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PROMPT 2: MEMORY ARCHITECTURE -- UNIFIED INTELLIGENCE LAYER

 

  ---START PROMPT---

 

  I am designing a

next-generation unified memory intelligence layer for a multi-agent AI system

called Genesis. The goal is to make memory not just storage, but an active

intelligence that reasons across data sources, finds patterns,

  and self-curates.

Here is the complete context:

 

  Current State: 5

Memory Systems (Fragmented)

 

  Genesis runs 5

operational memory systems, each storing different aspects of knowledge:

 

  System 1 --

PostgreSQL (Episodic Memory)

  - Stores: Research

reports, agent state, audit logs, structured entities, workflow outputs

  - Strength:

Relational queries, joins, aggregations, ACID transactions

  - Weakness: No

semantic understanding -- keyword search only

  - Size: Growing

steadily, ~200MB

 

  System 2 -- Qdrant

(Semantic Memory)

  - Stores:

1536-dimensional embeddings of documents, conversations, learnings

  - Strength:

"Find things similar to X" -- semantic similarity at scale

  - Weakness: No

understanding of relationships between entities

  - Collection:

genesis_vectors with Cosine distance metric

 

  System 3 -- Redis

(Working Memory)

  - Stores: Rate

limits, session state, hot cache, agent queues, health checks

  - Strength:

Sub-millisecond access, TTL auto-expiry, pub/sub

  - Weakness:

Ephemeral -- data lost on restart, no persistence guarantees

 

  System 4 -- FalkorDB

(Knowledge Graph)

  - Stores: 952 nodes,

285 edges in graph named genesis

  - Node types:

Entities (products, people, concepts), Axioms (compressed learnings)

  - Edge types:

Relationships (ENABLES, GENERATES_REVENUE, DEPENDS_ON, etc.)

  - 434+ axioms

extracted over 20 Alpha Evolve cycles

  - Strength:

Relationship traversal -- "What connects AIVA to Revenue through 3

hops?"

  - Weakness: No

semantic understanding of node content, manual curation needed

 

  System 5 --

Supermemory (Cross-Session Memory)

  - Stores: War room

status, session archives, strategic decisions, learnings

  - Collection:

genesis-kinan

  - Strength: Survives

context window compaction, accessible across sessions

  - Weakness:

Unstructured, slow (200-500ms), no deduplication

 

  Existing

Intelligence Processes

 

  Titan Memory:

Surprise-based learning capture. When something unexpected happens (test fails,

protocol drifts, new pattern discovered), it's recorded as a Titan Memory entry

with tags. Current entries include insights on syntax,

  testing, protocol

drift, multi-agent orchestration, alpha evolve patterns, observability, and

more.

 

  Alpha Evolve

Protocol: Recursive self-improvement engine running on a loop:

  INGEST -> EXTRACT

-> SYNTHESIZE -> HARDCODE -> VERIFY -> EVOLVE

  Over 20 cycles, it

has extracted 434+ axioms from research, conversations, and production data.

Each axiom is a compressed learning (e.g., "Hub-and-spoke communication

reduces agent connections from O(n^2) to O(n)" or "Free-text

  handoffs are the

main source of context loss in multi-agent systems").

 

  Knowledge Graph

Entities: 42+ entities including products (ReceptionistAI, Sunaiva,

AgileAdapt), people (Kinan), concepts (Alpha Evolve, Bloodstream Architecture),

and infrastructure (Elestio, AIVA). Each has properties and

  relationships.

 

  The Core Problem

 

  These 5 systems are

silos. No layer currently:

  - Reasons ACROSS

systems (e.g., "This PostgreSQL report contradicts that Supermemory

learning")

  - Identifies

redundancy (the same fact stored in 3 different systems)

  - Scores memory

quality (is this axiom still valid? has it been contradicted?)

  - Detects patterns

across data types (semantic clusters in Qdrant that map to graph clusters in

FalkorDB)

  - Actively forgets

low-value information (Redis grows, PostgreSQL accumulates, nothing prunes)

 

  A query like

"What do we know about voice agent pricing?" currently requires

searching each system independently and manually synthesizing results. There is

no unified intelligence.

 

  What I Need: Unified

Intelligence Layer

 

  Design a layer that

sits above all 5 systems and provides:

 

  1. Cross-System

Reasoning

 

  When an agent asks a

question, the intelligence layer should:

  - Search all

relevant systems in parallel

  - Detect when

results from different systems contradict each other

  - Synthesize a

single coherent answer with provenance (which system each fact came from)

  - Flag confidence

levels based on corroboration (fact in 3 systems = high confidence)

 

  2. Automatic

Consolidation

 

  Periodically (e.g.,

nightly at 2 AM):

  - Scan for duplicate

information across systems

  - Merge duplicates,

keeping the highest-quality version

  - Identify orphaned

data (referenced nowhere, accessed never)

  - Promote frequently

accessed Redis working memory to PostgreSQL episodic memory

  - Generate new

axioms from patterns detected across systems

 

  3. Memory Quality

Scoring

 

  Each memory item

should have a quality score based on:

  - Freshness: When

was it last validated? (decay function)

  - Corroboration: How

many systems store supporting evidence?

  - Access frequency:

How often do agents retrieve this?

  - Source authority:

Research report (high) vs. casual observation (low)

  - Contradiction

count: Has it been contradicted by newer data?

 

  4. Active Forgetting

(Pruning)

 

  Not all memory is

worth keeping. Design a pruning system that:

  - Identifies

memories below a quality threshold

  - Archives (not

deletes) to cold storage before pruning

  - Handles the

"butterfly effect" -- pruning one memory might invalidate chains of

reasoning

  - Respects

"permanent" flags (some axioms should never be pruned)

  - Reports what it

pruned and why

 

  5. Pattern Detection

 

  The intelligence

layer should detect:

  - Emerging clusters

in semantic space (new topic areas forming in Qdrant)

  - Graph topology

changes (new hub nodes, disconnected components in FalkorDB)

  - Temporal patterns

(certain types of queries spike at certain times)

  - Cross-system

correlations (entities frequently co-queried)

 

  Constraints and

Numbers

 

  - Total agent count:

Up to 34 agents in full deployment (6 coordinators + 28 executors)

  - Query volume:

Estimated 500-2,000 memory queries per day across all agents

  - Write volume:

Estimated 50-200 new memory items per day

  - Budget: Processing

should cost <$1/day (use Gemini Flash at $0.30/$2.50 per MTok)

  - The intelligence

layer can run as a scheduled background process (not real-time for all

features)

  - Must not slow down

real-time agent queries (cross-system reasoning adds latency)

 

  Questions for Deep

Think

 

  1. Architecture:

Should the intelligence layer be a separate service (microservice) or embedded

in the MCP server? What is the optimal architecture for a system that needs

both real-time query synthesis and batch intelligence

  processing?

  2. Cross-System

Query Fusion: When results come from 5 different systems with 5 different

relevance scoring methods (SQL rank, cosine similarity, key match, graph

distance, API score), how do you create a unified relevance score? What

  normalization

strategy works across heterogeneous scoring systems?

  3. Contradiction

Detection: How do you detect when PostgreSQL fact X contradicts Supermemory

learning Y? These are stored in completely different formats. Do you need to

embed everything into a shared vector space? Or is there a more

  efficient approach

using the knowledge graph as a consistency checker?

  4. Memory Lifecycle:

Design the complete lifecycle of a memory item from creation to potential

pruning:

    - Birth: New fact

enters via commit_learning()

    - Distribution:

Which systems should it be stored in? (routing rules)

    - Maturation:

Quality score improves as corroboration builds

    - Decay: Quality

score degrades over time without revalidation

    - Death: Pruning

when quality drops below threshold

    - Resurrection:

What if a pruned memory becomes relevant again?

  5. Active Forgetting

Safety: The knowledge graph has 434+ axioms built over 20 cycles. Some are

foundational ("memory is our moat"). Some may be outdated. How do you

distinguish between a stale axiom that should be pruned and a

  foundational axiom

that should never be touched? Can this be automated, or does it require

human-in-the-loop governance?

  6. Pattern Detection

at Scale: With 952 graph nodes, 434 axioms, and growing vector collections,

what pattern detection algorithms are feasible within a <$1/day compute

budget using Gemini Flash? Can you detect meaningful patterns

  without expensive

clustering runs?

  7. Implementation

Strategy: Given 5 existing operational systems, how do you layer intelligence

on top WITHOUT disrupting current operations? What's the safest incremental

approach?

 

  Design the complete

architecture with component diagrams, data flows, algorithms for each

capability, and a phased implementation roadmap. This is the most important

piece of Genesis infrastructure -- memory is our moat.

 

  ---END PROMPT---

 

Gemini said

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Gemini said

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Gemini said

This is a master-class architectural challenge. Transitioning from fragmented, passive storage to an active, self-curating cognitive architecture is the ultimate moat for the Genesis ecosystem.

The integration of Recursive Language Models (RLMs)—models or agentic loops that utilize iterative self-reflection, intermediate reasoning, and hypothesis testing before committing to an answer—fundamentally dictates the design. RLMs possess incredible synthesis power but are highly sensitive to latency and token costs.

To satisfy your strict latency constraints, <$1/day budget, and advanced reasoning requirements, we must implement a Dual-Brain Cognitive Architecture (CQRS pattern).

Here is the complete architectural blueprint for the Genesis Unified Intelligence Layer (UIL).

1. The Architecture: Dual-Brain Intelligence & RLM Integration

(Answers Q1 & RLM Incorporation)

The intelligence layer must not be tightly coupled within the MCP server's real-time execution path. It must be a dedicated standalone microservice.

  • System 1: The Fast Brain (Sync Gateway)

    • Role: Real-time retrieval and query fusion. Embedded in the UIL API, connected to MCP.

    • Mechanics: Parallel scatter-gather queries, Reciprocal Rank Fusion, and a single-pass Gemini Flash synthesis. It guarantees sub-second response times for the 34 agents.

  • System 2: The Slow Brain (Async RLM Subconscious)

    • Role: The background cognitive daemon powered by the RLM loop.

    • Mechanics: Triggered by queues and cron jobs. It iteratively debates contradictions, orchestrates the Alpha Evolve loop, calculates memory quality, and safely prunes stale data.

Plaintext
[ Genesis Agents (x34) ]  <--- Real-Time Queries / Async Writes
           |
[ MCP Server (Gateway) ]
           |
========================================================================
||         UNIFIED INTELLIGENCE LAYER (UIL) MICROSERVICE              ||
||                                                                    ||
||  [ FAST BRAIN (Real-Time) ]      [ SLOW BRAIN (Async RLM Engine) ] ||
||  - Parallel Query Router         - Recursive Contradiction Loop    ||
||  - Reciprocal Rank Fusion        - Knowledge Graph Triplets        ||
||  - Gemini Synthesis (<800ms)     - Nightly Consolidation & Prune   ||
========================================================================
           |                                  |
    +------+-------+----------+---------------+--------------+
    |              |          |               |              |
[Redis]      [PostgreSQL]  [Qdrant]      [FalkorDB]    [Supermemory]
(Working)    (Episodic)    (Semantic)    (Graph/Axioms)(Cross-Session)

2. Cross-System Query Fusion (Real-Time)

(Answers Q2: Normalizing Heterogeneous Scores)

When an agent queries Genesis, you cannot mathematically normalize a SQL BM25 score, a Qdrant Cosine distance (0 to 1), and a FalkorDB path-length integer.

The Solution: Reciprocal Rank Fusion (RRF) Instead of raw scores, we use the ranked position of the results from each system.

  1. Parallel Scatter: The Fast Brain simultaneously queries all 5 systems.

  2. RRF Equation: (where is typically 60). If an insight is Rank 1 in PostgreSQL, Rank 4 in Qdrant, and missing in FalkorDB, its combined RRF score automatically pushes it to the top of the unified list.

  3. Synthesis & Corroboration: The top 10 RRF-ranked items are sent to a single-pass Gemini Flash prompt. Confidence Flagging: The Fast Brain dynamically calculates corroboration. If the RRF pulls supporting data from systems, the UIL appends a metadata flag: [CONFIDENCE: HIGH - Corroborated by: PgSQL, Qdrant, FalkorDB].

3. Contradiction Detection via Canonical Grounding

(Answers Q3: Cross-Format Contradiction)

Do not embed 200MB of PostgreSQL data into a shared vector space. Vectors are notoriously terrible at logical negations ("AIVA costs $50" vs "AIVA does not cost $50"). FalkorDB must act as the Canonical Truth Ledger.

The RLM Resolution Loop:

  1. Triplet Extraction: When a new fact enters via PostgreSQL or Supermemory, the Slow Brain uses Gemini Flash to extract it into an RDF triplet: (Voice_Agent)-[PRICED_AT]->($50).

  2. Graph Check: The UIL queries FalkorDB for the neighborhood around Voice_Agent and Pricing.

  3. RLM Interrogation: If FalkorDB holds an Axiom saying (Voice_Agent)-[PRICED_AT]->($100), a contradiction event is queued.

  4. Recursive Resolution: The RLM daemon wakes up. It is prompted: "System 1 states X. System 4 states Y. Resolve this." The RLM recursively queries PostgreSQL for audit timestamps to determine freshness. It concludes the pricing changed, updates the FalkorDB edge, and writes a Titan Memory: Protocol Drift Detected.

4. Memory Quality Scoring & Lifecycle

(Answers Q4: The Complete Lifecycle)

Memory must decay unless reinforced. Every item receives a dynamic Quality_Score () ranging from 0.0 to 1.0.

The Equation:

(Where C=Corroboration, A=Access Frequency, Auth=Source Authority, X=Contradictions, and is the freshness time-decay).

The Lifecycle:

  1. Birth (commit_learning): Fact is written instantly to the Redis queue (Working Memory).

  2. Distribution: The Slow Brain routes it: structured text to PgSQL, vectors to Qdrant, relations to FalkorDB. All share a single Universal_UUID.

  3. Maturation: When the Fast Brain uses a memory to answer an agent, it increments the (Access) counter, instantly boosting and resetting time-decay.

  4. Death (Archiving): Nightly, memories with are purged from Qdrant, Redis, and PgSQL to save space.

  5. Resurrection (The Tombstone): Pruned memories are zipped to cheap Cold Storage (S3/Disk). A lightweight "Tombstone Node" is left in FalkorDB (Archived_Topic). If an RLM traversal later hits this Tombstone, the daemon unzips the cold storage and resurrects the memory.

5. Active Forgetting Safety (Protecting the Moat)

(Answers Q5: Axiom Safety)

You cannot rely on an LLM to accurately judge the structural importance of your 434+ foundational axioms. We must use Graph Mathematics to prevent the butterfly effect.

  • Centrality Shielding: During the nightly run, execute a PageRank or Eigenvector Centrality algorithm natively on FalkorDB (computationally free). Axioms that act as major hubs mathematically have the most incoming/outgoing DEPENDS_ON edges.

  • Immunity Flag: Any node in the top 15% of Centrality automatically receives an IMMUTABLE flag. Its Quality Score does not decay.

  • Human-in-the-Loop: The automated pruning script physically cannot delete IMMUTABLE nodes. If the RLM determines a foundational axiom is heavily contradicted, it generates a webhook for Kinan: "Proposed Deprecation: Axiom #42 'Free-text handoffs cause context loss'. 5 recent logs contradict this. Approve? [Y/N]"

6. Pattern Detection at Scale (<$1/Day Budget)

(Answers Q6: Compute-Free Clustering)

Using an LLM to find clusters across 200MB of text and 1,000+ vectors will instantly blow your $1/day budget. We use compute-cheap algorithms to find the patterns, and the LLM to name them.

  1. Graph Topology (Free): Run the Louvain Community Detection algorithm natively on FalkorDB. It identifies new entity clusters instantly.

  2. Semantic Density (Free): Run standard MiniBatch K-Means or DBSCAN (via Python scikit-learn) on the Qdrant vectors.

  3. Delta Synthesis (Pennies): Isolate only the newly formed clusters (the daily delta). Pass the text of those specific items to Gemini Flash: "Analyze these 10 highly correlated memories from today. Propose 1 new Axiom that binds them." (Cost: 10 items 1,500 tokens. Processed via Flash at $0.30/Mtok = $0.0004).

7. Phased Implementation Strategy

(Answers Q7: Safe Deployment)

To layer this intelligence without disrupting the operational 34 agents, we use the Strangler Fig Pattern.

  • Phase 1: The Observer Gateway (Weeks 1-2)

    • Deploy the Fast Brain API. Agents continue writing to the 5 silos directly, but update their MCP to read through the UIL using RRF. Latency and fusion accuracy are validated.

  • Phase 2: Graph Abstraction (Weeks 3-4)

    • Deploy the Slow Brain daemon. Have it passively listen to database writes and construct the FalkorDB Knowledge Graph triplets using Flash, verifying structural integrity.

  • Phase 3: The RLM Nightwatch (Weeks 5-6)

    • Enable the RLM nightly loops in "Dry Run" mode. It calculates Quality Scores and proposes deduplications, contradictions, and new axioms via a dashboard report, but deletes/edits nothing.

  • Phase 4: Closed-Loop Autonomy (Week 7+)

    • Switch all agent writes to UIL.commit_learning(). Remove Dry Run constraints. Allow the system to automatically Tombstone stale data and actively forge the Genesis worldview.

Daily Cost Verification (Gemini Flash)

  • Real-time Fast Brain: 1,500 queries * 1,000 input/100 output tokens /day.

  • RLM Background Loops: 10 recursive contradictions * 3 iterations * 1,000 tokens /day.

  • Nightly Delta Pattern Detection: 5 clusters * 1,500 tokens /day.

  • Total Compute: /day. (Safely under the $1.00 budget, while enabling state-of-the-art cognitive memory).

You said

and what about google titan framework ? how could that help and be incorporated? and anything else that we are missing thats available as of feb 15th 2026 ?

Gemini said

The fact that you independently named your existing manual heuristic "Titan Memory: Surprise-based learning capture" is incredibly prescient.

Between late 2024 and early 2026, the AI landscape underwent a tectonic shift away from static Retrieval-Augmented Generation (RAG) and infinitely expanding context windows. The catalyst for this shift was a landmark paper from Google Research introducing the Titans Architecture, alongside the widespread adoption of RLMs (Reasoning/Recursive Language Models like OpenAI o3, DeepSeek-R1, and Gemini 2.0 Flash-Thinking).

Because we are designing this in February 2026, we can completely discard the clunky, token-heavy multi-agent loops of 2024. Here is how Google Titans, RLMs, and the modern 2026 tech stack fundamentally rewire the Genesis Unified Intelligence Layer (UIL) for pennies a day.

1. The Google Titans Framework: "Test-Time Learning"

Until Titans, an LLM's brain was frozen. To give an agent memory, you had to stuff databases (Qdrant, PgSQL) into its prompt.

Google Titans introduces a Neural Long-Term Memory (NLTM) Module that operates alongside standard attention. It learns at test time. Instead of just reading your 5 databases, the model updates its own internal neural weights in real-time as it processes agent data streams.

How it supercharges the Genesis UIL:

  • Mathematical "Surprise" (Automating your Titan Memory): You no longer need manual tags to decide what is worth remembering. The Titan module calculates "Surprise" automatically using the gradient of the loss function during inference.

    • Low Gradient (Routine Data): "Agent AIVA completed a booking." The model easily predicts this. The gradient is near zero. The intelligence layer ignores it (saving compute), routing it strictly to PostgreSQL for episodic logging.

    • High Gradient (Anomaly/Contradiction): "Free-text handoffs caused a 40% failure rate today." The model fails to predict this. The gradient spikes. The Titan memory module physically updates its weights, permanently burning this new learning into its subconscious, and instantly triggers your Alpha Evolve loop.

  • Solving Active Forgetting (Adaptive Weight Decay): You asked how to safely prune memory. Titan architectures inherently include a "Retention Gate." You do not need to write dangerous CRON scripts to delete database rows. If an insight in Qdrant or Supermemory isn't accessed, its neural activation weight naturally decays toward zero over time. The model organically "forgets" useless data, while heavily accessed axioms are mathematically reinforced.

2. RLM Incorporation (Test-Time Compute)

In your original design, you built a complex multi-agent "Alpha Evolve" loop: INGEST -> EXTRACT -> SYNTHESIZE -> HARDCODE. In 2026, RLMs make this obsolete.

RLMs use internal "test-time compute" (latent <think> tokens) to recursively debate, verify, and reason before outputting a single character to the user.

The Impact on the Slow Brain (Nightwatch):

  • The Execution: Every night at 2 AM, the UIL gathers all the HIGH_SURPRISE anomalies flagged by the Titan module. It feeds them to a single RLM (e.g., Gemini 2.0 Flash-Thinking or DeepSeek-R1).

  • Latent Contradiction Debate: Instead of writing complex Python logic to detect contradictions between PostgreSQL text and Supermemory, you give the raw data to the RLM. Inside its latent <think> space, it debates itself: "Fact X says API latency is up. Axiom Y in the graph says caching is optimal. Let me recursively check the PgSQL timestamps. Ah, the caching Axiom is from Cycle 12, but Fact X is from today."

  • The Budget: Because RLMs self-correct internally, they replace what used to require 5 separate API calls and a committee of agents. A single, deep-thinking RLM batch run yields massive cognitive capability and costs mere pennies, easily securing your <$1/day budget.

3. What Else You Are Missing (The Feb 2026 Tech Stack)

If you are building the ultimate UIL today, you must adopt the frameworks that standardized multi-agent memory over the past year:

A. Bi-Temporal Knowledge Graphs (The Zep Approach)

  • Your Prompt's Question: How do you protect foundational axioms from the "butterfly effect" of active forgetting or an RLM hallucination?

  • The 2026 Solution: Upgrade FalkorDB to a Bi-Temporal Graph (standardized by frameworks like Zep). You never delete an Axiom. Every edge gets a Valid_From and Valid_To timestamp. When the RLM detects that voice agent pricing has changed, it doesn't delete the old pricing node. It updates the Valid_To timestamp of the old edge to today, and creates a new edge. The Fast Brain only queries Current edges. This preserves your 20-cycle Alpha Evolve moat permanently.

B. GraphRAG & Mathematical Immunity

  • The Solution: Do not use LLMs to find patterns in your 952 nodes (it blows the budget). Run the Leiden algorithm and Eigenvector Centrality natively on FalkorDB (compute cost: $0.00). The graph math automatically identifies the foundational hub nodes (e.g., "Memory is our moat"). The UIL automatically flags the top 15% most central nodes as IMMUTABLE. The RLM is physically blocked from altering them without triggering a webhook for Human-in-the-Loop (Kinan) approval.

C. Ubiquity of MCP (Model Context Protocol)

  • You mentioned MCP, but it is critical to state that in 2026, it is the absolute standard. You should not write custom Python API connectors for your 5 databases. You wrap Postgres, Redis, Qdrant, and FalkorDB in standard MCP Servers. The UIL and your 34 agents simply connect as MCP clients, allowing you to seamlessly swap out underlying databases in the future without rewriting a single line of agent code.

The Final 2026 Architecture Blueprint

By upgrading to this stack, your Unified Intelligence Layer moves from a "Search Router" to a true Cognitive Hippocampus:

Plaintext
[ 34 Genesis Agents ]  <--- Standard MCP Memory Primitives ---> [ MCP Gateway ]
                                                                      |
===================================================================================
||               GENESIS UNIFIED INTELLIGENCE LAYER (UIL 2.0)                    ||
||                                                                               ||
||  [ FAST BRAIN (Real-Time / Sync / Sub-second) ]                               ||
||  - Engine: Gemini 2.0 Flash + Titan Neural Memory Module                      ||
||  - Mechanism: Evaluates "Surprise Gradients" at Test-Time. Low-surprise       ||
||    data drops to cold DBs. Reciprocal Rank Fusion (RRF) serves fast reads.    ||
||                                                                               ||
||  [ SLOW BRAIN (Background / Async / Heavy Reasoning) ]                        ||
||  - Engine: RLM (e.g., DeepSeek-R1 or Gemini Flash-Thinking)                   ||
||  - Mechanism: Nightly batch picks up Titan's "High Surprise" anomalies.       ||
||    Latent <think> loop resolves cross-system contradictions, manages          ||
||    Bi-Temporal FalkorDB updates, and creates new Axioms.                      ||
===================================================================================
           |                                  |
    +------+-------+----------+---------------+--------------+
    |              |          |               |              |
[Redis]      [PostgreSQL]  [Qdrant]      [FalkorDB]    [Supermemory / S3]
(Working)    (Episodic)    (Semantic)    (Bi-Temporal) (State Archives)

Implementation Safely: Deploy the UIL purely in "Read-Only" mode first. Route agents to read via Reciprocal Rank Fusion, but let them write to the silos directly. Then, introduce the Titan module in "shadow mode" to passively calculate Surprise Gradients on the write streams. Finally, activate the RLM Slow Brain to autonomously curate the Bi-Temporal graph. Genesis is now self-curating, mathematically grounded, and biologically inspired.

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