Cuneiform

Package Install

Installable package: python3.11 -m pip install cuneiform-control. Current release: 0.1.0 on PyPI. Source: Zer0pa/Cuneiform.

python3.11 -m pip install cuneiform-control

For full install, smoke, source, and developer commands, click here.

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00 · GNOSIS-CUNEIFORM · COMPUTATIONAL MORPHOLOGY RESEARCH-READY · P5 NO-GO Five thousand years of writing, searchable by its shape. Cuneiform morphology, kept honest · Gnosis-Cuneiform · PyPI cuneiform-control v0.1.0 · github.com/Zer0pa/Cuneiform Cuneiform is one of the oldest writing systems on earth — five thousand years of pressed marks in clay. Gnosis-Cuneiform measures the geometry of those signs so archives, classrooms, and museums can look across collections by shape. The first attempt at a governing classifier scored 0.021916 against a 0.6 target, and the score stays on the record. This page is shape infrastructure, not a reading claim, and the image-bearing corpora stay outside the public pack.

Scope: cuneiform shape infrastructure. Classifier score 0.021916 missed the 0.6 target; this is search geometry, not a reading claim.

01 · THE GAP SUCCESS-ONLY RECORD Experts catalogue cuneiform sign by sign. Cross-collection search still begins from human memory.

02 · MARKETS ADJACENT FORECASTS Cultural heritage digitization '30 · $8.1B Research data management '30 · $6.7B Scholarly infrastructure '30 · $5.3B Digital humanities '30 · $3.2B AI for archaeology '30 · $1.4B source: adjacent research-infrastructure and heritage categories. Sign-shape search is one underbuilt workflow inside them, not a commercial reading service.

03 · VALUE OF MARKET $8.1 B Heritage digitization '30; cross-collection sign-shape search is one underbuilt workflow inside that spend.

04 · INSIGHT A cuneiform sign's geometry is a measurable signal.

05.1 · CURRENT TECH MEMORY-BOUND CATALOGUES Digitised tablets become images and catalogue entries. A scholar asking which signs look like this wedge pattern still walks between collections, emails curators, and stitches the answer together from human recall and PDF appendices.

05.2 · OUR TECH PUBLIC CONTROL PACK The cuneiform-control package on PyPI carries the morphology boundary in public: shape metrics, manifest checks, source policy, and the below-target classifier score recorded plainly at 0.021916. Anyone can install it, replay the manifest, and inspect the result without releasing image-bearing corpora or claiming text recovery.

05.3 · BENCHMARKS PHASE-2 RESULT STATUS P5 1NN check0.021916NO-GO Manifest5/5PASS Schema0errors PyPIv0.1.0 P5 0.021916 · NO-GO P6 diagnostic 0.038176 Manifest 5/5 PASS Verdict: Governing classifier below target · manifest passes against the SHA-pinned upstream tablet artefact.

06 · MEASUREMENT PHASE-2 RESULT LEDGER One classifier score against one chosen target.

06.1 · RESULT LEDGER · PHASE-2 STATUS P5 · governing 1NN 0.021916 · NO-GO P6 · diagnostic 0.038176 P7 0.051826 Manifest 5/5 PASS Phase 2 governing score 0.021916 against a 0.6 target. P6 and P7 diagnostics report alongside but do not repair the result. Manifest passes 5/5 against the SHA-pinned 9.28 MB upstream tablet artefact.

07 · KEY METRICS PACK STATUS

07.1 · GOVERNING 1NN CHECK 0.021916 Governing classifier · below 0.6 target, kept public

07.2 · MANIFEST INVARIANTS 5/5 Manifest invariants pass · stdlib-only smoke runner

07.3 · P6 DIAGNOSTIC 0.038176 Diagnostic score only · does not repair the governing result

07.4 · PYPI CONTROL PACK v0.1.0 cuneiform-control on PyPI · Apache-2.0, live 2026-05-04

07.5 · MANIFEST SHA-256 e4d85a…3daa24 9.28 MB upstream artefact · SHA-pinned, verified

08 · DETERMINISM REPLAYABLE PACKET The public packet preserves the same measured boundary.

08.1 · WHAT REPLAYS EXACTLY SHA-PINNED MANIFEST Across two fresh installs, the Phase 2 score (0.021916) hashes identically. The pack validates 5 cross-field invariants with 0 schema errors against the SHA-pinned 9.28 MB artefact, stdlib-only on any Python 3.8+ host. This is not scientific proof of text recovery: the smoke does not re-run the governing 1NN. It proves the public control pack still matches the recorded morphology boundary, byte for byte, so the failed result cannot quietly drift over time.

08.2 · HONEST BLOCKER Honest Blocker · Manifest validation only: the smoke proves shape, not science. It does not repair the classifier result, recover cuneiform text, or release image-bearing corpora. Raw bytes remain private; Traditional-Knowledge protocols, museum image rights, and public-review limits apply. RELEASING.md and .gpd/STATE.md release-state drift pending.

09 ANCIENT SIGNS WITH A SEARCHABLE SHAPE.

09.1 · THIS LAB'S AMBITION The ambition is applied infrastructure for the cuneiform world: measure the geometry of a sign once, then let it travel into catalogue lookup, cross-collection comparison, classroom teaching, and museum metadata — without ever claiming text recovery or releasing image-bearing tablets the field has agreed to protect.

09.2 · WHAT THIS IS Shape-search infrastructure is public, the manifest passes, and the failed classifier score stays on the record.

09.3 · WHAT IT IS NOT The governing classifier missed its 0.6 target. Image-bearing corpora and release-state drift stay outside the pack.

09.4 · ARCHIVES · NEAR-TERM (12–24 MO) Tablet archives gain shape lookup A researcher chasing a wedge pattern across the British Museum, the Louvre, and CDLI no longer relies on memory and email. Sign geometry becomes a queryable field across catalogues, and the answer arrives before the trip is booked.

09.5 · TEACHING · NEAR-TERM (12–24 MO) Cuneiform classrooms see sign families A graduate seminar can group signs by visible form before any language claim enters the room. Students see how wedges relate to wedges, building intuition for variation across scribes, periods, and regions instead of memorising tables.

09.6 · CATALOGUING · MID-TERM (24–48 MO) Museums describe signs by geometry Curators add measured shape descriptors to tablet records alongside provenance and period. Discovery improves for the next generation of scholarship, and the metadata stays honest about what was photographed versus what was read.

09.7 · METHOD · MID-TERM (24–48 MO) Heritage AI keeps its discipline A loud public no-go score makes premature decipherment claims harder to publish unchallenged. Funders, reviewers, and journalists gain a reference for what restraint looks like when an early model misses the threshold its own authors chose.

09.8 · METHOD · PARADIGM (48 MO+) Failed science becomes shared memory Heritage scholarship gains a habit of preserving negative results with the same care as positive ones. A century from now, the next attempt on cuneiform morphology starts from a known floor, not from a forgotten draft, and the field learns faster because of it.

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Install / Developer Commands Detailed

Package Install

Installable package: python3.11 -m pip install cuneiform-control. Current release: 0.1.0 on PyPI. Source: Zer0pa/Cuneiform.

python3.11 -m pip install cuneiform-control

Import smoke:

python3.11 - <<'PY'
import importlib.metadata as md
import cuneiform_control

print("cuneiform-control", md.version("cuneiform-control"))
PY

CLI smoke:

cuneiform-smoke --help

Install success only proves package acquisition/import. Product scope, stale PyPI state, platform limits, and blockers remain in the front-door sections below.

• Use the hyphenated PyPI name for install; the Python module is cuneiform_control.

Quick Start

Verified end-to-end on 2026-04-25. Commands run from a fresh clone.

### 1. Install the smoke surface (zero third-party runtime deps; pytest pulled in for tests)
python3 -m venv /tmp/cuneiform-control
source /tmp/cuneiform-control/bin/activate
python -m pip install --upgrade pip
python -m pip install -e . pytest

### 2. Hermetic self-test against bundled fixtures (3 cases, < 1 second)
pytest -q