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AI Deep Systems · first vertical slice

Tiny Transformer Evidence Lab

Predict first, edit a bounded fixture, run real deterministic numeric math, replay semantic tensor steps, compare logits and sampling, defend a trade-off, then try a transfer input.

Evidence boundary before you run

This is a tiny deterministic numeric teaching model. It is not a reproduction of a production LLM, and this page makes no network or real-model call.

d_model=3 · one head · d_k=d_v=2
ACTUAL CALCULATION

Matrix/vector values produced by this browser engine.

DETERMINISTIC SIMULATION

A fixed tiny fixture plus named uint32 PRNG; no learned model is loaded.

ILLUSTRATIVE

Replay highlighting only; the highlight is not another model execution.

REAL MODEL CALL

NOT USED on this page.

MODELED ESTIMATE

Displayed scalar slots × 4 bytes, assuming float32 payload only.

NOT OBSERVED

GPU memory, runtime memory, latency, and production-model behavior.

Determinism scope: identical validated config and seed replay the same trace in this engine. This is not a claim of bitwise reproducibility across model frameworks, releases, browsers, CPUs, or GPUs.

1 · predict before evidence

Lock a falsifiable receipt

Predict the output of ordered merges and the Q tensor shape. The receipt stays fixed while you edit parameters.

2 · bounded semantic edit

Prompt, tokenizer fixture, weights, and sampling

Only JSON data and scalar controls are parsed. No input is evaluated as JavaScript or executed on a server. Vocabulary size must remain compatible with the fixed embedding/output fixture.

1–32 Unicode characters; every final token must exist in the vocabulary fixture.

Tokenizer merge and vocabulary fixtures

This ordered character-merge fixture is inspectable BPE-inspired teaching machinery, not a GPT-2 tokenizer reproduction.

Q / K / V weights · each shape [3, 2]

Canonical for d_k=2: 1/√2 ≈ 0.7071

0 selects the separate greedy path.

Blank disables top-k.

(0, 1]; 1 disables top-p.

Unsigned 32-bit integer.

No request leaves the browser. Invalid shapes, ranges, tokens, or IDs fail closed before calculation.

Implementation counterexamples are locked.

Run the current prediction receipt and visit every semantic replay step before comparing the broken, correct, and over-engineered attention variants.

4 · defend, then transfer

Commit your trade-off before a new input

Why must a causal mask be applied before softmax? What changes when the score scale is removed or sampling filters become narrower? This field records an answer; it does not grade understanding.

Current-run gate: lock a prediction, run it, keep the draft unchanged, and visit every replay step (0/0).

Lock the defense first. Loading this input clears the prediction receipt, so you must predict again.

Primary sources and omitted architecture

This one-head forward pass intentionally omits multi-head concatenation, residual connections, layer normalization, MLP blocks, training/backprop, a production tokenizer, KV cache, batching, quantization, and hardware kernels. Those omissions prevent this page from standing in for a production Transformer.