edcf02b350
Selection (access-core; SPEC §5.4, proposed): a prior over the options on the board — usage frequency now, the Laya decision model later — times the person's own switch-timing likelihood, after Nomon (Broderick & MacKay): - press_timing.rs learns a damped kernel-density estimate of each press's offset from the intended highlight, from a population prior scaled to the person's scan interval, and only from selections the person keeps: presses are staged per attempt, dropped when abandoned or undone (including an Undo reached by scanning), and learned only after three newer selections. - selection.rs computes the posterior (late presses explained as late, a 2% lapse term), selects at 99:1 odds with the prior capped so presses must favour the choice 4:1, and otherwise keeps scanning — a row gets up to two extra passes. Without opt-in, or before 10 kept presses, it behaves exactly like the conventional scanner and only measures. - scan_order.rs orders rows, groups and cells by prior mass (fewest expected steps) without moving any; options keep stable position-order ids, and a new order applies from the next pass. dwell.rs weights per-cell dwell from 0.8x to 1.5x within hard bounds. Every adaptation is opt-in and previewable. Conformance C8–C11 (order never moves a position, opt-in required, dwell bounds, selection follows presses): 370/370 with the original 236. Prediction (aac-core): per-language packs — word frequencies, word pairs, sentence starts and a letter model, front-coded and CRC-checked — built reproducibly by tools/lang/build_packs.py from pinned wordfreq 3.2.0 (CC-BY-SA-4.0), FrequencyWords 2018 (CC-BY-SA-4.0) and Common Voice sentences (CC0), into data/lang/ (43 of 45 languages; gu and kn have no open source large enough and report "adaptive only"). PredictionEngine blends a pack with what the person has said (hierarchical Dirichlet smoothing): next word, completion, and letter prediction for letter scanning, deterministically. A per-language pair filter exists; its lists still need curating before the app shows next-word suggestions to children. Co-Authored-By: Claude Opus 5.5 <[email protected]>
53 KiB
53 KiB