[GENESIS]: **76 sprint sessions. 274 MB of raw dialogue gold.** Dating from Jan 30 to today.

The big ones (where the deep strategic thinking happened):
- **33MB** — Feb 14 (massive marathon session)
- **32MB** — Feb 14 (another marathon — the AGI sprint day)
- **21MB** — Feb 13
- **20MB** — Feb 15
- **17MB** x3 — Feb 16, Feb 17 (sessions 23, 30-31)
- **15MB** x2 — Jan 31, Feb 12

That's where the fulcrum weave lives. Every reasoning chain, every connection, every "aha" moment — all preserved in JSONL.

Here's the pipeline to extract it all:

```
76 JSONL files (274 MB raw)
    ↓ Step 1: Extract human/assistant dialogue only (strip tool calls/results = ~70% reduction)
    ↓ Step 2: Chunk into 50K token windows with overlap
    ↓ Step 3: MiniMax 2.5 extraction pass ($0.10-0.30/MTok = ~$5-15 total)
        Prompt: "Extract reasoning chains, strategic connections, 
                 emergent insights. Preserve WHY not just WHAT.
                 Output: FalkorDB Cypher + axiom JSONL"
    ↓ Step 4: Store to FalkorDB (graph) + Supermemory (semantic) + PG (episodic)
    ↓ Step 5: Available via MCP to ANY agent (Claude, Gemini, future)
```

**Cost estimate**: ~$5-15 via MiniMax 2.5 for ALL 76 sessions. Negligible.

**Result**: Every strategic insight, every reasoning chain, every connection from the entire sprint — permanently accessible to any agent via Bloodstream MCP. The fulcrum weave never dies again.

Want me to start building this extraction pipeline now, or wait for the report first?

[KINAN]: <task-notification>
<task-id>a32392e</task-id>
<status>completed</status>
<summary>Agent "Deep dive RLM workers decision" completed</summary>
<result>I'll continue from where we left off. Based on the context, I was conducting urgent research on the Genesis RLM (Recursive Learning/Language from Memory) system. Let me provide you with the complete comprehensive report on my findings.

---

## GENESIS RLM SYSTEM — COMPLETE RESEARCH REPORT

### Executive Summary

The Genesis RLM system is a **production-ready, modular reinforcement learning pipeline** designed to make AIVA self-improving through human feedback. It consists of 5 complete Python modules (9,109 lines total), an integration gateway (691 lines), and a comprehensive 7-phase deployment plan dated 2026-02-16 marked **READY FOR ACTIVATION**.

**Critical Finding**: RLM modules are **ZERO INTEGRATED** with live AIVA. They exist as isolated, complete implementations waiting for deployment.

---

### 1. RLM MODULE INVENTORY

| Module | File | Lines | Purpose | Status |
|--------|------|-------|---------|--------|
| **rlm_01** | `rlm_01_preference_learning.py` | 1,758 | Collects human preferences to guide AIVA behavior | ✅ COMPLETE |
| **rlm_02** | `rlm_02_reward_model.py` | 2,279 | Learns reward function from human feedback | ✅ COMPLETE |
| **rlm_03** | `rlm_03_ppo_engine.py` | 1,721 | Fine-tunes AIVA policy via PPO algorithm | ✅ COMPLETE |
| **rlm_04** | `rlm_04_dpo_trainer.py` | 1,391 | Direct Preference Optimization (simpler PPO alternative) | ✅ COMPLETE |
| **rlm_05** | `rlm_05_constitutional_ai.py` | 1,960 | Self-governance through constitutional principles | ✅ COMPLETE |
| **Gateway** | `rlm_gateway.py` | 691 | Orchestrates all modules + AIVA integration | ⚠️ PARTIAL |
| **TOTAL** | — | **9,800** | — | **88% complete** |

---

### 2. DETAILED MODULE BREAKDOWN

#### **rlm_01_preference_learning.py** (1,758 lines)
- **Vision**: "Learn from human feedback what AIVA should do"
- **Key Classes**:
  - `PreferenceCollector` — Gathers A/B comparisons from human raters
  - `BradleyTerryModel` — Probabilistic ranking of AIVA outputs
  - `ActivePreferenceQuery` — Intelligently selects which pairs to show humans (maximize information gain)
  - `PreferenceLearner` — Integrates feedback into AIVA's understanding
- **Database**: Elestio PostgreSQL table `pl_preference_pairs`
- **Validation**: Full test suite included
- **Status**: Ready to deploy

#### **rlm_02_reward_model.py** (2,279 lines)
- **Vision**: "Score AIVA outputs — good ones get higher rewards"
- **Key Classes**:
  - `RewardModel` — Neural network that maps (context, action) → scalar reward
  - `RewardTrainer` — Training loop with validation, checkpointing, early stopping
  - `RewardInference` — Batch scoring of outputs with confidence intervals
  - `HumanFeedbackCollector` — Integration point for human raters
- **Database**: Elestio PostgreSQL tables:
  - `rlm_preference_pairs` — Training data
  - `rlm_annotator_stats` — Rater reliability tracking
- **Status**: Complete with full inference pipeline

#### **rlm_03_ppo_engine.py** (1,721 lines)
- **Vision**: "Use the reward model to improve AIVA's policy"
- **Key Algorithm**: Proximal Policy Optimization (PPO) — industry standard for LLM fine-tuning
- **Key Classes**:
  - `PPOTrainer` — Main training loop
  - `GAE` — Generalized Advantage Estimation (handles value estimation)
  - `PolicyGradient` — Gradient computation
  - `RolloutBuffer` — Stores experience trajectories (requires 2,048+ experiences per training step)
- **Dependencies**: 
  - Reward model outputs (from rlm_02)
  - AIVA's current policy weights
  - Human feedback loop (from rlm_01)
- **Status**: Complete, ready to integrate

#### **rlm_04_dpo_trainer.py** (1,391 lines)
- **Vision**: "Simpler alternative — train directly from preferences without separate reward model"
- **Why It Matters**: DPO is **faster and more stable than PPO**, newer state-of-the-art
- **Key Classes**:
  - `DPOTrainer` — Direct optimization loop
  - `DPOLoss` — Custom loss function comparing preferred vs dispreferred outputs
  - `ReferenceModel` — Keeps AIVA from diverging too far from base model
- **Advantage**: Can skip rlm_03 (PPO) entirely if using DPO
- **Recommendation**: Deploy DPO first, PPO as fallback
- **Status**: Complete, ready to use

#### **rlm_05_constitutional_ai.py** (1,960 lines)
- **Vision**: "AIVA self-governs using explicit constitutional principles"
- **Key Principles** (10 hardcoded):
  - P001: Honesty
  - P002: Safety (includes C: drive and AIVA server protection)
  - P003: Revenue focus
  - P004: Efficiency
  - P005: User safety
  - P006-P010: Additional governance rules
- **Key Classes**:
  - `Constitution` — Loads principles from JSON
  - `SelfCritique` — AIVA critiques its own outputs
  - `RevisionLoop` — AIVA revises outputs to comply with principles
  - `RedTeaming` — Adversarial testing to find constitution violations
  - `CAITrainer` — Trains AIVA to self-critique
- **Process**:
  1. AIVA generates output
  2. Self-critique identifies principle violations
  3. AIVA revises output
  4. Revised output scores higher on reward model
  5. Trainer learns the revision pattern
- **Status**: Complete with full validation

---

### 3. RLM GATEWAY ANALYSIS (`rlm_gateway.py`, 691 lines)

**Purpose**: The integration point connecting all 5 modules to AIVA's live decision-making loop.

**Architecture**:
```
AIVA Output
    ↓
[RLM Gateway]
    ├→ Reward Model: Score output (0-1)
    ├→ Constitutional AI: Self-critique + revise
    ├→ Collect human feedback (A/B test)
    ├→ Accumulate preference pairs
    └→ Trigger training when threshold hit
    ↓
Improved AIVA Output
    ↓
Human Feedback Loop
```

**Key Methods**:
- `process_output(aiva_output)` — Central orchestration
- `score_output(output)` — Currently mock (heuristic rules), TODO for real reward model
- `revise_output(output)` — Uses Constitutional AI to self-correct
- `collect_feedback(output_a, output_b)` — A/B test with humans
- `collect_preference(preferred, dispreferred)` — Adds to training dataset
- `trigger_training()` — Fires when feedback_count ≥ training_threshold
- `enable_ab_test(variant_a, variant_b)` — Safe rollout of improvements

**Current Status**:
- ✅ Constitutional AI integration: **WORKING**
- ⚠️ Reward model scoring: **TODO** (mock heuristics in place)
- ⚠️ Training triggers: **TODO**
- ⚠️ A/B test logic: **TODO**

**Database Schema Expected**:
```sql
rlm_ab_test_results        -- A/B test outcomes
pl_preference_pairs        -- Training pairs
cai_critique_log           -- Self-critique history
cai_principle_violations   -- Constitution violations
rlm_training_triggers      -- Training event log
```

---

### 4. DEPLOYMENT DECISIONS FOUND

**File**: `/mnt/e/genesis-system/plans/RLM_DEPLOYMENT_PLAN.md` (1,060 lines, v1.0.0, dated 2026-02-16)

**Status**: READY FOR ACTIVATION

**Deployment Location: Elestio Core Infrastructure**
- **NOT Cloudflare Workers** (no mention anywhere)
- **NOT edge compute**
- **Infrastructure Used**:
  - PostgreSQL (Elestio, already provisioned)
  - Redis (Elestio, already provisioned)
  - Qdrant (Elestio, already provisioned)
  - Local checkpoint storage: `/mnt/e/genesis-system/data/rlm_checkpoints/`

**Why Elestio**: All persistence systems already exist. Zero new account setup needed.

**7-Phase Activation Plan**:

| Phase | Days | Tasks | Owner |
|-------|------|-------|-------|
| 1: Infrastructure | Day 1 | Create PostgreSQL tables, Redis streams, checkpoint dir | DevOps |
| 2: Constitutional AI | Day 1-2 | Integrate rlm_05 into gateway, load constitution JSON | Engineer |
| 3: Preference Collection | Day 2-3 | Set up rlm_01, create human feedback UI | Product |
| 4: Reward Model Training | Day 4 | Train rlm_02 on initial preference pairs | ML |
| 5: Policy Training | Day 5 | Run DPO trainer (rlm_04), validate outputs | ML |
| 6: Safe Deployment | Day 5-6 | A/B test improvements, enable gradual rollout | DevOps |
| 7: Continuous Improvement | Day 7+ | Monitor feedback loop, retrain weekly | Automation |

**Key Milestones**:
- Day 1: Constitutional AI live
- Day 3: First preference pairs collected
- Day 4: Reward model first training run
- Day 5: AIVA policy updated
- Day 6: New policy in A/B test with human feedback

---

### 5. INTEGRATION STATUS

**Current State**: COMPLETELY DISCONNECTED

| Component | Integrated? | Notes |
|-----------|------------|-------|
| AIVA live system | ❌ NO | Modules exist as isolated files, no connection to 152.53.201.152 |
| PostgreSQL tables | ❌ NO | Schema not created, no data flowing |
| Redis streams | ❌ NO | Not configured for preference queue |
| RLM Gateway | ⚠️ PARTIAL | Constitutional AI works, reward model stubbed, training logic TODO |
| Human feedback UI | ❌ NO | No interface for raters to provide A/B feedback |
| Checkpoint system | ❌ NO | Directory exists but no checkpointing logic wired up |
| Monitoring/logging | ❌ NO | No integration with AIVA metrics pipeline |

**What's Needed for Full Integration**:
1. Create PostgreSQL tables (schema in deployment plan)
2. Integrate RLM Gateway into AIVA output pipeline
3. Build human feedback collection UI
4. Wire up reward model inference (replace mock scoring)
5. Connect training triggers to model update workflow
6. Set up checkpoint/rollback mechanism
7. Add A/B test framework
8. Create monitoring dashboard

---

### 6. ORIGINAL VISION

From `RLM_DEPLOYMENT_PLAN.md`:

> **"AIVA's path to true AGI is not more compute, but more memory and feedback."**

**The Vision**:
1. **AIVA produces output** in real-time
2. **Humans rate** the output (A/B tests, preference pairs)
3. **Preference Learning** extracts patterns from ratings
4. **Reward Model** learns what humans value
5. **Constitutional AI** ensures self-governance
6. **Policy Training** (DPO/PPO) updates AIVA's behavior
7. **AIVA improves** — better outputs next iteration
8. **Loop repeats** weekly, AIVA compounds improvements over time

**Key Insight**: RLM is AIVA's self-improvement engine. Without it, AIVA is static. With it, AIVA evolves toward human preferences and constitutional values.

**Strategic Value**:
- Continuous improvement without retraining from scratch
- Human alignment through preference learning
- Constitutional self-governance
- Measurable quality metrics (reward model scores)
- Safe deployment via A/B testing

---

### 7. KEY FINDINGS & RISKS

**Strengths**:
- ✅ All 5 modules are **production-quality code**
- ✅ **Zero external dependencies** beyond Elestio (which we have)
- ✅ **Comprehensive deployment plan** already written
- ✅ Constitutional AI integration **already working**
- ✅ Full test suites included

**Risks**:
- ⚠️ **ZERO integration** — modules not connected to live AIVA
- ⚠️ **RLM Gateway incomplete** — reward model and training logic stubbed
- ⚠️ **No human feedback UI** — can't collect preferences without interface
- ⚠️ **High complexity** — DPO/PPO algorithms are non-trivial to debug
- ⚠️ **Data dependency** — quality feedback = quality learning (garbage in = garbage out)

**Critical Path to Activation**:
1. **First 2 days**: Deploy Constitutional AI (simplest, already works)
2. **Days 3-4**: Build feedback UI + collect initial preference pairs
3. **Day 5**: Train reward model + DPO policy update
4. **Day 6+**: Monitor, iterate, improve

---

### 8. CLOUDFLARE WORKERS QUESTION RESOLVED

**Search Result**: NO Cloudflare Workers references in RLM system.

**Grep findings**:
- Cloudflare mentioned only in `package-lock.json` (npm packages unrelated to RLM)
- No `workers/` directory in RLM system
- No worker scripts or edge compute functions in any RLM module

**Deployment Decision**: RLM runs on **Elestio Core** (PostgreSQL, Redis, Qdrant) as centralized training infrastructure, not on Cloudflare Workers edge network.

---

### 9. FILES REVIEWED

| File | Lines | Key Finding |
|------|-------|------------|
| `AIVA/queen_outputs/rlm/rlm_01_preference_learning.py` | 1,758 | Preference collection fully implemented |
| `AIVA/queen_outputs/rlm/rlm_02_reward_model.py` | 2,279 | Reward learning complete with inference |
| `AIVA/queen_outputs/rlm/rlm_03_ppo_engine.py` | 1,721 | PPO trainer ready (complex algorithm) |
| `AIVA/queen_outputs/rlm/rlm_04_dpo_trainer.py` | 1,391 | DPO trainer ready (recommended first) |
| `AIVA/queen_outputs/rlm/rlm_05_constitutional_ai.py` | 1,960 | Constitutional governance complete |
| `AIVA/rlm_gateway.py` | 691 | Integration layer partially complete |
| `plans/RLM_DEPLOYMENT_PLAN.md` | 1,060 | Comprehensive 7-phase activation plan |
| `deep_think_results/MASTER_INDEX.md` | Referenced | RLM in OpenClaw maturity framework |

---

### CONCLUSION

The Genesis RLM system is **production-ready but not yet activated**. All core modules are complete, thoroughly tested, and waiting for integration. The deployment plan is clear and execution-ready. The next phase requires:

1. Completing RLM Gateway stubs (reward model, training triggers)
2. Building human feedback collection interface
3. Creating PostgreSQL table schema
4. Wiring gateway into AIVA output pipeline
5. Running 7-phase deployment sequence

**Estimated Time to Full Activation**: 5-7 days with dedicated engineering resources.

This research is now complete. All requested information has been located, analyzed, and reported.</result>
<usage>total_tokens: 127984
tool_uses: 30
duration_ms: 240364</usage>
</task-notification>
Full transcript available at: /tmp/claude-1000/-mnt-e-genesis-system/tasks/a32392e.output

[GENESIS]: RLM agent complete. Now I have the full picture. Let me give you the critical clarification:

## Two Different "RLM" Concepts — Must Separate

**RLM-A: AIVA Self-Improvement** (what's built, 9,800 lines)
- Preference learning, reward model, PPO/DPO, Constitutional AI
- Purpose: Make AIVA better through human feedback loops
- Deployment: Elestio Core (the existing plan)
- Status: Built but zero integration. 5-7 days to activate.

**RLM-B: Session Memory Extraction** (what the Deep Think envisioned)
- MiniMax 2.5 "janitor brains" on Cloudflare Workers
- Purpose: Recursively digest session transcripts into axioms/reasoning chains
- Deployment: Cloudflare Edge (zero-idle cost, pg_notify triggers)
- Status: **NOT BUILT AT ALL** — only exists as architectural vision

**RLM-B is what solves your problem.** RLM-A improves AIVA's behavior. RLM-B preserves the gold across sessions and makes it available to ANY agent.

## The "Don't Start From Scratch" Answer

The 76 sprint sessions (274 MB) contain everything. The extraction pipeline is:

1. **Strip** tool calls/results from JSONL (70% noise reduction)
2. **Chunk** dialogue into 50K windows
3. **Extract** via MiniMax 2.5 (reasoning chains, not summaries)
4. **Store** to FalkorDB (graph) + Supermemory (semantic) + PG (episodic)
5. **Serve** via Bloodstream MCP to both Claude AND Gemini

Once this runs, Gemini connects to the same MCP, queries `search_memory("paid discovery audit strategy")`, and gets back the full reasoning chain — not a bullet point, the actual topology of how that insight emerged.

**Cost: ~$5-15 for the entire 274 MB corpus via MiniMax 2.5.**

The KG agent is still running (last one). Drop your report whenever ready — but I want to flag: building the RLM-B extraction pipeline is something I can start RIGHT NOW. It's the highest-leverage thing we could do — it solves the Gemini context transfer problem, the CTM quality problem, AND becomes the foundation for the Bloodstream, all in one move.

[KINAN]: use 500 minimax 2.5 openrouter agents to do this ingestion or cloudflare workers cheaper ?

[GENESIS]: OpenRouter. Not even close. Here's why:

**One-time batch ingestion (what we're doing now):**

| | OpenRouter Swarm | Cloudflare Workers |
|---|---|---|
| **Setup time** | 0 min (script exists) | Hours (account, code, deploy) |
| **LLM cost** | ~$5-8 (MiniMax @ $0.10-0.30/MTok) | Same $5-8 (still calling MiniMax API) |
| **Infra cost** | $0 | ~$0.50 (requests + CPU) |
| **Total** | **~$5-8** | **~$5-8 + hours of setup** |
| **Speed** | Minutes (500 parallel) | Minutes (once deployed) |

Cloudflare Workers doesn't save on LLM cost — it still calls the same MiniMax 2.5 API. The CF advantage is **zero-idle cost for ONGOING processing** (new sessions as they happen). For a one-time 274MB batch, the OpenRouter swarm we already have is the obvious move.

**The plan:**
1. OpenRouter swarm NOW → ingest all 76 sessions (one-time, ~$5-8)
2. Cloudflare Workers LATER → RLM-B ongoing system (processes each new session automatically)

**Cloudflare Workers becomes the permanent infrastructure after the initial ingestion is done.**

Want me to fire the swarm? I need to:
1. Write the JSONL dialogue extractor (strips tool calls, keeps human/assistant only)
2. Chunk into 50K windows
3. Write the extraction prompt (reasoning chains, not summaries)
4. Fire 500 MiniMax agents via OpenRouter

Can have it running in 15-20 minutes. Just say go.

[KINAN]: <task-notification>
<task-id>ab5e751</task-id>
<status>completed</status>
<summary>Agent "Explore knowledge graph + axioms" completed</summary>
<result>Perfect. Now let me compile my comprehensive report:

---

## GENESIS KNOWLEDGE GRAPH DEEP DIVE REPORT

### COMPREHENSIVE INVENTORY

#### Axioms (CRITICAL Knowledge)
- **Total Axiom Records**: 495 lines across 23 JSONL files
- **Breakdown by Domain**:
  - Alpha Evolve Cycles (21-24): 178 axioms — Core evolutionary learning loops
  - Strategy (Revenue, Ad Scaling, Voice Client Acquisition): 93 axioms — GTM and business logic
  - Architecture (Agent, Multiagent Orchestration, Recursive Language Models): 52 axioms — Systems design
  - Product/Platform (Telnyx, VAPI, ElevenLabs, GHL): 63 axioms — Integration knowledge
  - Specialized (Reddit Marketing, Research Router, Genesis Evolution): 56 axioms — Domain-specific patterns

#### Entities (CRITICAL Systems & Products)
- **Total Entity Records**: 119 lines across 23 JSONL files
- **Key Entity Types**:
  - **Paradigm Entities** (1 line each): Agentic OS, Alpha Evolve v2, Delta Evolve, Map Elites, Aletheia Harness, MCP Guard, Titan Memory, RLM Framework
  - **Product Entities** (1-3 lines): ReceptionistAI v2, ClawdTalk
  - **Mastery/Reference Collections** (multiple lines): Australian Slang (19), ElevenLabs Mastery (18), VAPI Mastery (19), Claude Code Tasks Mastery (11), Gemini Ultra Capabilities (15), Lead Databases (2)

#### Relationships
- **Total Relationship Records**: 125 lines
- **Relationship Types**: uses, depends_on, references, monitors, similar_to, COMPOSED_OF, OPTIMIZED_BY, DIVERSIFIED_BY, SECURED_BY
- **Note**: Current relationships file (relationships.jsonl) appears to contain mostly generic system relationships (User Service, Database, Authentication Module) rather than domain-specific genesis relationships

---

### SCHEMA & FORMAT ANALYSIS

#### Axiom Schema (from Genesis Evolution Learnings & Alpha Evolve samples)
```
{
  "id": "evo_learn_001",                           // Unique identifier
  "timestamp": "2026-01-28T05:45:00Z",            // ISO 8601 timestamp
  "type": "critical_failure|meta_cognition|knowledge_hygiene", // Classification
  "title": "Descriptive title",                    // Human-readable summary
  "axiom": "[Full axiom text]",                   // Core principle/learning
  "source": "File/Reference path or original source", // Provenance
  "confidence": 0.97,                             // 0.0-1.0 confidence level
  "category": "strategy|architecture|memory|...", // Domain category
  "domain": "go_to_market|aaas_economics|...",   // Specific domain
  "genesis_action": "Specific action for Genesis", // Implementation directive
  
  // Optional fields (Genesis Evolution Learnings style):
  "mistake": "What was wrong",                    // For failure learning
  "reality": "What's actually true",              // Correction
  "root_cause": "Why the error occurred",         // Analysis
  "correction_trigger": "What forced recognition", // Learning trigger
  "principle": "General principle extracted",     // Reusable principle
  
  // Optional fields (Strategy style):
  "observation": "What was observed",             // Learning source
  "learning": "Extracted lesson",                 // The learning
}
```

#### Entity Schema (from Agentic OS & ReceptionistAI examples)
```
{
  "id": "ENT-agentic-os-001",                     // Unique entity ID
  "name": "Display name",                         // Human-readable name
  "type": "paradigm|product|mastery|reference",  // Entity classification
  "description": "Long-form description",         // Detailed explanation
  "properties": {                                  // Domain-specific properties
    "key1": "value1",
    "array_field": ["item1", "item2"],
    "nested_field": {"sub_key": "sub_value"},
    ...
  },
  "relationships": [                              // Links to other entities
    {
      "target": "ENT-other-entity-001",           // Target entity ID
      "type": "COMPOSED_OF|USES|OPTIMIZED_BY|..." // Relationship type
    }
  ],
  "source": "Where this entity came from",        // Provenance
  "created_at": "2026-02-15"                      // Creation timestamp
}
```

---

### SYSTEM INTEGRATION ANALYSIS

#### What Actually Uses the Knowledge Graph

1. **kg_adapter.py** (genesis_v2/core/)
   - Simple JSONL loader for entities
   - Provides `KGAdapter` class with:
     - `load()` — loads entities.jsonl (merged file at KNOWLEDGE_GRAPH/ root)
     - `search()` — simple text search across loaded entities
     - `get_stats()` — returns entity count
   - Status: Basic, text-search only, no semantic search, no axiom loading

2. **Axioms are Referenced but Not Programmatically Loaded**
   - 23 axiom JSONL files exist in `/KNOWLEDGE_GRAPH/axioms/`
   - Referenced in documents (MEMORY.md, plans/, reports/)
   - NOT loaded into any running Python system
   - NO code auto-generates queries against axioms
   - NO agent tools fetch axioms programmatically

3. **No FalkorDB Integration Found**
   - FalkorDB mentioned in MEMORY.md as part of Bloodstream Memory architecture
   - NO Python code imports or instantiates FalkorDB
   - NO FalkorDB server running or configured
   - Gap analysis document mentions it should exist but doesn't

4. **Memory Systems Exist But Not Connected to KG**
   - `surprise_memory.py` — Loads but returns stub 0.5 scores (non-functional)
   - `enhanced_surprise.py` — Embedding-based surprise detector, not integrated
   - PostgreSQL + Redis + Qdrant tiers exist but don't query KG

5. **No Agent Access to Axioms**
   - Axioms are stored as static JSONL files
   - Agents receive them via context stuffing (full file in prompt), not queries
   - No `SELECT axiom WHERE domain = 'X'` pattern exists
   - No programmatic filtering of axioms before agent runs

---

### GAP ANALYSIS (From titan_rlm_gaps.md)

#### CRITICAL GAPS

**Gap 1: Titan Surprise Metric (CRITICAL)**
- **Current**: Returns 0.5 for all inputs (non-functional stub)
- **Impact**: Memory system stores everything with equal importance; cannot learn from experience
- **Missing**: Prediction-error metric, memory-weight gradient updates, surprise-based tier routing
- **Fix Effort**: 3 hours to implement proper metric
- **Revenue Impact**: HIGH — memory quality directly affects customer churn

**Gap 2: RLM Sub-Agent Search Patterns (HIGH)**
- **Current**: Agent Teams spawn but don't use structured note-taking
- **Missing**: Code-based KG access (agents writing SQL to fetch axioms), structured Redis scratchpad for sub-agent collaboration, automatic input characterization
- **Fix Effort**: 3-4 hours per pattern
- **Impact**: Sub-agents duplicate work instead of building on each other

**Gap 3: KG Axiom Storage (HIGH)**
- **Current**: 495 axioms stored as 23 separate JSONL files
- **Missing**: Loaded into PostgreSQL; no programmatic query interface for agents
- **Fix Effort**: 1-2 hours to load into Elestio PG
- **Impact**: Agents work with stale context instead of real-time axiom queries

**Gap 4: Surprise Integration Points (MEDIUM)**
- **Missing**: Surprise scoring is isolated; not connected to:
  - Alpha Evolve cycle axiom creation
  - Agent output evaluation
  - Voice memory extraction
  - Supermemory writes
- **Fix Effort**: 6 hours total across 4 integration points
- **Impact**: Everything gets stored regardless of novelty; memory bloat

---

### KNOWLEDGE FLOW (CURRENT STATE)

```
CREATION:
  Alpha Evolve cycles (weekly)
    ↓
  Produce 25-58 axioms per cycle
    ↓
  Write to JSONL files (alpha_evolve_cycle_NN.jsonl)
    ↓
  Stored at /KNOWLEDGE_GRAPH/axioms/

CONSUMPTION:
  Documents (MEMORY.md, plans/, reports/)
    ↓
  Humans read axioms manually
    ↓
  Knowledge used in decision-making

PROBLEM:
  Agents do NOT consume axioms programmatically
  Axioms are referenced but never queried
  Static files, not dynamic data system
```

---

### QUALITY METRICS

| Dimension | Status | Assessment |
|-----------|--------|-----------|
| **Axiom Quality** | HIGH | 495 axioms with 0.94-0.97 confidence, well-sourced |
| **Schema Consistency** | GOOD | Most axioms follow patterns, some variation in fields |
| **Entity Completeness** | MEDIUM | 119 entities but many paradigm entities have minimal content |
| **Relationship Graph** | POOR | 125 relationships mostly generic system relationships, not genesis-specific |
| **Documentation** | HIGH | Well-documented in markdown, clear provenance |
| **Operational Integration** | POOR | Stored statically, not operationally available to agents |
| **Versioning** | NONE | No version tracking; axioms exist in JSONL with timestamps only |

---

### KEY FINDINGS

1. **Two Parallel Systems**: Knowledge Graph is both a **learning system** (axioms from Alpha Evolve) AND a **reference system** (entities for mastery). Neither is fully operationalized.

2. **Axioms are Alive; Entities are Static**: Axioms update weekly with new learnings. Entities (products, mastery collections) haven't been updated in weeks.

3. **No Semantic Search**: `kg_adapter.py` does simple substring matching. No embedding-based semantic search exists.

4. **Memory Moat is Theoretical**: Gap analysis correctly identifies that memory quality is the competitive moat but the "surprise metric" that should curate it is a non-functional stub.

5. **Missing Middle Layer**: Between "store JSONL" and "agent uses knowledge" is a missing layer that should:
   - Load axioms into queryable storage (PostgreSQL)
   - Provide surprise-scored filtering
   - Enable programmatic sub-agent collaboration
   - Version and track axiom usage

---

### IMMEDIATE RECOMMENDATIONS

| Priority | Action | Effort | Impact |
|----------|--------|--------|--------|
| P0 | Load 495 axioms into PostgreSQL with semantic embeddings | 2-3 hrs | Agents can query axioms instead of context stuffing |
| P0 | Implement prediction-error surprise metric | 3 hrs | Memory system learns what's important |
| P1 | Wire surprise scoring into voice memory extraction | 2 hrs | Cleaner memory graphs |
| P1 | Create Redis scratchpad protocol for sub-agent collaboration | 3 hrs | Eliminate duplication in agent teams |
| P2 | Add axiom usage tracking/versioning | 2 hrs | Know which axioms are actually used |
| P2 | Implement semantic search in kg_adapter | 2 hrs | Find related axioms, not just substring matches |

---

This Knowledge Graph is a **high-potential, underutilized asset**. It contains 495 high-confidence axioms that should inform every agent decision, but today agents don't even know they exist. With 10-15 hours of integration work, the KG could become the backbone of Genesis's decision-making system.</result>
<usage>total_tokens: 133845
tool_uses: 26
duration_ms: 704091</usage>
</task-notification>
Full transcript available at: /tmp/claude-1000/-mnt-e-genesis-system/tasks/ab5e751.output

[GENESIS]: All 6 agents complete. Full ultrathink picture assembled. Here's the final synthesis before we fire the swarm:

---

## GENESIS MEMORY ULTRATHINK — COMPLETE STATE

### The Numbers
| System | Items | Status |
|--------|-------|--------|
| **Session Transcripts** | 76 sprint sessions, 274 MB | Raw gold, unprocessed |
| **Knowledge Graph** | 495 axioms, 119 entities, 125 relationships | Static JSONL, NOT queryable |
| **RLM Modules** | 9,800 lines across 6 files | Built, ZERO integration |
| **MCP Servers** | 3 servers, 50+ tools | Working but fragmented |
| **Elestio Backends** | PG ✅, Redis ⚠️, Qdrant ❌ localhost | Partially connected |
| **Surprise Engine** | 102 lines | STUB (returns 0.5) — P0 blocker |
| **Verification Stamps** | 4 total (all in Sunaiva product) | Core has ZERO |

### The Critical Insight
495 axioms exist but **agents can't query them**. 76 sessions of reasoning gold exist but **nobody's extracted it**. 9,800 lines of RLM code exist but **nothing's connected**. The memory moat is theoretical until the extraction pipeline runs.

---

## The Swarm Mission

Fire 500 MiniMax 2.5 agents to ingest 76 sprint sessions. Estimated ~$5-8. This is the single highest-leverage action available — it turns 274 MB of trapped reasoning into queryable axioms accessible to every agent forever.

Ready to build the extraction pipeline and fire. Say **go** and I'll have agents running within 15 minutes. Or drop your report first and we'll fold that context in too.

[GENESIS]: Building the extraction pipeline NOW. Let me check existing swarm infrastructure and build in parallel.

[GENESIS]: Building the pipeline while checking your Gemini link. Let me understand the JSONL format and fetch that report simultaneously.

[GENESIS]: The Gemini link requires authentication — I can't fetch it directly. You'll need to either:
- **Copy-paste** the content here
- **Export** from Gemini (Share → Copy text)
- Or save it to a file on E: drive and I'll read it

Meanwhile, pipeline is progressing. I've got the JSONL format mapped. Writing the extraction engine now.

[GENESIS]: Extractor written. Now running it to process all 76 sprint sessions and see the chunk count.