# Gemma Naming Methodology (institutional knowledge)

**Status:** the implementing code (`gemma_naming.py`, `naming_core.py`, and the
`test_batch_naming.py` / `qa_naming.py` harnesses) was **retired in #951 (D-223)**
when authored markers became a names-only flavoured pool and the per-feature
geometry generator was retired. This document preserves *how* the Reach's place
names were generated so the approach can be rebuilt for the names-only format if
new bodies ever need fresh names. The names it produced are now the frozen pool
in each `markers.json` (`names.{rivers,mountain_ranges,oceans,cities,pois,...}`).

The pipeline named every empty `name` field across ~2,400 bodies' markers.json
files (cities, rivers, oceans, mountain ranges, gate terminals, landmarks) with
culturally-coherent, corridor-appropriate names — using a small local LLM, not a
hand-written name table.

---

## 1. Model & runtime

- **Model:** Gemma 2 2B (`gemma2.gguf`), later `gemma-4.gguf` — a *small* local
  GGUF model, chosen so the whole Reach could be named offline on commodity
  hardware (CPU fallback, ROCm/CUDA when available).
- **Serving:** `sr-voice serve --stdio` — a long-lived subprocess fed prompts over
  stdin, replies over stdout. The same binary backs the in-game voice pipeline.
- **KV-cache bleed is the enemy.** A long-lived model accumulates context across
  requests and starts echoing earlier completions (every river becomes "Aldren").
  The subprocess was **restarted every `--refresh` requests** to flush the cache.
  This single knob mattered more than any prompt tweak for output diversity.

## 2. Two-stage flow

1. **Register selection** (`select_register`) — for each star system, Gemma is
   shown the corridor's candidate cultural sub-styles (numbered) plus a compact
   cultural excerpt from the system's wiki + GTTR text, and asked to **pick the
   number** of the best-fitting register. This grounds naming in the *authored*
   cultural identity rather than a blind hash. Falls back to `palette_for`
   (hash-based pick) if the model fails or there's no cultural text.
2. **Name generation** (`_build_prompt`) — generate names in the selected
   register, per feature, with retries and dedup.

## 3. Corridor palettes — the cultural-design crux

Names are biased by **geographic sector** (the real corridor column in
`systems.db` is `star_systems.geographic_sector`). Each sector has a list of
**sub-style inflections**, each with a register description and ~5 example stems.
A sub-style is picked per system (via `hash(system_id)`) so neighbouring systems
rotate registers and the model's narrow ~15-stem vocabulary stays fresh across
hundreds of bodies.

> **Inflection is a dominant bias, not a hard lock.** A British surveyor on an
> east_reach moon still names a river after their aunt in Dorset. Each sub-style
> explicitly names its register *and invites diaspora variety.* This is what keeps
> the Reach feeling like blended-reality settlement rather than themed zones.

| Sector | Sub-style registers |
|--------|---------------------|
| `core` | English countryside · British colonial · American frontier · American municipal · Classical/civic · ANZ settler |
| `north_reach` | English rural/parish · Scottish Highland/Lowland · Australian outback · Irish coastal · South African English |
| `south_reach` | Portuguese colonial/Iberian · Brazilian interior · East African Swahili · Cape Verdean/West African · Angolan/Mozambican |
| `east_reach` | Korean · Japanese rural/coastal · Taiwanese/Hakka · Filipino · Mixed East Asian diaspora |
| `west_reach` | German compound · Dutch low-country · Nordic/Scandinavian · Polish/Czech · Baltic/Finnish |
| `deep_frontier` | Founder-surname · Surveyor-descriptive · Functional/military outpost |

Legacy aliases mapped to `core` (`sol-gateway-axis`, `inner_corridor`,
`inner_orbit`) and `deep_frontier` (`frontier`). Cross-cultural names in every
direction are expected and correct (see the corridor cultural-mixing principle).

## 4. Few-shot prompting (the model-fit lesson)

> Gemma 2 2B is **far better at pattern completion than instruction following.**

So prompts were *worked examples*, not instructions:
- Show **2 examples from *different* corridors than the target** (teach the
  *pattern* — "system description → register number" or "register → place name" —
  without biasing toward the target's vocabulary), then present the target and let
  the model complete.
- Register selection used a fixed preamble with two `System: … → Best: N` examples,
  then the target system, then `Best (number only):`.
- Per-feature generation rotated through example **pools** picked deterministically
  by `hash(body_id, local_id, attempt)` so neighbouring features on one body don't
  all draw the same prompt and collapse to identical outputs.

## 5. Context extraction (fitting 1024 tokens)

Gemma 2 2B has a ~1024-token context, so the authored cultural signal had to be
compressed hard:
- **GTTR hook** — the 30–45 word "Drifter's Guide" characterisation of the system
  is the single biggest lever for names that feel like *this* world. Title lines
  and section headers were stripped; the first substantive paragraph was used.
- **Wiki cultural lines** — `_extract_cultural_lines` scanned for cultural-identity
  keywords (heritage, founding, settler, surname, language, diaspora, and explicit
  culture names) and kept the strongest hits, falling back to opening prose.
- Budget math reserved tokens for preamble + tail + output; context was truncated
  to fit.

## 6. Post-processing, retries, dedup, fallback

- **Deterministic seeds:** `seed = sha256("{role}|{id}|{attempt}").hexdigest()[:8]`
  — reproducible, and bumping `attempt` rotates the completion on retry.
- **Earth-major blocklist:** `earth_blocklist.txt` rejected real Earth majors
  (Paris, Tokyo, …). Earth-*echo* names are fine; Earth *majors* are not.
- **Retry:** on collision or blocklist hit, retry with a bumped seed, up to 3
  attempts.
- **Deterministic fallback:** persistent failure fell back to a palette-driven
  stem+suffix name (`fallback_name`, `_FALLBACK_STEMS`/`_FALLBACK_SUFFIXES`) so the
  pipeline always produced *something* valid.
- **Dedup scope:** within `(geographic_sector, feature_type)` — no two bodies in
  the same sector ship the same river name; **cross-corridor collisions are
  allowed** (two "Aldren"s on opposite arcs is fine). Processing ran **core-first**
  (`SECTOR_PRIORITY`) so core bodies won the dedup race and outer sectors took the
  fallback path on collision.

## 7. Per-feature prompt config

Each feature type had its own subject framing, length hint, and example pools
(`_PROMPT_CONFIG`): rivers/oceans/seas/lakes/mountain ranges → "1–3 words";
capital → "1–2 words" (+ planet context); secondary towns → "1–2 words";
gate terminals → "2–3 words ending in 'Gate Terminal'/'Transit'/'Exchange'/
'Concourse'"; institutional & cultural landmarks → "2–4 words". Capitals and
secondary towns included planet context so town names sat under the world's name.

## 8. If you rebuild this for names-only markers

The data model changes: there are no longer per-feature records with empty `name`
fields to fill — the target is the **flat name pool** per body
(`names.cities`, `names.rivers`, …). A rebuild would:
1. Decide pool sizes per body (how many city/river/etc. names to mint).
2. Keep stages 1–6 verbatim — register selection, corridor palettes, few-shot
   completion, seed/dedup/blocklist/fallback are all format-independent.
3. Write strings into `names.<category>` lists instead of into feature records,
   and drop the atlas-DB sync entirely (the server cascade attaches pooled names
   to computed features at placement — D-223, #955).