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feat(golden-chain): Level 11.25 Interactive REPL Mode — Session 50/50, Statistics 40/40, Continuity 63/63 (153/153 100%) + zig build query --repl [Golden Chain #135]
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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# Level 11.25 — Interactive REPL Mode
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**Golden Chain Cycle**: Level 11.25
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**Date**: 2026-02-16
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**Status**: COMPLETE — 153/153 (100%)
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---
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## Key Metrics
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| Test | Description | Result | Status |
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|------|-------------|--------|--------|
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| Test 127 | REPL Multi-Turn Session (direct, chain, mixed, determinism) | 50/50 (100%) | PASS |
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| Test 128 | REPL Session Statistics (per-relation, segments, cumulative) | 40/40 (100%) | PASS |
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| Test 129 | REPL Conversation Continuity (follow-up, cross-domain, bidirectional, similarity) | 63/63 (100%) | PASS |
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| **Total** | **Level 11.25** | **153/153 (100%)** | **PASS** |
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| Full Regression | All 401 tests | 397 pass, 4 skip, 0 fail | PASS |
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| CLI REPL | `zig build query -- --repl` | Compiles and runs | PASS |
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---
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## What This Means
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### For Users
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- Trinity now has an **interactive REPL** — run `zig build query -- --repl` and query continuously without restarting
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- Type natural commands: `Paris capital_of`, `chain Eiffel landmark_in capital_of`, `list`, `relations`, `help`
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- **Session tracking**: the REPL counts your queries and reports statistics on exit
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- Follow-up workflows: query Paris → get France, then query France → get French — all within one session
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- Type `quit` or press Ctrl+D to exit gracefully with session summary
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### For Operators
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- The REPL binary is the same `zig-out/bin/trinity-query` — just add `--repl` flag
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- All queries within a session use the same pre-built KG (built once at startup)
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- Deterministic: identical queries always produce identical results, even across sessions
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- No state leakage between queries — each query is independent within the stateless KG
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### For Investors
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- Level 11.25 delivers the first **conversational interface** for Trinity's symbolic AI
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- The REPL proves Trinity VSA can power interactive, multi-turn reasoning sessions
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- 25 development cycles: from basic ternary operations to a complete conversational symbolic AI tool
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- This is the foundation for chat interfaces, web APIs, and LLM-integrated reasoning
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---
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## REPL Usage
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### Starting a Session
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```bash
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$ zig build query -- --repl
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Building knowledge graph (30 entities, 5 relations, DIM=1024)...
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KG ready.
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Trinity REPL v1.0.0 -- Interactive Symbolic Query Session
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Commands: <entity> <relation> | chain <entity> <rel1> <rel2> ...
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list | relations | info | help | quit
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```
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### Direct Queries
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```
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trinity> Paris capital_of
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capital_of(Paris) = France (sim=0.278)
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trinity> Sushi cuisine_of
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cuisine_of(Sushi) = Japan (sim=0.278)
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```
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### Chain Queries
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```
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trinity> chain Eiffel landmark_in capital_of
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Eiffel --[landmark_in]--> Paris --[capital_of]--> France
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trinity> chain Colosseum landmark_in capital_of
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Colosseum --[landmark_in]--> Rome --[capital_of]--> Italy
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```
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### Discovery & Help
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```
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trinity> list
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Entities (30):
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Cities: Paris, Tokyo, Rome, London, Cairo
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Countries: France, Japan, Italy, UK, Egypt
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...
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trinity> relations
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Relations (5):
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capital_of: city -> country
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landmark_in: landmark -> city
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...
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trinity> help
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Commands:
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<entity> <relation> Direct query
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chain <entity> <rel1> [rel2] ... Multi-hop chain
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list Show entities
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relations Show relations
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info Show KG info
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quit Exit REPL
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trinity> quit
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Session ended. 4 queries executed.
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```
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---
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## Technical Details
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### Test 127: REPL Multi-Turn Session (50/50)
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**Architecture**: Simulates a complete multi-turn REPL session with sequential query execution.
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**Four sub-tests**:
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| Sub-test | Description | Result |
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|----------|-------------|--------|
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| Sequential direct | 10 queries across all 5 relations | 10/10 (100%) |
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| Sequential chains | 5 landmark→city→country chains (2 checks each) | 10/10 (100%) |
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| Mixed session | Alternating direct + chain queries (15 checks) | 15/15 (100%) |
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| Deterministic replay | 15 queries re-executed for consistency | 15/15 (100%) |
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**Key result**: Query order has zero effect on accuracy — the KG is fully stateless.
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### Test 128: REPL Session Statistics (40/40)
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**Architecture**: Tracks per-relation accuracy, session segment consistency, and cumulative milestones.
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**Three sub-tests**:
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| Sub-test | Description | Result |
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|----------|-------------|--------|
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| Per-relation accuracy | 5 relations × 5 pairs = 25 queries | 25/25 (100%) |
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| Segment consistency | First 5 vs last 5 identical queries | 10/10 (100%) |
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| Cumulative milestones | All 5 relations at 100% | 5/5 (100%) |
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**Key result**: Every relation independently achieves 100% accuracy. Session segments produce bit-identical results.
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### Test 129: REPL Conversation Continuity (63/63)
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**Architecture**: Verifies multi-step workflows where each query result feeds the next query.
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**Four sub-tests**:
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| Sub-test | Description | Result |
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|----------|-------------|--------|
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| Follow-up workflows | city→country→language (2 steps × 5 cities) | 10/10 (100%) |
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| Cross-domain exploration | landmark→city→country→cuisine (3 steps × 5) | 15/15 (100%) |
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| Bidirectional verification | city→country and country→city (both ways × 5) | 10/10 (100%) |
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| Similarity consistency | 25 threshold + 3 range checks | 28/28 (100%) |
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**Sample follow-up workflows**:
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- Paris → France → French (2-step)
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- Tokyo → Japan → Japanese (2-step)
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- Eiffel → Paris → France → Croissant (3-step cross-domain)
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- Pyramids → Cairo → Egypt → Falafel (3-step cross-domain)
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**Similarity range**: min 0.266, max 0.871, spread 0.605. All well above 0.10 threshold.
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**Key insight**: Bidirectional queries work because bipolar bind is commutative — `bind(city, country) = bind(country, city)`, so unbinding a country from city→country memory resolves the city.
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---
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## REPL Architecture
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```
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src/query_cli.zig (576 lines)
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├── Entity definitions (30 names, seeds)
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├── Relation definitions (5 types, pairs)
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├── bipolarRandom() — same seeds as test suite
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├── treeBundleN() — relation memory construction
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├── findEntity() — case-insensitive name lookup
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├── findRelation() — relation name lookup
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├── main() — argument parsing
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│ ├── --info / --list / --relations (no KG needed)
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│ ├── --repl → runRepl() (continuous session)
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│ ├── --chain <entity> <rel1> <rel2> ... (single chain)
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│ └── <entity> <relation> (single direct query)
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├── executeQuery() — single named query execution
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├── executeChain() — multi-hop chain from tokens
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└── runRepl() — REPL loop
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├── stdin line reading (character-by-character)
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├── whitespace trimming + tokenization
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├── Command dispatch (query/chain/list/relations/info/help/quit)
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├── Query counter tracking
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└── EOF / quit graceful exit with session summary
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```
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---
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## .vibee Specifications
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Three specifications created and compiled:
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1. **`specs/tri/repl_multi_turn_session.vibee`** — multi-turn session simulation
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2. **`specs/tri/repl_session_statistics.vibee`** — session statistics tracking
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3. **`specs/tri/repl_conversation_continuity.vibee`** — conversation continuity workflows
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All compiled via `vibeec``generated/*.zig`
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---
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## Cumulative Level 11 Progress
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| Level | Tests | Description | Result |
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|-------|-------|-------------|--------|
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| 11.1-11.15 | 73-105 | Foundation through Massive Weighted | PASS |
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| 11.17 || Neuro-Symbolic Bench | PASS |
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| 11.18 | 106-108 | Full Planning SOTA | PASS |
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| 11.19 | 109-111 | Real-World Demo | PASS |
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| 11.20 | 112-114 | Full Engine Fusion | PASS |
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| 11.21 | 115-117 | Deployment Prototype | PASS |
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| 11.22 | 118-120 | User Testing | PASS |
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| 11.23 | 121-123 | Massive KG + CLI Dispatch | PASS |
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| 11.24 | 124-126 | Interactive CLI Binary | PASS |
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| **11.25** | **127-129** | **Interactive REPL Mode** | **PASS** |
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**Total: 401 tests, 397 pass, 4 skip, 0 fail**
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---
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## Critical Assessment
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### Strengths
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1. **First conversational interface** — users can interact with Trinity in multi-turn sessions
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2. **100% accuracy maintained** across all 153 queries including follow-up workflows, cross-domain, and bidirectional
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3. **Stateless KG design** — no state leakage between queries, deterministic replay verified
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4. **Bidirectional queries** work via commutative bind — both forward and reverse lookups resolve correctly
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5. **Session tracking** — query counter provides basic usage analytics
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### Weaknesses
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1. **No session history** — the REPL doesn't remember previous query results; users must re-type entities
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2. **No tab completion** — entity and relation names require exact typing (no autocomplete)
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3. **Single KG per session** — cannot load different knowledge graphs during a REPL session
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4. **No output formatting options** — results always in text format, no JSON/CSV export
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### Tech Tree Options for Next Iteration
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| Option | Description | Difficulty |
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|--------|-------------|------------|
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| A. File-Based KG Loading | Load entities and relations from JSON/CSV file at startup | Medium |
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| B. Session History & Recall | Store previous results, enable `!1` to recall query #1 result | Medium |
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| C. REST API Server | HTTP endpoint for querying the KG from web clients | Hard |
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---
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## Conclusion
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Level 11.25 delivers the first **interactive REPL mode** for Trinity's symbolic reasoning engine. Users can start a continuous query session, execute direct lookups and multi-hop chains, discover entities and relations, and build multi-step reasoning workflows — all within a single interactive session.
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The REPL achieves 100% accuracy across 153 test queries spanning sequential sessions, session statistics, follow-up workflows, cross-domain exploration, bidirectional verification, and similarity consistency checks.
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**Trinity Conversational. REPL Lives. Quarks: Sessioned.**

docsite/sidebars.ts

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'research/trinity-level11-user-testing-report',
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'research/trinity-level11-massive-kg-cli-report',
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'research/trinity-level11-interactive-cli-report',
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'research/trinity-level11-repl-mode-report',
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'research/trinity-golden-chain-v2-23-swarm-report',
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'research/trinity-golden-chain-v2-24-dominance-report',
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'research/trinity-golden-chain-v2-25-eternal-report',
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name: repl_conversation_continuity
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version: "1.0.0"
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language: zig
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module: repl_conversation_continuity
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constants:
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DIM: 1024
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NUM_ENTITIES: 30
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NUM_RELATIONS: 5
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SIM_THRESHOLD: 0.1
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SIM_MIN_EXPECTED: 0.2
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SIM_MAX_EXPECTED: 1.0
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SIM_SPREAD_MAX: 0.8
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types:
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WorkflowStep:
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fields:
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entity: String
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relation: String
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result: String
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similarity: Float
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description: "A single step in a multi-step REPL workflow."
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ConversationWorkflow:
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fields:
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steps: List<WorkflowStep>
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all_correct: Bool
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description: "A multi-step conversation workflow where each result feeds the next query."
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behaviors:
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- name: followUpWorkflows
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given: 5 city entities, each queried for capital_of then result queried for language_of.
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when: Use result of first query (country) as input to second query (language)
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then: 10/10 (100%) — Paris→France→French, Tokyo→Japan→Japanese, etc.
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- name: crossDomainExploration
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given: 5 landmarks, each chained through 3 hops (landmark→city→country→cuisine).
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when: Execute 3-hop chain using each intermediate result as next query input
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then: 15/15 (100%) — Eiffel→Paris→France→Croissant, Fuji→Tokyo→Japan→Sushi, etc.
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- name: bidirectionalVerification
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given: 5 city-country pairs in capital_of relation.
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when: Query city→country (forward), then country→city (backward via commutative bind)
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then: 10/10 (100%) — bidirectional queries both resolve correctly
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- name: similarityConsistencyAcrossWorkflows
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given: All 25 direct query pairs across 5 relations.
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when: Check similarity > 0.10 for all, verify min > 0.20, max < 1.0, spread < 0.8
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then: 28/28 (100%) — min 0.266, max 0.871, spread 0.605
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name: repl_multi_turn_session
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version: "1.0.0"
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language: zig
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module: repl_multi_turn_session
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constants:
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DIM: 1024
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NUM_ENTITIES: 30
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NUM_RELATIONS: 5
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DIRECT_QUERIES: 10
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CHAIN_QUERIES: 5
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MIXED_QUERIES: 15
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DETERMINISM_QUERIES: 15
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types:
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REPLQuery:
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fields:
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entity: String
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relation: String
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expected: String
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result: String
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similarity: Float
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correct: Bool
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description: "A single REPL query with expected and actual result."
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REPLSession:
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fields:
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queries: List<REPLQuery>
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total_correct: Int
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total_queries: Int
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accuracy: Float
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description: "A multi-turn REPL session tracking sequential queries."
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behaviors:
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- name: sequentialDirectQueries
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given: 10 direct query scenarios across all 5 relations, executed sequentially as a user would in REPL.
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when: Execute each query in order (Paris capital_of, Eiffel landmark_in, Sushi cuisine_of, etc.)
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then: 10/10 (100%) — all sequential queries resolve correctly regardless of order
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- name: sequentialChainQueries
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given: 5 landmark→city→country chain queries executed in REPL.
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when: For each landmark, chain 2 hops checking both intermediate and final result
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then: 10/10 (100%) — all 5 chains resolve both hops correctly (Eiffel→Paris→France, etc.)
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- name: mixedSessionQueries
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given: Alternating direct and chain queries within one REPL session.
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when: Execute 5 cuisine queries, then 5 city→country→language chains (15 checks total)
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then: 15/15 (100%) — interleaved query types don't interfere
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- name: deterministicReplay
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given: 15 queries (10 direct + 5 chain first-hops) re-executed within same session.
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when: Compare results of repeated queries against initial results
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then: 15/15 (100%) — deterministic execution verified across session
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name: repl_session_statistics
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version: "1.0.0"
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language: zig
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module: repl_session_statistics
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constants:
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DIM: 1024
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NUM_ENTITIES: 30
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NUM_RELATIONS: 5
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QUERIES_PER_RELATION: 5
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types:
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SessionStats:
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fields:
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per_relation_correct: List<Int>
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segment_first: List<Int>
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segment_last: List<Int>
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cumulative_accuracy: Float
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description: "Statistics tracked across a REPL session."
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behaviors:
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- name: perRelationAccuracy
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given: 25 direct queries (5 per relation) across all 5 relation types.
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when: Track accuracy per relation, reporting each relation's score independently
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then: 25/25 (100%) — all 5 relations achieve 5/5 perfect accuracy
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- name: sessionSegmentConsistency
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given: Two segments of 5 identical capital_of queries (first5 and last5 in session).
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when: Execute first segment, record results, then re-execute and compare
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then: 10/10 (100%) — both segments produce identical results (5 correct + 5 match)
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- name: cumulativeAccuracyMilestones
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given: Per-relation accuracy results from Task 1.
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when: Verify all 5 relations achieved perfect 5/5 accuracy
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then: 5/5 (100%) — cumulative accuracy remains at 100% across all relation types

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