Release v0.3.0 · Robertg761/Slide

[0.3.0] - 2026-08-10

Gesture typing now uses the offline FUTO Swipe neural spatial model on supported 64-bit Android
devices, followed by Slide's own trie-constrained CTC beam search. The existing deterministic
decoder remains available automatically if neural inference cannot start.
The suggestion strip shows coalesced partial candidates while a swipe is still in progress,
without allowing preview inference to queue behind newer finger positions.
Typed correction can recover bounded two-edit mistakes through a trie-pruned weighted sequence
search. The fast single-edit path remains first, so ordinary key slips keep their existing
latency.
A trigram language model adds the word before the immediately preceding word to tap, swipe, and
next-word ranking. On 16,139 held-out typo cases it bought 194 additional correct corrections
for 14 additional wrong ones over bigram context alone.
Slide learns a private per-key spatial calibration from confirmed typing and correction choices.
It is stored under Android's no-backup directory and is removed by Clear learned data.

Swipe traces now retain Android's historical samples and event timing, are resampled at a stable
cadence, and are normalised against the active keyboard layout before neural inference.
Choosing a different swipe candidate repairs the learned phrase evidence for the rejected and
selected words. Undoing an autocorrection likewise repairs phrase evidence and learns the
confirmed touch alignment rather than the rejected correction.
Release and CI builds reproducibly prepare a pinned ExecuTorch Android runtime, fetch immutable
swipe-model revisions, verify their SHA-256 digests, and package the model licence and visible
FUTO Swipe attribution. Release verification now checks the swipe models, trigram, native
runtime ABIs, compression methods, and exact hashes in the final APK.

A corrupt or interrupted copied swipe-model file is atomically replaced instead of being reused
on the next keyboard start.
Neural runtime failures disable repeated retries for that keyboard process and fall back cleanly;
shutdown now waits for any active decode before releasing native modules.
Live swipe previews are cancelled when the gesture, field, or keyboard session becomes stale, so
a late partial result cannot replace the final candidates.

Neural swipe inference is packaged for arm64-v8a and x86_64. The deterministic decoder is used on
32-bit Android ABIs.
The neural instrumentation test and release APK were built successfully, but this release's
neural accuracy and latency have not yet been measured on a physical Android device.