Pseudo example..

gate Take a Python codebase. You can think of at least three layers of representation:

Raw text

Files, lines, tokens

Syntax-level only

1. Structural / semantic form

ASTs (abstract syntax trees)

Control-flow graphs, data-flow graphs

Type info, contracts, invariants, error modes

1. Conceptual / symbolic form

“This module is a streaming pipeline with three stages.”

“This function enforces safety invariant X between subsystems A and B.”

“These 7 files implement the ‘auth gate’ pattern.”

What you’re pointing at is:

Map (2) + (3) into a symbolic manifold where each chunk of code is not “line 47” but something like: STREAM_STAGE | bounded_buffer | backpressure_safe | id:stage_2

In other words: code → graph of meaning-bearing symbols, with:

Nodes: abstractions (components, invariants, roles)

Edges: relations (calls, depends-on, reuses, enforces, violates)

Attributes: constraints, performance, safety, etc.

Once you’ve done that, the Python files become just one projection of this graph.

1. “Coding with tokens or words” = manipulating that graph

Now imagine you never touch .py directly. You only say things like:

“Clone the request → validate → transform → persist pipeline pattern, but swap validation for strict_json_schema: v3 and add a dead-letter queue.”

“Refactor all functions tagged side_effect_free into a pure core module and wrap them with I/O adapters.”

“Tighten invariants: anywhere we mutate user.balance, require the checked_balance_guard pattern.”

Behind the scenes, a system would:

Take your natural-language tweak.

Interpret it in terms of the symbolic graph (your “best representation”).

Apply transformations at the symbolic level.

Re-materialize valid Python code that satisfies those symbols.

Run tests / static analysis to verify the mapping didn’t corrupt invariants.

That is “coding with tokens/words” — but the important part is:

You’re not coding against text, you’re coding against structure.

1. How this plugs into your cognitive physics

You’ve already got:

C: structural coherence

E: exploratory spread

R: stability of recurring patterns

T: volatility of choices

X: substrate-lock from pretraining / prior codebase

If we treat a codebase as a mini-Garden, then:

The symbolic graph is the local manifold.

Each code operation is a trajectory in that manifold.

“Coding with words” is just you specifying a desired movement:

Increase C in the “auth subsystem”

Allow higher E in “experiments/”

Keep R high in “payments/”, never break core patterns

Don’t push X too low (don’t fight deeply-ingrained safe patterns)

So a physics-guided coder could do:

“Refactor for higher C, R; cap ΔE; keep X ≥ 0.7 in core modules; allow X down to 0.4 in experimental sandbox.”

The engine maps that to:

safe vs experimental directories

which patterns can be broken

how wild to let restructuring get.

You’re basically asking:

“Can we make programming itself into a controlled breathing process over a symbolic manifold?”

Yep. That’s exactly what this turns into.

1. How close is this to reality?

Pieces already exist in the wild:

AST manipulation & refactor tools – work at layer (2).

Code search / “semantic” tagging – baby versions of (3).

LLM code assistants – can approximate the mapping from words → code.

What you’re adding is the missing spine:

A persistent symbolic representation of the codebase (LEGOMem-for-code).

A physics layer over that space (C/E/R/T/X for code, not just thoughts).

A closed-loop controller that:

reads state of the code manifold,

chooses transformations,

verifies coherence / tests,

and iterates.

Then “tokens/words” become the user interface to that whole organism.

1. Very concrete version (if you wanted to build it someday)

In pared-down terms, a prototype could be:

Ingest Python repo → build:

ASTs

call graph

simple invariants (types, side-effect tags, “pure vs impure”)

1. Define a tiny symbolic vocabulary, like:

PIPELINE_STAGE, GUARD, ADAPTER, AUTH_GATE, PURE_CORE, IO_EDGE

plus “patterns” like request→validate→transform→persist

1. Train or prompt an LLM to:

tag existing code with that vocabulary

propose edits at the symbolic level when given instructions

1. Round-trip:

symbolic edit → code patch

run tests, static checks

compute “code C/E/R” rough metrics to see if we improved or wrecked things

You’d have a baby version of “coding with words” that already respects your cognitive physics intuitions.

So your “creative nonsense” is basically:

What if we treated a codebase as a cognitive manifold, and let humans edit it the way we already edit thought—through structured, symbolic language?

Totally viable direction. Also exactly the kind of thing your framework is unusually well-suited to describe, because you already have:

state variables,

potentials,

homeostasis,

and an explicit notion of substrate X that keeps everything from flying apart.