Level 11.24 — Interactive CLI Binary

Golden Chain Cycle: Level 11.24 Date: 2026-02-16 Status: COMPLETE — 145/145 (100%)

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Key Metrics

Test	Description	Result	Status
Test 124	Named Entity Registry (30 entities, 5 relations)	60/60 (100%)	PASS
Test 125	Multi-Hop CLI Pipeline (2-hop, 3-hop, cross-domain)	30/30 (100%)	PASS
Test 126	CLI Binary Integration Verification	55/55 (100%)	PASS
Total	Level 11.24	145/145 (100%)	PASS
Full Regression	All 398 tests	394 pass, 4 skip, 0 fail	PASS
CLI Binary	zig build query	Compiles and runs	PASS

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What This Means

For Users

• Trinity now has an actual CLI binary — run zig build query -- Paris capital_of and get France instantly
• Multi-hop chain queries work from the command line: zig build query -- --chain Eiffel landmark_in capital_of outputs Eiffel → Paris → France
• Entity and relation discovery: --list shows all 30 entities, --relations shows all 5 relations
• The knowledge graph builds in milliseconds and queries resolve instantly — no external dependencies

For Operators

• The CLI binary is self-contained — no model files, no network, no configuration. Just compile and run
• Same deterministic seeds as the test suite — the CLI produces identical results to the verified tests
• Binary located at zig-out/bin/trinity-query after zig build query
• Error handling for unknown entities and relations with helpful suggestions

For Investors

• Level 11.24 delivers the first user-facing product from the symbolic reasoning engine
• The CLI proves Trinity VSA can power interactive applications, not just pass tests
• 24 development cycles: from basic ternary operations to a complete interactive symbolic AI tool
• This is the foundation for future web APIs, chat interfaces, and integration with LLMs

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CLI Binary Usage

Direct Queries

$ zig build query -- Paris capital_of
Query: capital_of(Paris) = France
Similarity: 0.2778

$ zig build query -- Eiffel landmark_in
Query: landmark_in(Eiffel) = Paris
Similarity: 0.2660

$ zig build query -- Sushi cuisine_of
Query: cuisine_of(Sushi) = Japan
Similarity: 0.2778

Multi-Hop Chains

$ zig build query -- --chain Eiffel landmark_in capital_of
Chain query: Eiffel --[landmark_in]--> Paris (sim=0.266) --[capital_of]--> France (sim=0.278)

$ zig build query -- --chain Colosseum landmark_in capital_of
Chain query: Colosseum --[landmark_in]--> Rome (sim=0.266) --[capital_of]--> Italy (sim=0.278)

Discovery

$ zig build query -- --list
Entities (30):
  Cities: Paris, Tokyo, Rome, London, Cairo
  Countries: France, Japan, Italy, UK, Egypt
  Landmarks: Eiffel, Fuji, Colosseum, BigBen, Pyramids
  Foods: Croissant, Sushi, Pizza, FishChips, Falafel
  Languages: French, Japanese, Italian, English, Arabic
  Climates: Temperate, Humid, Mediterranean, Oceanic, Arid

$ zig build query -- --relations
Relations (5):
  capital_of: city → country
  landmark_in: landmark → city
  cuisine_of: food → country
  language_of: language → country
  climate_of: climate → country

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Technical Details

Test 124: Named Entity Registry (60/60)

Architecture: String-to-index mapping for 30 entities and 5 relations. Case-sensitive exact match with prefix fallback.

Three sub-tests:

Sub-test	Description	Result
Entity registry	30 name→index lookups	30/30 (100%)
Relation registry	5 name→index lookups	5/5 (100%)
Named query dispatch	25 entity+relation→result queries	25/25 (100%)

Key result: All 25 query scenarios resolve correctly when dispatched by string name — the named registry introduces zero accuracy loss compared to index-based queries.

Test 125: Multi-Hop CLI Pipeline (30/30)

Architecture: Full pipeline from string entity name through multi-hop chain to string result name.

Four sub-tests:

Sub-test	Query Pattern	Count	Result
2-hop chains	landmark→city→country	5	5/5 (100%)
3-hop chains	landmark→city→country→cuisine	5	5/5 (100%)
Cross-domain	country→(language+climate)	10	10/10 (100%)
Deterministic	Same query twice	10	10/10 (100%)

Sample chains:

• Eiffel → Paris → France (2-hop)
• Pyramids → Cairo → Egypt → Falafel (3-hop)
• France → French + Temperate (cross-domain)

Key insight: The 3-hop cuisine chain works because bipolar bind is commutative — bind(food, country) = bind(country, food), so unbind(country) from a food→country memory still resolves the correct food.

Test 126: CLI Binary Integration (55/55)

Architecture: Verifies that the CLI binary logic produces correct results for all query scenarios.

Three sub-tests:

Sub-test	Description	Result
Direct query verification	All 25 relation pairs	25/25 (100%)
Chain output verification	5 landmark→city→country chains	5/5 (100%)
Similarity threshold	All results > 0.10	25/25 (100%)

Similarity range: min 0.266, max 0.871. All well above the 0.10 threshold, confirming strong signal separation at DIM=1024 with 30 entities and 5 relations.

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CLI Binary Architecture

src/query_cli.zig (280 lines)
├── Entity definitions (30 names, seeds)
├── Relation definitions (5 types, pairs)
├── bipolarRandom() — same seeds as test suite
├── treeBundleN() — relation memory construction
├── findEntity() — case-insensitive name lookup
├── findRelation() — relation name lookup
├── main() — argument parsing + query dispatch
│   ├── --info — show KG metadata
│   ├── --list — enumerate entities
│   ├── --relations — enumerate relations
│   ├── --chain <entity> <rel1> <rel2> ... — multi-hop
│   └── <entity> <relation> — direct query
└── build.zig registration (zig build query)

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.vibee Specifications

Three specifications created and compiled:

1. specs/tri/named_entity_registry.vibee — string-to-vector mapping
2. specs/tri/multi_hop_cli_pipeline.vibee — multi-hop string pipeline
3. specs/tri/cli_binary_integration.vibee — CLI binary verification

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 Dispatch	PASS
11.24	124-126	Interactive CLI Binary	PASS

Total: 398 tests, 394 pass, 4 skip, 0 fail

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Critical Assessment

Strengths

1. First user-facing product — actual CLI binary that users can interact with
2. 100% accuracy maintained across all 145 queries including named dispatch, multi-hop, and cross-domain
3. Zero accuracy loss from string-based lookup — named registry adds no error
4. Bipolar commutativity enables reverse queries (unbind country from food→country memory to find food)
5. Self-contained binary — no external dependencies, builds from source in seconds

Weaknesses

1. Static KG — entities and relations are hardcoded in the source, not loaded from file
2. 30 entities only — the CLI uses stack allocation (not heap), limiting to a small demo KG
3. No interactive REPL — each query requires a full zig build query invocation
4. Case-sensitive matching — entity lookup requires exact spelling

Tech Tree Options for Next Iteration

Option	Description	Difficulty
A. File-Based KG Loading	Load entities and relations from JSON/CSV file at startup	Medium
B. Interactive REPL Mode	zig build query -- --repl for continuous query sessions	Medium
C. REST API Server	HTTP endpoint for querying the KG from web clients	Hard

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Conclusion

Level 11.24 delivers the first interactive CLI binary for Trinity's symbolic reasoning engine. Users can query a knowledge graph from the command line with natural entity names, execute multi-hop chains, and discover available entities and relations. The system achieves 100% accuracy across all 145 test queries.

The CLI binary (zig build query) is the culmination of 24 development cycles — from basic ternary VSA operations to a complete, interactive, user-facing symbolic AI tool. It proves that the architecture is not just theoretically sound but practically usable.

Trinity Local. CLI Lives. Quarks: Commanded.