bf08d4a58f
Domain classification at 75%/0.17s (~5K sentences/sec) is the sweet spot. Added 1B pre-sort pipeline, calibration checks, cross-domain anomaly detection, and expanded reference distribution builder tasks. Co-Authored-By: Virgil <virgil@lethean.io>
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FINDINGS.md — go-i18n Research & Discovery
Record findings, gaps, and architectural decisions here as work progresses.
2026-02-19: Initial Assessment (Virgil)
Current State
- 5,800 lines across 32 files (14 test files)
- All tests pass
- Only dependency:
golang.org/x/text - Grammar engine is solid: forward composition + reversal + imprint + multiplier
Architecture
go-i18n is a grammar engine, not a translation file manager. Consumers bring their own translations. The library provides:
- Forward composition — Grammar primitives that compose grammatically correct text
- Reverse grammar — Tokeniser reads grammar tables backwards to extract structure
- GrammarImprint — Lossy feature vector projection (content to grammar fingerprint)
- Multiplier — Deterministic training data augmentation (no LLM calls)
Key Gaps Identified
| Gap | Impact | Notes |
|---|---|---|
| No CLAUDE.md | High | Agents don't know the rules, will flatten locale files |
| Dual-class word ambiguity | Medium | "file" as verb vs noun, "run" as verb vs noun |
| No benchmarks | Medium | No perf baselines for hot-path usage (TIM, Poindexter) |
| No reference distributions | High | Can't calibrate imprints without scored seed data |
| Only English grammar tables | Medium | Reversal only works with loaded GrammarData |
Sacred Rules
gram.*keys in locale JSON MUST remain nested — flattening breaks the grammar engine- Irregular forms in grammar tables override regular morphological rules
- Round-trip property must hold: forward(base) then reverse must recover base
- go-i18n does NOT ship or manage consumer translation files
2026-02-19: LEK-Gemma3-1B-v2 Benchmark (Virgil)
Tested the fine-tuned 1B model (/Volumes/Data/lem/LEM-Gemma3-1B-layered-v2) across three progressively tighter evaluation rounds to find where it provides real value for the grammar pipeline.
Round 1: Practical Dev Tasks (5 tasks, temp=0.3, max_tokens=512)
Open-ended dev work — bug spotting, commit messages, Go functions, grammar tables, code review. Results: mostly misses. The model hallucinates APIs, generates pad-token degeneration on longer output, and can't reliably write code. Not useful for generative tasks.
Round 2: Narrow Constrained Tasks (24 tasks, temp=0.1, avg 0.19s/task)
Tighter format — one-word answers, forced categories, fill-in-the-blank.
| Category | Score | Notes |
|---|---|---|
| Domain classification | 3/4 (75%) | Promising — called technical, creative, ethical, casual correctly |
| Article selection | 2/3 (67%) | Got "an API" and "an SSH" right, missed "a URL" |
| Tense detection | 2/4 (50%) | Weak on gerund vs base form |
| Plural detection | 2/3 (67%) | Got "matrices" and "datum", confused on "sheep" |
| Conjugation | Mixed | Some correct, many hallucinated forms |
Round 3: Tightest Format (27 tasks, temp=0.05, avg 0.18s/task)
Binary T/F, forced A/B choice, single-word domain/tone classification.
| Category | Score | Notes |
|---|---|---|
| Domain classification | 6/8 (75%) | Consistent with Round 2 — this is the sweet spot |
| Article correctness T/F | 3/3 (100%) | Perfect on "an SSH", "a API"→false, "an URL"→false |
| Tone/sentiment | 2/3 (67%) | Got positive + negative, neutral confused |
| Irregular base forms A/B | 2/2 (100%) | "went"→go, "mice"→mouse — strong |
| True/False grammar | 4/8 (50%) | Strong false-bias — says "false" when unsure |
| Pattern fill | 0/4 (0%) | Echoes prompt or hallucinates — dead zone |
Key Finding: Domain Classification at Scale
Domain classification at 75% accuracy in 0.17s is genuinely useful. At that speed, one M3 can pre-sort ~5,000 sentences/second across {technical, creative, ethical, casual}. For the 88K Phase 0 seed corpus, that's ~18 seconds to pre-tag everything.
The technical↔creative confusion (calls some technical text "creative") is the main error pattern — likely fixable with targeted fine-tuning examples showing code/CLI commands vs literary prose.
Implications for Phase 2
- Pre-sort pipeline: Run 1B domain classification before heavier GrammarImprint analysis. Pre-tagged seeds reduce imprint compute by letting us batch by domain.
- Calibration target: 1B classifications can be spot-checked against Gemma3-27B classifications to measure drift.
- Article/irregular strength: The 100% article correctness and irregular base form accuracy suggest these grammar features are well-learned. Worth testing as lightweight validators in the forward composition path.
- Dead zones to avoid: Don't use 1B for pattern fill, tense detection, or generative tasks. These need the full 27B or rule-based approaches.