Ocdoo / TraceField

# Cognitive Landscape
An experimental cognitive layer for studying how experience changes a system
itself, rather than what it can look up. It is **not** a memory retrieval
system: nothing the user says is stored, and there is no index to search.
The question it tries to make testable: can a history of interaction leave
persistent internal structure that changes what can happen later?
## Core mechanism
Every structure holds three states:
| State | Role |
|---|---|
| `m` | fast state; reacts first to local mismatch, decays with real elapsed time |
| `theta` | slow structure; changes only by consolidating a fraction of `m` |
| `p` | plasticity; gates how much of `m` reaches `theta`, and is itself changed by instability |
One local update (`landscape/plasticity.py`) is: predict with `theta + m`,
measure mismatch against the input, move `m` toward the mismatch, consolidate
`p * rate * m` into `theta`, keep the rest in `m`, then raise `p` when `|m|`
stays large and lower it when things are quiet. There is no reward, no
importance score, no central judge, and no LLM deciding what matters.
## What this version does (v0.2)
Two perception channels feed the same equations:
1. **Embedding channel (primary).** Each turn of text is embedded locally
 (`BAAI/bge-small-zh-v1.5` through `fastembed`/ONNX Runtime, no torch) and
 routed by cosine similarity into unnamed vector structures, where `theta`
 is a region centre, `m` a fast offset and `p` a scalar. The v0.1 scalar
 equations are applied unchanged to vectors via a minimal `Vector` type
 (`landscape/vector.py`). An input far from every region starts a new one;
 thresholds and the measurements behind them are documented in
 `VectorLandscapeConfig` (`landscape/config.py`). The model loads on a
 background thread, so a host never blocks on it.
2. **Keyword channel (parallel, from v0.1).** Host-supplied `EvidenceSignal`s
 still update the original keyed scalar structures.
Readout happens only at compile time (`landscape/vector_compiler.py`): the
prompt is embedded, regions it would route into are selected, and each one is
described with the nearest phrases from a fixed library of ~100 topic phrases
and ~100 interaction-style phrases (`landscape/phrases.py`), three near
`theta`, two near `m`, plus a tension line when the current position would no
longer route back into its own centre. Phrases are never written back into
structures.
Ten consecutive turns with no signal on any channel write a zero-signal
warning, so silent perception failure is visible.
State lives in two files: the v0.1 keyed structures (schema unchanged) and a
separate vector file holding only numbers and timestamps.
This version does **not** include reaction learning, behaviour prediction, or
a shadow mode.
## Not verified
- **Effect on LLM behaviour is unverified.** Compiled context has not been
 shown to change what a model does; there is no evaluation of injection.
- **Vector structures mostly track topic content, not interaction style.**
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