OBSERVATION CHAMBER / MVP
AI Word Flow
Watch how a simplified AI word model passes signals across layers, then change a clue or weight and see the next-word candidates reorder.
Horizontal flow field
Only the input layer is visible.
Click an arrived clue to enable or disable it. Select a line to inspect its weight.
Sentence in progress
cycle 0Choose a candidate
0%Choosing here updates both the canvas and the primary action.
Detailed controlsweights, reasons, and output
Secondary actions
- Current layer
- Input layer
- Input nodes
- 0
- Arrived lines
- 0
- Top candidate
- Shown at candidate layer
- Softmax temperature
- 0.65
- What the sentence needs now
- needs_clause
Selected
-Select an arrived node or line.
Outside this model's vocabulary
Candidate competition
Before / After / Delta
| Candidate | Before | After | Delta |
|---|
How input was recognized
REPORT
Record the current stateAbout this model
Values stay hidden until signals reach that layer.
Changing a clue or line recomputes only downstream layers.
This limited-vocabulary model uses no external AI or API and does not reproduce real GPT or LLM internals.
SYSTEM NOTE
Each layer uses a weighted sum h_j = phi(sum_i(w_ij x_i)). Candidate score z_i combines semantic, transition, sentence-shape, END, and repetition-inhibition contributions. Candidate share is stable softmax: exp((z_i - max(z)) / T) / sum_k exp((z_k - max(z)) / T), with T = 0.65. An intervention changes a clue or sentence-shape weight and recomputes only downstream layers.
OBSERVATION GUIDE
Touchpoints for Observation
Observe next-word signals arrive, candidate shares settle, and rankings move when a cause is changed.
- Open the first cycle one layer at a time, choose a candidate, continue automatically, then compare the rain-clue and sentence-shape interventions.
- Candidate shares are relative values inside this model.
- Rain OFF weakens one clue path; the place-word experiment changes a sentence-shape contribution.
- Restoring an intervention returns to the same small-model comparison state.
This lab does not reproduce the internals of GPT or another LLM, and it is not a general-purpose text generator. It uses a limited vocabulary and hand-designed weights as a small observation model. Softmax, weights, and sentence-shape rules are simplified for observation, so each intervention result is specific to this model.
Runs inside the browser with no upload and no registration.
OBSERVATION POLICY
This lab runs in your browser. No image, pointer trace, or input is uploaded by this prototype.
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Route
This chamber belongs to Signals & Networks.
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