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d22e889 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # Metric reference
LIMEN v0.1 treats the layer axis as an ordered sequence. For each token, the
hidden state is an array \(h_\ell\), where \(\ell\) is the layer index.
This is a **layer-wise geometric path**. It must not be confused with a
continuous-time physical trajectory or with the token-to-token generation
trajectory.
## Descriptive trajectory metrics
| Metric | Definition | Useful for | Does not establish |
|---|---|---|---|
| Path length | \(\sum_\ell \lVert h_{\ell+1}-h_\ell\rVert_2\) | Total movement through layers | Reasoning effort |
| Displacement | \(\lVert h_L-h_0\rVert_2\) | Net first-to-last change | Semantic progress |
| Tortuosity | Path length / displacement | Route indirectness | Confusion or intelligence |
| Mean speed | Mean adjacent-layer distance | Typical layer-wise movement | Runtime latency |
| Speed CV | Standard deviation / mean speed | Movement variability | Instability in a control-theory sense |
| Mean acceleration | Mean norm of the second difference | Changes in movement | Physical acceleration |
| Turning angle | Mean angle between consecutive differences | Directional change | A cognitive transition |
Tortuosity is undefined when displacement is zero and is reported as `NaN` in
the per-token arrays. Summary statistics omit non-finite values.
## Probability baselines
Entropy is reported in natural units (`nats`). The top-1/top-2 margin is the
difference between the two largest softmax probabilities.
These baselines are included because many apparent geometric effects can be
explained by ordinary changes in model confidence. A trajectory metric should
not be presented as incrementally useful until it is compared against them on
held-out data.
## Required controls for stronger claims
- exact model and tokenizer revisions;
- fixed extraction location and layer indexing;
- response length and token-position controls;
- prompt-family controls;
- repeated seeds when sampling is enabled;
- shuffled layer and token-order baselines;
- held-out evaluation;
- cross-checks on more than one architecture.
No v0.1 metric is a validated universal invariant, functional locator or
causal control signal.
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