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Correct INIT extraction and profile item fields

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gamer147
2026-07-22 16:44:53 -04:00
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docs/name-resolution.md
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@@ -102,16 +102,16 @@ free handholds — several of which we've already built:
now, statically, from methods 1–3. A *complete* map needs Frida for the tail. It's incremental:
label the ~dozen hottest globals first (biggest readability payoff), grow the rest on demand.
**Partial map — BUILT (v1, 2026-07-06).** `tools/global_map.py` → `build/global-var-map.json`
**Partial map — BUILT (v1, refreshed 2026-07-22).** `tools/global_map.py` → `build/global-var-map.json`
(all evidence) + `build/global-var-map.md` (labelled subset). It ingests `build/data/*.json`
(name/desc/field bases), scans the 481-script corpus for each global's **access shape**
(2D-table base + stride, 1D-array base, row-index, scalar), and ranks "current entity" index
pointers by purity. **First result: 16,354 of 49,435 distinct globals labelled** —
pointers by purity. **Current result: 15,918 of 49,386 distinct globals labelled** —
| kind | count | example |
|---|---|---|
| string tables (names/descs/messages) | 3,199 | `0x23a3` = skill-name table |
| per-entity data-field arrays (from *INIT) | 12,700 | dense = shared fields, `?` = sparse per-entity |
| string tables (names/descs/messages) | 3,206 | `0x23a2` = skill-name lookup base |
| per-entity data-field arrays (from *INIT) | 12,311 | dense = shared fields, `?` = sparse per-entity |
| row-major record tables (from access shape) | 122 | `0x52383` = record-table[stride 30] |
| 1D arrays | 307 | |
| index / "current entity" pointers | 26 | `0x152616` (purity 0.51), `0xeff75` (0.95) |
@@ -128,6 +128,36 @@ Labels are prefixed `=` to mark them as inferred aliases. Regenerate the `.asm`
Confidence is marked per entry; labels ending `?` are low-confidence guesses.
### INIT field-semantics workflow and ITINIT pilot (2026-07-22)
The old name-mode extractor's boundary rule was wrong for sparse tables: it treated any increasing
`global-string` destination as another description. ITINIT begins with 101 consecutive name-only records,
so the generated JSON collapsed them into item zero and fabricated 67 description columns. Static consumer
evidence also proves the tables are one-based: scripts look up item names from `0x1bd2 + item_id`, while the
first populated name is written to `0x1bd3`. `extract_init.py` now infers the parallel-array record span from
the dominant name-to-description delta (SKINIT 300; ITINIT/EBINIT 1000), recognizes column-zero names inside
that span, emits the one-based runtime id, and distinguishes the lookup base from the first written cell.
Corrected counts are **131 skills, 287 items, and 277 units**.
Semantic recovery is an evidence ladder, cheapest and strongest first:
1. Profile each write base across named records (population, value domain, common values and examples).
2. Mine every direct corpus consumer of that base and identify its role from the consuming operation/script.
3. Cross-resolve enums and foreign keys against other INIT/MES tables and visible descriptions.
4. Curate only supported names in `vm-map/globals.toml`; retain uncertainty in the profile rather than
promoting guesses. Use dynamic observation only for fields that remain ambiguous after static consumers.
`tools/init_table_profile.py ITINIT --build` materializes steps 1–2 in
`build/data/ITINIT-field-profile.{json,md}`. The first pass names eleven stable ITINIT arrays: catalog sort
key, random-item tier, item category, icon id, shared ITMES handler id, attack and defense elements, weapon
class, granted skill id, and minimum/maximum range. The strongest joins are independently human-readable:
attack/defense values index AFINIT's Japanese attribute strings, granted-skill values resolve to SKINIT, all
handler values resolve to ITMES.BIN, and every min/max-range record says `range 2` in its item description.
The remaining 764 ITINIT write bases are not automatically 764 item fields. Most occur once because ITINIT
also initializes linked effect/stat arrays while defining an item. Treat the profile's `population` and
consumer evidence as a classification aid before assigning table ownership.
### The curated registry — `vm-map/globals.toml` (2026-07-07)
The v1 auto map (`build/global-var-map.json`) infers *shapes* but cannot recover branch-flag
@@ -168,18 +198,22 @@ by SCJUMP/FIELD). **Known/named anchors:** `0x3234` `chapter_mode` (enum 1..9),
`0x6c9–0x6cd` UI toggles. Config/settings globals written by `CONFIG`/`INITCONFIG` (scene-reach 0)
are *not* story flags — the miner over-tags them; they are recategorized `unknown` when curated.
### Future step — growing the map (planned, not yet done)
### Future step — growing the map
The v1 map labels *shapes and tables*; the next increments add *meaning*, cheapest first:
1. **Fold in the `*MES` message-table writers** (`ITMES`, `SKMES`, `VIMES`, …) and any other
1. **Continue INIT semantics by evidence density.** Finish ITINIT's repeatedly populated fields and linked
effect arrays, then run the same profiler on SKINIT and EBINIT. Add explicit foreign-key joins (item →
skill, unit → attack/skill) once the target table ids are confirmed. Do not infer meaning from column
position alone.
2. **Fold in the `*MES` message-table writers** (`ITMES`, `SKMES`, `VIMES`, …) and any other
`set-string`/`copy-to-global` writers not covered by the `*INIT` set — pure static win,
extends the string/data labels. (Also: most name-table bases are *read* rarely — reads
likely go through `*MES`/an indirection; tracing that would connect names to their readers.)
2. **Label 2D record tables by their readers** — cross-reference which scripts read each
3. **Label 2D record tables by their readers** — cross-reference which scripts read each
`rec[sN]` table and infer purpose from context (e.g. RECOVER's 30-wide tables ↔ a
status/recovery system). Static, medium effort.
3. **Name *which stat* each field is (Frida).** The one step needing live tools: change a
4. **Name *which stat* each field is (Frida).** The one step needing live tools: change a
known value in-game (take damage, gain XP), watch which global moves → definitive
`field@X = "HP"`. Reserve for the fields that matter; this is the last mile.