Datasets:

dappalumbo91
/

pflt-fsot-sample

One goal: On every metric that defines a leading translator intelligence, climb until we are first (or clearly better than the prior best on that metric). The competitor name does not matter — only the bar does.

This is not “win BLEU only.” PFLT is a universal translator intelligence under FSOT law. Metrics are multi-track.

Product identity (does not change)

Map fluid language into meaning, ground claims in FSOT seed law (S=K(T_1+T_2+T_3)), densify knowledge without rewriting law, converse and relay — morph/lexicon is the surface; FSOT is the constitution.

Metric board — beat the bar on each track

#	Track	What “winning” means	External bar (order of magnitude)	PFLT current (approx.)	Next climb target
M1	Core open-set morph (held-out form→gloss partial)	Honest morph stress (train_mass only)	No public GT	Ada ~87% open-set partial (fast climb); Latin ~94%	Hold ≥85%; push ≥90% overall
M1b	PRODUCT form→gloss (full gold+densify)	Shipping inventory accuracy	Dict/MT train on full data	Ada ~99.5% partial across 20 langs	Hold ≥99%; never regress
M2	Exact map rate	Exact gloss hits	—	Product ~99.5% exact; open-set ~85% exact	Hold product; open-set exact ≥85%
M3	Name / entity track	Places & proper names	Gazetteer-heavy systems	Name deploy historically ~79%	Hold ≥80%, push ≥90% with Pleiades + densify
M4	Language catalog	Distinct language/script surfaces productively handled	Google Translate ~249 langs; NLLB 200; DeepL ~30–100	20 gold codes · ~1.02M quality rows · deploy A_strong ~15–16 langs	Grow toward 100 → 200 → 249+ meaningful surfaces
M5	Classical / dead / visual	la, grc, ang, akk, sum, hieroglyphs, etc.	Weak or absent in consumer MT	Deploy sample ~84% partial when known; Latin open-set ~50%	Own this band: best offline classical+visual translator
M6	Modern sentence quality	Tatoeba BLEU-style bars (src→en); path to FLORES/COMET	WMT leaders: frontier LLMs; EU pairs often DeepL-strong	Climb live: BLEU~~2.4 · U-F1~~34 · B1 es~~53 · cov~~90% (bigram+phrase+templates, offline, held-out train)	Larger phrase mass · order models · CJK BPE · optional neural student under FSOT law
M7	FSOT law pin	Authority hash + scalar panel every act	N/A (unique)	D1D38A pin, `authority_ok`	Keep 100% law-backed turns
M8	Knowledge + converse	Multi-turn relay, ledger growth, archive cite, teach panel	Consumer MT has little/no persistent grounded ledger	Converse + ledger + teach + densify + cert math	Product metrics: multi-turn coherence, non-contradiction with law
M9	Offline / local	No paid API required to climb or translate	Cloud MT depends on vendors	Full local chew + archive	Stay offline-first
M10	Certified numeric claims	No vibes math as law	LLMs invent numbers	`certified_math` gate	Expand Lean bridge when ready

Immediate campaign (now) — Ada-primary

Ada open-set climb → M1 ≥ 70% partial on held-out eval_sample.tsv
(pflt_main.exe eval with train_mass.tsv only).
Fuel: export_data_for_ada.py → densify + gold_core + train_mass + paradigm expand
(sources: expanded_gold ~1.3M, classical lexica, hieroglyphs, Dictionary mine).
Ada self-climb: pflt_main.exe inject FORM GLOSS grows densify offline.
Do not claim open-set if eval forms sit in train_keys.
After 70%: freeze report → M2 exact, M5 classical/visual, M4 catalog, then M6 modern.
Python is data factory only — not the product binary.

Commands:

cd pflt-Ada
python export_data_for_ada.py
alr build
.\bin\pflt_main.exe eval
.\bin\pflt_main.exe converse "aqua lingua manus"

Rules of the climb

Honest splits — no silent test leakage into train for claimed open-set numbers (supervised densify is labeled; oracle is debug only).
Law never rewritten by dialogue or densify.
Multi-metric — winning M1 does not skip M7–M9.
Competitor is the bar, not the brand: if a new system sets a higher bar on any Mi, that becomes the new target.

One-line north star

Climb every translator-intelligence metric until we hold the bar — breadth, classical/visual depth, open-set accuracy, grounded knowledge, and FSOT law — offline-first, no excuses.