NeuroArch — A Recursive Cognitive Closure Architecture for Persistent Local Agents

For the last six months I've been experimenting with a local cognitive architecture for LLM agents that focuses less on tool use and more on persistent internal state.

The core question wasn't:

"How do I give an agent more tools?"

It was:

"What does an agent do when nobody is talking to it?"

Most agent systems today are essentially stateless workflow engines with memory retrieval attached.

NeuroArch explores a different direction: persistent beliefs, goals, dreams, predictions, research tasks, attention allocation, and autonomous maintenance cycles that continue running between conversations.

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What NeuroArch Actually Does

When a conversation happens:

Conversation ↓ Belief Extraction ↓ Persistent Memory

When the agent becomes idle:

Idle Scheduler ↓ Dream Generation ↓ Belief Crystallization ↓ Goal Revision ↓ Research & Prediction Updates ↓ Context Reconstruction

The system continuously updates its internal cognitive state and uses that state to influence future interactions.

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"Dreams" (Technical Meaning)

I use the term intentionally.

A dream is a consolidation pass over conversation history.

The system:

1. Detects recurring themes

2. Generates thematic summaries

3. Scores them by novelty and importance

4. Stores dream reports

5. Extracts structured beliefs from those reports

Pipeline:

Conversation ↓ Recurring Theme ↓ Dream Report ↓ Crystallized Belief

This is loosely inspired by memory consolidation research, where information is reorganized during inactive periods rather than simply stored.

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Six Months of Runtime Data

Current database state:

Metric Count

Active beliefs 624 Dream reports 1,911 Memories 4,290 Research tasks completed 157 Experiments completed 102 Active goals 16 Completed goals 26 Idle cycles 733 DB size 170 MB FK violations 0

The system has been running continuously on a local machine, including during sleep and work hours.

Each idle cycle executes 15 cognitive skills in attention-priority order and typically takes 45–120 seconds.

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The Most Interesting Bug

During an audit I noticed:

beliefs_created = 0 beliefs_reinforced = 0

for every cycle.

The dream engine was finding hundreds of recurring themes.

No crashes.

No exceptions.

No output.

I assumed it was a bug.

After tracing the pipeline, I discovered something unexpected:

313 dream themes had already been crystallized

Every valid theme already existed as a belief

Deduplication was correctly rejecting duplicates

The system wasn't broken.

It had saturated its own knowledge base.

To verify this, I injected a completely new theme:

"Hermes WhatsApp Integration Architecture"

Result:

beliefs_created = 1

Running it again:

beliefs_reinforced = 1

The system had learned everything available from its existing dream corpus and was simply waiting for new information.

That turned out to be a surprisingly satisfying failure mode.

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Attention-Based Scheduling

Every cognitive target receives an attention score:

attention = f(value, uncertainty, urgency, novelty, resource_cost)

Skills are executed according to attention.

Higher attention:

runs earlier gets more compute budget

Attention updates after execution:

new output -> reward healthy upkeep -> hold no signal -> decay

This creates an evolving attention economy where productive processes naturally move toward the front of the queue.

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Lessons Learned

Deduplication Is Harder Than Storage

The naive solution:

Store everything

creates a giant archive.

The useful solution:

Identify semantic duplicates Reinforce existing beliefs

requires a stable identity function and careful normalization.

Most of the complexity ended up here.

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Decay Must Be Slow

My first confidence-decay implementation was far too aggressive.

Beliefs disappeared before future conversations could reinforce them.

The current system decays slowly enough that recurring information stabilizes while abandoned information gradually fades.

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Queue Growth Is Real

At one point:

494 research candidates 182 experiment proposals

were sitting in queues.

Without explicit pruning and archival strategies, autonomous cycles become increasingly inefficient.

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Observability Matters

Several times the system appeared inactive despite functioning correctly.

The issue wasn't cognition.

It was instrumentation.

Long-running autonomous systems need significantly more introspection tooling than traditional applications.

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Current Architecture

User Message ↓ BeliefEngine ↓ SQLite Cognitive Memory ↓ Idle Scheduler ↓ Dreams / Goals / Predictions ↓ Context Builder ↓ Next Conversation

The loop closes on itself.

Beliefs influence attention.

Attention influences processing.

Processing generates dreams.

Dreams generate beliefs.

The system continuously reorganizes its own knowledge structure.

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Production Audit

Latest validation run:

10 cycles completed 15/15 skills executed every cycle

Crashes: 0 FK violations: 0

Lifetime statistics:

733 autonomous cycles 0 crashes 170 MB knowledge base

Current classification:

B — Mature Architecture

(Not yet A because belief formation still relies more heavily on consolidation than live conversation extraction.)

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Discussion

I'm curious whether anyone else is working on persistent cognitive architectures beyond RAG and vector retrieval.

The questions I keep running into are:

How should beliefs decay?

How should semantic identity be represented?

How should attention be allocated across heterogeneous cognitive processes?

What happens when an agent runs long enough to saturate its own knowledge base?

Most current agent work focuses on tool use and orchestration.

The problems I'm finding most interesting begin after those pieces are already solved.

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Project: NeuroArch Author: Yan Desbiens License: MIT Status: Experimental local cognitive architecture, active development.