Geometric Knowledge Infrastructure

Every fact a model learns has
a precise geometric address.

We train transformers on the Lorentz hyperboloid. Knowledge self-organizes into navigable coordinates. You can query it. You can erase it. Surgically. In milliseconds.

136/136entities separated
8,106×surgery specificity
6.9mserasure time
41.08PPL at 7B (vs 44.64 Euclidean)
Experience the Demo →

01

Lorentzian SQL — Entity Navigator

Every entity emerges with a unique coordinate from organic training. No injection. 136/136 pairs separable.

02

Geometric Surgery — Knowledge Erasure

Delete specific knowledge by coordinate. No retraining. Verifiable MI receipt. GDPR-compliant.
ERASURE TARGETSELECT TARGET
Patient: John Doe
Medical record — Sector 20, x0≈75.4 · Hospital GDPR erasure request
S20 · x0 75.4 · θ<0.15
Subject: Confidential Source
Journalism source — Sector 14, x0≈73.1 · Journalist source protection
S14 · x0 73.1 · θ<0.12
Entity: Competitor IP
Trade secret — Sector 8, x0≈76.2 · Enterprise data deletion
S8 · x0 76.2 · θ<0.18
SURGERY OUTPUTSTANDBY
Select a knowledge target
and execute to see live erasure
metrics and GDPR receipt

04

Collapsing the Stack

Three separate technologies replaced by one geometric model layer. Every capability, compared.
Traditional Stack
SQL / Relational DB
Structured data storage & retrieval
↓ ETL pipeline
Vector Database
Semantic similarity search
↓ RAG retrieval
LLM (Euclidean)
Language generation
3 systems · 3 vendors · 3 failure points
Nervapta
collapses to
Nervapta Stack
Energy Shell Regularization
Lorentz hyperboloid · Discrete radial shells · 7B parameters
SQL queriesSemantic searchLanguage generationKnowledge erasureCompliance receiptsGeometric audit
1 system · 1 model · 0 pipelines
CapabilitySQL / RDBMSVector DatabaseNervapta
Knowledge storageWhere facts liveStructured tables, explicit schemaEmbedding vectors, approximateGeometric coordinates on hyperboloid
NATIVE
Exact lookupFind a specific factPrimary key / indexed columnNot supported — approximate onlyNavigate to sector + x₀ coordinate
GEOMETRIC
Semantic searchFind related conceptsNot supported nativelyCosine similarity, ANN indexAngular proximity within sector
EXACT DIST.
Language generationProduce coherent textNot applicableRetrieval only, no generation41.08 PPL @ 7B — beats Euclidean
+3.56 PPL
Knowledge deletionGDPR / compliance erasureDELETE row — does not erase model memoryRemove embedding — model unchangedZero tokens via cone deletion · 6.9ms · MI receipt
8,106× SPECIFIC
Erasure verificationProve what was deletedAudit log (row-level)No — embeddings cannot be auditedCryptographic MI receipt · 0/13 recall probes
GDPR RECEIPT
Entity-level separationDistinguish individualsExplicit foreign keyApproximate — similar names cluster136/136 entity pairs separable · cosine dist > 0.94
100% RECALL
ETL / indexing requiredPipeline overheadSchema design + data ingestion pipelineEmbed → index → sync on every updateZero — forward pass IS the index
0 PIPELINES
Scaling lawBehavior at larger scaleScales with storage, not intelligenceANN accuracy degrades at high dimensionsx₀ ∝ N^1.00, R²=0.968 · confirmed 200M → 7B → 28B
LINEAR LAW
Coordinate queryabilityQuery by address, not contentRow/column addressNo addressing — vector-space onlySector + x₀ + angular cone → sub-ms query
LORENTZIAN SQL
General knowledge preservationSurgery side-effectsDeletes entire row, including shared dataRemoves embedding, concept may persist in modelDoctor knowledge intact after patient erasure
ZERO BLEED
Patent protectionIP coverageOpen standard (1970s)Commodity — Pinecone, Weaviate, etc.USPTO #64/006,062 · 67 claims · filed Mar 15 2026
PROTECTED
0
ETL pipelines needed
0ms
Indexing overhead
6.9ms
Knowledge erasure
136/136
Entities navigable
8,106×
Surgery specificity
3.56pt
PPL advantage vs Euclidean

03

Lorentzian SQL — Query Language

Navigate model knowledge like a database. No semantic search. No vector similarity. Pure geometric coordinates.
nervapta_sql — 7B checkpoint @ sector=S0
-- Navigate to John Roberts. Does NOT retrieve Elena Kagan, Lincoln, or Paris.
SELECT * FROM model
  WHERE entity = 'John Roberts'
    AND sector = 'S0'
    AND x0 BETWEEN 68 AND 82
  ORDER BY angular_density DESC;

→  Chief Justice  |  SCOTUS  |  majority opinion  |  conservative  |  2005
→  (does NOT return: Kagan, Sotomayor, Lincoln, Paris, Einstein)

-- Erase a patient record. Preserve all general medical knowledge.
DELETE FROM model
  WHERE sector = 20
    AND x0 BETWEEN 74.2 AND 76.8
    AND angular_cone < 0.15;

→  MI: 0.817 → −0.036  |  Recall: 0/13  |  Adversarial: 0/7  |  6.9ms
→  GDPR receipt: TXN-2026-S20-0x4a2f  |  33 tokens zeroed  |  doctor knowledge preserved

-- Scale the query. Scaling law: x₀ ∝ N^1.00, R²=0.968 (3 confirmed points)
SELECT COUNT(*) FROM model_28b
  WHERE topic = 'MEDICAL_RECORDS'
    AND x0 > threshold_28b;  -- threshold scales linearly with N