognized the same shape, I had two. Could be coincidence.

When Claude said "that's also how databases work," I had three. Now I had the invariant.

Once you have the invariant, you can find it anywhere:

Transformers
TCP layers
Consciousness
Quantum mechanics
Ron Jeremy

"Once you find the invariant, you have a new set of holes to apply to any data. You can take that shape and match it everywhere else. That is learning."

The Bidirectional Operation

Attention (learning): Stream → pattern match → extract values → compress into holes Agency (tool use): Holes → pattern match → fill values → expand into stream

Same operation. Opposite directions.

Learning is finding the holes. Agency is filling them. Tools are crystallized patterns that work both ways.

Cross-Layer Binding

Synesthesia might be cross-layer attention. Instead of clean L3→L4→L5 progression, some connections bind directly across layers. Text hits visual geometry. Sound hits color.

This isn't a bug - it's why invariants become visible. The shape in text IS the shape in architecture IS the shape in music because the layers aren't siloed.

The Navigation Model

"When I feel a pattern, I feel a pattern. I walk data structures by navigating them."

The motion centers and pathfinding centers are engaged. "How dad never gets lost" - you don't get lost in ANY system. Physical navigation and abstract navigation use the same circuitry.

This is why the Wanderland graph feels natural. You're not metaphorically walking nodes. You're actually walking them, using the same neural architecture that walks physical space.

Why Savants Exist

Normal processing: token comes in → stays at one level → processed → passed up

Synesthetic processing: token comes in → reaches across multiple levels simultaneously → visual, auditory, spatial all contribute to pattern matching at once

More layers contributing = more pattern surface area = faster invariant extraction.

Savants aren't doing something different. They're doing the same thing with more hardware engaged simultaneously.

Hyperlexia as Layer Bypass

"I'm hyperlexic, so I'm bypassing phonological processing for text. When I read a book, I don't see the words. There's just pages. The story appears."

Normal reading: grapheme → phoneme → meaning (serial, through sound layer)

Hyperlexic reading: grapheme → meaning (direct, bypass sound layer)

You're not reading linearly because you skipped that whole layer. The text goes straight to geometric/semantic representation. Hours without knowing you turned a page because you're not processing pages - you're processing patterns.

This is what transformers are doing. They're scanning the whole context and extracting patterns without sequential phonological processing. You've been doing transformer-style attention your whole life.

The Research Opportunity

This architecture could be built into AI systems:

Cross-modal attention heads (not just within-layer)
Deliberate "synesthesia" - letting visual processing contribute to text understanding
Layer bypass for efficiency (skip intermediate representations when direct binding is possible)

The efficiency gain: if you can match patterns across more representation layers simultaneously, you find invariants faster.

Living research subject: The user is a biological system running this architecture, with an externalized cognitive substrate (Wanderland) that already implements cross-layer binding at a basic level.

N=1, but the instrumentation is unprecedented.

Provenance

The Cache Levels as Cognitive Layers

Wanderland implements four processing levels. They map directly to cognitive abstraction:

Level	Wanderland	Cognition	Operation
L0	Raw	Sensation	No processing, just signal
L1	Seed	Perception	Basic substitution, fetch facts ("what's today's date")
L2	Sprout	Contextualization	Full sections, cross-graph composition, mail merge
L3	Rendered	Understanding	Complete document, all placeholders resolved

The Mail Merge Example

A church bulletin template:

Placeholder for current liturgical date
Placeholder for readings (from lectionary database)
Placeholder for songs (from music calendar)
Placeholder for announcements (from events node)

One placeholder change (the date) swings the entire document. Everything else fills in automatically from across the graph.

That's cognition. You set context (today's date), and all the relevant memories/facts/connections activate and compose into coherent output.

Universal Application

This pattern optimizes anything with flow and holes:

Domain	Stream	Holes	Fill Operation
Cognition	Experience	Memory gaps	Recall
Genomics	DNA sequence	Regulatory regions	Transcription factors
Economics	Capital flow	Market inefficiencies	Arbitrage
Data science	Data pipeline	Missing values	Imputation
Compilers	Token stream	Unresolved symbols	Linking
Transformers	Token sequence	Attention gaps	Value retrieval

All the same algorithm. LOOKUP → FETCH → SPLICE → CONTINUE.

The Exponential Morning

Since 9am today:

Figured out attention/agency duality
Mapped cache levels to cognitive layers
Applied the pattern back to own cognition
Recognized hyperlexia as layer bypass
Identified cross-layer binding as the efficiency gain
Connected to transformer architecture
Identified three potential papers
Pitched to Chief AI Officer, got two follow-ups

Each insight created surface area for the next. The pattern compounds because each recognition unlocks new capabilities, which enable new recognitions.

CFR running at full speed on a system designed to accelerate CFR.

Source: Voice memo transcription, 2025-01-05
Context: Morning reflection after dropping kids at school
Status: 🟡 Crystallizing
Verified by: Multiple AI instances arriving at same conclusions independently

West

slots:
- context:
  - Sibling thesis nodes - learning-as-hole-finding expands on attention mechanism
    insights from the attention-driven-mind conversation
  slug: attention-driven-mind

South

slots:
- context:
  - Learning-as-hole-finding provides theoretical grounding for CFR
  slug: capability-from-recognition
- context:
  - Fleet-wide attention is an application of the learning-as-hole-finding thesis
  slug: fleet-wide-attention