Level 11.23 — Massive KG Scale + CLI Query Dispatch

Golden Chain Cycle: Level 11.23 Date: 2026-02-16 Status: COMPLETE — 354/354 (100%)

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Key Metrics

Test	Description	Result	Status
Test 121	Heap-Allocated Massive KG (120 entities, 12 relations)	144/144 (100%)	PASS
Test 122	CLI-Style Query Dispatch (5 query types)	40/40 (100%)	PASS
Test 123	Massive Batch Integration (100 entities, 10 relations)	170/170 (100%)	PASS
Total	Level 11.23	354/354 (100%)	PASS
Full Regression	All 395 tests	391 pass, 4 skip, 0 fail	PASS

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What This Means

For Users

• Trinity VSA now supports 120+ entity knowledge graphs — the previous stack limit of ~40 entities has been eliminated through heap allocation
• CLI-style query dispatch proves the system can handle the diversity of queries a command-line user would make: direct lookups, multi-hop chains, cross-domain queries, and multi-relation queries
• 100-entity batch processing with 170 queries across 10 relations completes with 100% accuracy and full deterministic consistency

For Operators

• Heap allocation is the key scaling technique: std.testing.allocator provides leak-tracked heap storage for entity vectors (~70KB each)
• At 120 entities: ~8.4MB heap (well within any server's capacity)
• At 100 entities: ~7MB heap with 10 relations, 2x5 split memories
• The theoretical limit is now memory-bound, not stack-bound — thousands of entities are feasible
• All 12 relations use 4x3 split memories (4 sub-memories, 3 pairs each) for maximum signal separation

For Investors

• Level 11.23 breaks through the scalability barrier — from 40 entities (stack-limited) to 120+ entities (heap-allocated)
• 100% accuracy across 354 queries demonstrates the architecture scales without degradation
• CLI-style query dispatch validates the system as a local testing tool — users can interact with the knowledge graph via command-line patterns
• 23 development cycles from basic ternary operations to a massive-scale, heap-allocated, CLI-queryable symbolic reasoning engine

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Technical Details

Test 121: Heap-Allocated Massive KG (144/144)

Architecture: 120 entities across 10 categories — Scientists(12), Universities(12), Cities(12), Countries(12), Fields(12), Instruments(12), Theories(12), Elements(12), Labs(12), Continents(12). Twelve relation types, all using 4x3 split memories.

Heap allocation pattern:

const entities = try allocator.alloc(Hypervector, 120);
defer allocator.free(entities);
// ~8.4MB heap (120 × 70KB per Hypervector)

Ten query tasks:

Task	Query Pattern	Hops	Result
1	scientist→university	1	12/12
2	scientist→field	1	12/12
3	scientist→instrument	1	12/12
4	scientist→university→city	2	12/12
5	scientist→university→city→country	3	12/12
6	scientist→(theory + element) divergent	1×2	24/24
7	scientist→univ→lab + lab→element cross-chain	2+1	24/24
8	field→instrument cross-domain	1	12/12
9	scientist→lab direct	1	12/12
10	country→continent	1	12/12

Key result: 3-hop chains across 120 candidates maintain 100% accuracy. Heap allocation eliminates the stack overflow that prevented scaling beyond ~40 entities.

Test 122: CLI-Style Query Dispatch (40/40)

Architecture: 30 entities across 6 categories — Cities(5), Countries(5), Landmarks(5), Foods(5), Languages(5), Climates(5). Five relation types with 2-way split memories.

Five CLI query types:

Query Type	CLI Pattern	Count	Result
direct	"What country is city X in?"	5	5/5
chain2	"What country is landmark X in?"	5	5/5
chain3	"What food for landmark X?"	5	5/5
cross_domain	"Language + climate for country X?"	10	10/10
multi_rel	"All relations for country X?"	15	15/15

Key result: The query dispatch system routes each query type to the appropriate relation memories and chain logic. This proves the VSA engine can serve as the backend for a CLI query tool.

Test 123: Massive Batch Integration (170/170)

Architecture: 100 entities across 10 categories — People(10), Companies(10), Cities(10), Countries(10), Products(10), Skills(10), Universities(10), Departments(10), Projects(10), Tools(10). Ten relation types with 2x5 split memories.

Five batch types:

Batch	Type	Queries	Result
1	Direct 1-hop all 10 relations	100	100/100
2	2-hop person→company→city	10	10/10
3	3-hop person→company→city→country	10	10/10
4	Cross-relation person→(company+skill+project)	30	30/30
5	Deterministic consistency (repeat 20 queries)	20	20/20

Key result: 100 direct queries across all 10 relations against 100 candidates — every single one correct. Deterministic consistency verified: identical queries always produce identical results.

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Scaling Analysis

Metric	Level 11.21 (Stack)	Level 11.23 (Heap)	Improvement
Max entities	~40	120+	3x (unlimited potential)
Memory model	Stack (~320KB)	Heap (~8.4MB)	Scalable
Relations	6	12	2x
Query accuracy	100%	100%	Maintained
3-hop chains	100% at 40	100% at 120	Scales linearly
Candidate pool	40	120	3x with zero degradation

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.vibee Specifications

Three specifications created and compiled:

1. specs/tri/heap_massive_kg.vibee — 120 entities, 12 relations, heap allocation
2. specs/tri/cli_query_dispatch.vibee — 30 entities, 5 CLI query types
3. specs/tri/massive_batch_integration.vibee — 100 entities, 10 relations, batch processing

All compiled via vibeec → generated/*.zig

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Cumulative Level 11 Progress

Level	Tests	Description	Result
11.1-11.15	73-105	Foundation through Massive Weighted	PASS
11.17	—	Neuro-Symbolic Bench	PASS
11.18	106-108	Full Planning SOTA	PASS
11.19	109-111	Real-World Demo	PASS
11.20	112-114	Full Engine Fusion	PASS
11.21	115-117	Deployment Prototype	PASS
11.22	118-120	User Testing	PASS
11.23	121-123	Massive KG + CLI	PASS

Total: 395 tests, 391 pass, 4 skip, 0 fail

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Critical Assessment

Strengths

1. Stack barrier broken — heap allocation enables 120+ entities, previously impossible
2. 100% accuracy maintained at 3x scale — no degradation from 40 to 120 entities
3. CLI query dispatch proven — 5 query types routed programmatically
4. 170-query batch at 100 entities — massive-scale batch processing works
5. Full deterministic consistency — 20/20 repeat queries identical

Weaknesses

1. No actual CLI binary — query dispatch is simulated within tests, not a standalone executable
2. All relations are 1:1 — each scientist maps to exactly one university, one field, etc. No many-to-many
3. Heap allocation per-test — entities are allocated and freed per test, not persisted across queries
4. No dynamic KG updates — all memories are static, built at initialization

Tech Tree Options for Next Iteration

Option	Description	Difficulty
A. Persistent KG Store	Save/load heap-allocated KG to disk for cross-session persistence	Hard
B. Many-to-Many Relations	Support entities with multiple values per relation (professor teaches 3 courses)	Medium
C. Interactive CLI Binary	Build actual zig build query command that reads from stored KG and accepts stdin queries	Medium

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Conclusion

Level 11.23 breaks through the scalability barrier with heap allocation, enabling 120-entity knowledge graphs with 12 relations and 100% accuracy across 354 queries. The CLI query dispatch validates the system as a practical local testing tool with 5 query types. Massive batch integration at 100 entities confirms the architecture scales without degradation.

The transition from stack to heap is the key infrastructure achievement — the theoretical entity limit is now determined by available RAM, not stack size. At ~70KB per entity, a machine with 1GB free RAM could support ~14,000 entities.

Trinity Scaled. Heap Lives. Quarks: Massive.