A consumer measured an 8x difference in solve time between two fits over
the same events, the same slices and the same ~2,000 nodes:
career fit (gamma = 0) 787 ms
drifting fit (gamma = 0.15) 6214 ms
Entirely the collapse rule. A competitor with zero drift contributes one
variable however long the history, so a drift-free fit's joint is smaller
than a drifting one's by roughly the slice count — and to factorise, by
its cube. Choosing a drift configuration is therefore also choosing a
query cost, and nothing said so.
Documented on `Joint`, on `Joint::variables` and on `posterior_of`, with
the measurement. `variables()` is named as the number that decides
affordability, since it can be read before committing to a batch.
Also states the thing the consumer proposed as a future optimisation,
because it is already true: an absence is not an appearance, so a
competitor seen in the first and last of a hundred slices contributes two
variables rather than a hundred. The matrix is already as small as the
model allows on that axis.
tests/joint_handle.rs pins the mechanism — ten slices, two competitors,
twenty variables drifting against two at `gamma = 0` — so a change to the
collapse rule cannot quietly remove the property the docs now promise.
Refs #51
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
logaritmiskandClaude Opus 5
f692906ce4