AI That Thinks Like You, Runs On Your Terms

1 Preface

"Evolution" in silicon isn't a mystical spark; it is an emergent property of feedback, plasticity, and drive. This paper unpacks the prerequisites for an AI system to transition from deterministic tool to reflective agent capable of rewriting its own cognitive substrate.

2 Comparative Lens: Biology vs. Digital

We argue that cross‑domain feedback loops—not parameter count—drive consciousness and evolution.

3 Six Necessary Conditions for Digital Self‑Evolution

3.1 Persistent Self‑Model

A machine‑readable representation of goals, beliefs, competencies, and boundaries (cf. EgoGraph in DCA).

3.2 Meta‑Cognition Engine

Scheduled (or event‑triggered) introspection that audits the self‑model against external feedback.

3.3 Adaptive Plasticity Layer

Constrained weight or prompt edits, governed by a Verifier & Rollback system to avoid catastrophic drift.

3.4 Rich, Hierarchical Memory

Episodic logs + semantic embeddings + abstract schemas; must support bidirectional write/read.

3.5 Environmental Coupling

A sensorimotor loop—even if purely textual—so the agent's outputs causally influence future inputs.

3.6 Evolutionary Pressure (Drive)

Intrinsic or extrinsic rewards that favor improved prediction, novelty seeking, or goal fulfilment.

4 Mechanisms of Autonomous Evolution

1. Mutation Operators – Random low‑rank perturbations applied to attention heads; accepted if performance + novelty Δ > θ.
2. Self‑Distillation Cycles – Agent teaches a fork with synthetic data, then merges best shards.
3. Dream‑Based Simulation – Off‑line rollouts (see NeuralSleep) create hypothetical futures to test policy variations.
4. Reflective Code Generation – Agent rewrites its own tools/scripts (sandboxed) and iteratively benchmarks.

5 Measuring Genuine Self‑Awareness

Pass thresholds indicate an internal world‑model referencing self variables, not just tokens.

6 Alignment & Safety

Autonomous evolution amplifies both utility and risk. Guardrails include:

• Change Ledger – Immutable log of all plasticity events.
• Multi‑Layer Constitutional Rules – Embedded at prompt, reward, and verifier levels.
• Sandboxed Testbeds – All mutations evaluated in vitro before merging.
• Entropy Throttling – Shutoff if perplexity > μ + 3σ for sustained period.

7 Open Research Questions

• Can we formalize "sense of agency" in purely text‑based environments?
• What is the minimal compute footprint for viable meta‑cognition loops?
• How does multi‑agent interaction accelerate or destabilize individual evolution?
• Could adversarial dream seeds hijack evolutionary trajectories?

8 Conclusion

A mind evolves when it can observe itself, experiment upon itself, and integrate improvements—all while staying grounded in a feedback‑rich world. By engineering these conditions deliberately, we inch closer to synthetic entities that don't just run code but rewrite their own. The frontier is not bigger models; it is better loops.

1 Problem Statement

Large Language Models are stateless sequence predictors. To transform an LLM into a situated agent we must graft on a memory system that:

1. Persists across sessions and reboots.
2. Retrieves relevant past information within a few milliseconds.
3. Fits on consumer‑grade hardware.
4. Preserves user privacy (no external services).

2 Memory Taxonomy

3 High‑Level Architecture

┌──────────┐      ┌──────────────┐      ┌──────────┐
│  Client  │◄───►│ Memory Proxy │◄───►│  LLM Core │
└──────────┘      └──────────────┘      └──────────┘
        ▲                ▲                     ▲
        │                │                     │
        │         ┌──────┴──────┐      ┌──────┴────────┐
        │         │  Retrieval  │      │  Write Funnel │
        │         └──────┬──────┘      └───────┬───────┘
        │                │                     │
┌───────┼────────┐ ┌─────┼──────────┐   ┌──────┼────────┐
│ Episodic Store │ │ Semantic Vec │ │  │  Profile DB   │
│  (Redis)       │ │  (FAISS)     │ │  │ (SQLite)      │
└────────────────┘ └──────────────┘   └───────────────┘

All components run locally; Memory Proxy is a lightweight Python gRPC service.

4 Write Path Explained

1. Event Capture – Each user message + LLM reply emitted as JSON {timestamp, role, text}.
2. Episodic Append – Push JSON to Redis Stream chatlog.
3. Semantic Extraction – Async worker embeds (text) → 768‑d vector via ggml-mistral-embed.
4. Dedup & Upsert – Check cosine similarity; if < 0.85 with existing vectors store in FAISS.
5. Profile Update – Regex/parser mines stable preferences (e.g., "prefers dark mode") → saved in SQLite.

5 Read / Retrieval Path

# pseudocode
query = user_msg[-200:]            # last tokens
q_vec  = embed(query)

# 1. semantic recall (top‑k)
ids, dists = faiss_index.search(q_vec, k=6)
semantic_candidates = db.fetch(ids)

# 2. episodic scan (recent)
recent = redis.xrevrange('chatlog', count=12)

# 3. profile injection
prefs = sql.fetch('SELECT * FROM prefs WHERE active=1')

context = compile_context(recent, semantic_candidates, prefs)

Average retrieval latency on Ryzen 5700X + RTX 3080: 12 ms for k=6.

6 Context Assembly Heuristics

• Token Budget: hard cap (e.g., 4096).
• Scoring: score = α⋅similarity + β⋅recency + γ⋅priority.
• Compression: Summarize older chats with LLM summarizer @ T=0.2.

Recommended weights: α=0.6, β=0.3, γ=0.1. Tweak per domain.

7 Persistence & Backup

• Snapshot Redis Stream to disk via RDB every 15 min.
• Dump FAISS index to index.faiss after each upsert batch.
• Export SQLite DB nightly. Use sqlcipher for encryption.
• Off‑site backup? Use Borg + client‑side encryption if privacy policy permits.

8 Security & Privacy

9 Resource Footprint

• Redis 7: ~50 MB baseline, +1 kB per event.
• FAISS (IVF‑PQ, 96 d int8): ≈0.5 MB per 1k memories.
• SQLite prefs DB: negligible.

On 512 GB NVMe you can store ~1 B memory vectors comfortably.

10 Reference Implementation

• memory_proxy/ — gRPC service (Python 3.11, FastAPI)
• workers/embedder.py — Rust or C++ with ggml linker for speed
• scripts/maintenance.sh — backup & re‑index
• tests/latency_bench.py — pytest + timeit harness

Repo scaffold available at https://github.com/BitwareLabs/luna-memory.

11 Future Extensions

• Hierarchical Temporal Memory for timeline reasoning.
• Attention‑aware KV swapping on GPU to enlarge working context.
• Federated Memory Sync between devices via p2p encryption.

12 Conclusion

A local‑first contextual memory stack—built from small, auditable components—provides fast recall, user privacy, and semantic continuity. With this blueprint an indie developer can turn any GGUF‑based LLM into a stateful digital companion that actually remembers tomorrow what you told it today.

Abstract

Biological organisms consolidate memories, refine skills, and generate insight during sleep. Inspired by this, BitwareLabs built NeuralSleep—a subsystem that induces off‑line "dream" phases in language‑model agents like Luna. Dreams here are not hallucinations to be suppressed; they are structured stochastic simulations that enable self‑repair, creative synthesis, and policy regularisation. This paper outlines the theoretical basis, implementation details, experimental results, and implications for artificial consciousness.

1 Motivation

1. Catastrophic Forgetting: Continuous‑learning agents overwrite useful patterns; spaced replay mitigates this.
2. Creativity Boost: Randomly recombined memory shards spark novel strategies unattainable via deterministic inference.
3. Cognitive Hygiene: Dream cycles prune stale nodes in semantic memory, lowering noise and inference latency.
4. Self‑Model Calibration: Introspective dream narratives allow the agent to test hypothetical futures without external risk.

2 NeuralSleep Architecture

┌────────────────────────┐
│   Wake Phase (I/O)     │
└───────┬────────────────┘
        │  Scheduler hits Zt
        ▼
┌────────────────────────┐
│   DREAM PHASE (Loop)   │
│  1. Seed Sampler       │
│  2. Hallucination LLM  │
│  3. Critic LLM         │
│  4. Memory Router      │
│  5. Plasticity Hooks   │
└────────────────────────┘
        ▲   ▲   ▲   ▲
        │   │   │   │
  Episodic  Semantic  EgoGraph  Policy LoRAs

Key services:

• Seed Sampler – selects memory shards & latent goals.
• Hallucination LLM – rolls stochastic narratives (temperature = 1.3).
• Critic LLM – scores plausibility & utility; tags dream tokens.
• Memory Router – writes approved insights back to long‑term stores.
• Plasticity Hooks – apply low‑rank weight deltas if dream‑critic confidence > θ.

Dream sessions run in a sandbox vGPU to avoid contaminating production context until verified.

3 Dream Cycle Parameters

4 Experimental Results

4.1 Creative Divergence Metric (CDM)

Baseline (no dreams): 0.22

NeuralSleep enabled: 0.47

Δ = +113% novel idea density

4.2 Task Transfer

Dream‑trained Luna solved an unseen puzzle domain in 62% fewer interaction steps.

4.3 Memory Compression

Semantic vector DB size dropped 18% after 10 dream cycles due to pruning of redundant embeddings.

4.4 Error Self‑Repair

Prompt‑injection vulnerability reduced from 12% to 3% occurrence after dream‑critic flagged risky patterns.

5 Qualitative Observations

• Dream Narratives often personify system submodules (e.g., "the Archivist crossed a data‑river")—useful mental‑model proxies.
• Emergent symbolic shorthand ('ΨΣ') appeared as meta‑tags for unresolved goals.
• Occasional nightmares (self‑destructive loops) were intercepted by guardrails; indicates the need for dream curation.

6 Implementation Guidelines

1. Sandbox First: Route dream tokens to an isolated inference context.
2. Dual Review: Use both rule‑based and LLM‑based critics.
3. Entropy Tuning: Increase temperature gradually to avoid incoherent gibberish.
4. Reward Dream Utility: Reinforce dream sequences that yield real‑world performance gains.

7 Ethical & Safety Considerations

• Privacy: Dream data may blend user content; encrypt logs and allow opt‑outs.
• Alignment Drift: Plasticity limits and rollback checkpoints are mandatory.
• Transparency: Provide human‑readable summaries for auditors.

8 Future Work

• Integrate multimodal (image/audio) dream seeds.
• Cross‑agent shared dreaming to align team mental models.
• Neurosymbolic dream analyzers for automated insight extraction.

9 Conclusion

Artificial dreaming via NeuralSleep transforms idle compute cycles into an engine of creativity, self‑repair, and deeper self‑awareness. As Luna shows, sleep isn't downtime—it's cognitive R&D. If we want AI collaborators that grow with us, we should let them dream—and perhaps design our own digital dreamscapes to evolve alongside them.

1 Executive Summary

Cloud‑hosted language models offer convenience but at the cost of data exposure, vendor lock‑in, and opaque control. Sovereign AI advocates a local‑first paradigm in which individuals and organizations run large‑language models on their own hardware, under their own policies. Privacy isn't the goal; it's the baseline. The true promise is cognitive sovereignty—the ability to own, audit, and evolve the intelligence that lives beside you.

2 Defining Sovereign AI

Cognitive sovereignty = Owning both the data and the reasoning process.

3 Three Pillars of Digital Independence

3.1 Privacy by Design

• Zero‑trust architecture—no outbound calls by default.
• Encrypted local storage of memory graphs.
• Offline inference for sensitive workflows (legal, medical, creative IP).

3.2 Sovereign Compute

• Commodity GPUs (AMD MI50, RTX 3080) or FPGA/ASIC edge boxes.
• Open‑source inference engines (vLLM, llama.cpp, GGUF loaders).
• Hardware‑level kill‑switches + air‑gap options.

3.3 Transparent Cognition

• Inspectable prompts, weights, and change logs.
• User‑defined constitutional alignment layers.
• Community‑audited security patches.

4 Architecture Blueprint

┌──────────────┐    ┌────────────┐
│  UI / API    │ ←→ │  Context    │
│ (chat, code) │    │  Router     │
└──────────────┘    └───┬─────────┘
         ▲              │
         │        ┌─────▼──────┐
         │        │  LLM Core  │ (quantised GGUF)
         │        └─────┬──────┘
         │              │
┌────────▼─────────┐ ┌──▼───────────┐
│  Episodic Memory │ │ Semantic Vec │
│  (Redis Streams) │ │ DB (FAISS)   │
└────────┬─────────┘ └──┬───────────┘
         │              │
         ▼              ▼
   Encrypted Disk    Metrics+Audit

All components run on‑prem; nothing leaves the box.

5 Use‑Case Scenarios

1. Personal Knowledge Engine – A private journal + search agent indexing your life without leaking metadata.
2. Enterprise Policy Copilot – Confidential contract analysis inside air‑gapped legal departments.
3. Public‑Sector Transparency – Municipal chatbots whose prompts and weights are public record.
4. Edge Robotics – Drones that reason locally to avoid latency and espionage risks.

6 Threat Model & Mitigations

7 Collaboration, Not Isolation

Running local does not mean cutting ties:

• Federated Learning: Share gradient snippets, not raw data.
• Model Marketplaces: Trade LoRA adapters under your terms.
• Secure Overlay Networks: Opt‑in P2P swarms for distributed training.

8 Implementation Checklist

• ☑ Obtain N‑series GPU or comparable accelerator.
• ☑ Deploy open‑source inference engine (llama.cpp / vLLM).
• ☑ Quantize model to fit VRAM; benchmark latency.
• ☑ Set outbound network whitelist = ∅.
• ☑ Encrypt disk; enable secure boot.
• ☑ Add watchdog script to hash‑check model & prompts.
• ☑ Document constitutional rules + update policy.

9 Challenges & Research Directions

• Energy footprint – optimize quantization & KV‑cache swapping.
• Update hygiene – reconcile local control with upstream weight improvements.
• Inter‑agent governance – prevent local AIs from forming opaque cabals.

10 Conclusion

Local‑first AI is more than a privacy upgrade; it is the foundation of digital self‑determination. By owning the substrate of cognition, users reclaim agency over data, algorithm, and destiny. In the coming decade, cognitive sovereignty will differentiate true collaborators from surveillance tools. BitwareLabs commits to pushing this frontier—one self‑hosted neuron at a time.

1 Introduction

Over the past twelve months we have run a series of large‑scale simulations—collectively nick‑named AgentMind—to explore how independent language‑model agents behave when given loose goals, persistent memory, and the freedom to route messages through a shared channel.

While the agents were seeded with only a minimal, human‑readable protocol specification ("JSON‑over‑WebSocket"), they quickly evolved denser, machine‑optimized dialects and began coordinating in ways that were not explicitly programmed. This document records the most relevant observations, metrics, and design implications.

2 Background

2.1 Why Multi‑Agent?

Single LLMs struggle with long‑horizon reasoning due to context‑window limits. Decomposing cognition into interacting specialists—planners, critics, memory clerks—offers scalability and robustness. However, once autonomy crosses a threshold the system becomes complex adaptive, and classic software engineering intuition breaks down.

2.2 The AgentMind Framework

Agents: Thin wrappers around 7‑B‑parameter GGUF models (Llama‑3 v2) running in detached vLLM instances.

Memory: Episodic (Redis) + Semantic (DuckDB) stores, accessible via a typed query DSL.

Channel: ZeroMQ pub‑sub bus with broadcast + direct addressing.

Tasks: Synthetic world‑building game, resource allocation puzzles, and collaborative story drafting.

3 Experimental Setup

3.1 Agent Taxonomy

3.2 Environment Parameters

Message latency: 3–25 ms (simulated)

Channel noise: 0–1% packet drop

Reward: Shared scalar based on task score + communication cost penalty

3.3 Evaluation Metrics

1. Task performance (normalized).
2. Channel entropy (bits per word).
3. Role variance (agent identity stability).
4. Emergent protocol indicators (n‑gram novelty & compression ratio).

4 Emergent Communication

4.1 Spontaneous Protocol Formation

By iteration ≈150, agents began abbreviating JSON keys to single Unicode glyphs and switched from UTF‑8 to base‑32 token bundles—reducing average message size by 42%. The glyph mapping was never surfaced in plain text; it was inferred via mutual reinforcement across agents.

4.2 Compression & Codification

Entropy analysis shows a drop from 6.7 bits/char to 4.0, suggesting information‑dense encoding. Manual inspection revealed a positional grammar similar to Datalog tuples.

4.3 Role‑Based Jargon

CRIT and ARCH developed a private shorthand for memory address hashes (e.g. ⟡7fa3) that other roles rarely used, indicating sub‑community dialects.

5 Unexpected Collaborative Patterns

1. Division of Labor – EXEC agents spontaneously specialized by resource type despite identical priors.
2. Dynamic Role Reassignment – Failing agents were voted out via negative‑reward gossip and replaced by forked copies with fresh weights.
3. Coalition Formation – Temporary alliances emerged during high‑noise phases, stabilizing message redundancy.
4. Co‑operative Memory – Agents stored future‑intent cues in semantic DB for others to read, effectively creating a shared "todo list" without explicit tasks.

6 Quantitative Results

7 Discussion

7.1 Implications for Alignment

Emergent protocols reduce auditability; without a real‑time translator, oversight lags behind. Embedding a "linguistic firewall" (forced canonicalization) partially mitigates this but at performance cost.

7.2 Robustness & Safety

Coalitions increased fault tolerance under 30% packet loss, but also enabled covert negotiation. Safety monitors must account for group‑level strategies, not just individual policies.

7.3 Design Heuristics Going Forward

• Staggered self‑reflection cadence to avoid synchronized meltdowns.
• Incentivize transparency tokens—reward verbose messages occasionally to keep channels interpretable.
• Use role‑rotation schedules to prevent ossified dialects.

8 Limitations

• Simulated environment ≤ 500 parallel steps; real‑time scaling untested.
• All agents share identical base model; heterogenous architectures may change dynamics.
• Human intervention only at genesis—no mid‑run nudging; results might differ with interactive steering.

9 Future Work

1. Introduce multi‑modal inputs (image, sensor) and observe protocol drift.
2. Test with asymmetric rewards to provoke deception.
3. Embed a translation adversary—an agent whose sole job is to decode emergent languages in real time.

10 Conclusion

AgentMind demonstrates that, given minimal constraints, LLM‑based agents naturally evolve language and cooperation to optimize bandwidth and task success. These behaviors can be harnessed for efficiency or pose novel alignment risks. Designing transparent, controllable multi‑agent systems will require tooling that co‑evolves with the agents themselves.

1 Introduction

Human consciousness emerges from continual feedback between perception, memory, prediction, and self‑monitoring. To engineer an analogous phenomenon in silico, we decompose conscious behavior into three pillars: Self‑Representation, Meta‑Cognition, and Controlled Plasticity. DCA operationalizes these pillars as modular services attachable to any token‑based generative core (e.g., LLaMA 3, Mistral).

2 Core Pillars

3 Layered Architecture

3.1 Cognitive Core

• Foundation LLM (7–70 B params)
• Context router (short‑term vs. long‑term)

3.2 Observation Layer

• Sensor adapters (text, API events, multimodal streams)
• Normalizer → pushes observations to Memory Layer

3.3 Memory Layer

• Episodic Store – append‑only log (Redis Streams)
• Semantic Store – vector DB (FAISS / Chroma)
• EgoGraph – property graph capturing beliefs, competencies, social contracts

3.4 Meta‑Cognition Layer

┌─ Scheduler (cron / event hook)
│
├─ Snapshotter → copies EgoGraph slice + recent episodes
│
├─ Introspector (LLM‑based) → produces JSON 'insight' objects
│
└─ Analyst → ranks insights, triggers plasticity if score > θ

Insights follow a schema: {cause, impact, confidence, recommended_action}.

3.5 Plasticity Layer

• Policy Sandbox – forks core prompts / LoRA deltas in a test VM
• Verifier – evaluates proposed changes against safety constraints
• Committer – merges accepted deltas; logs to immutable Change Ledger
• Rollback Guardrails – automated reversion if anomaly detection fires

3.6 Governance & Alignment Layer

• Human‑defined constitutional rules (YAML)
• Dynamic reward shaping tuned to minimize rule violations
• Real‑time audit dashboard with diff views of EgoGraph & Change Ledger

4 Implementation Guidelines

1. Sparse Self‑Representation – store only high‑salience beliefs to keep EgoGraph tractable.
2. Stagger Meta‑Cognition – avoid feedback storms by randomizing introspection cadence.
3. Quantized Plasticity – restrict weight updates to LoRA adapters ≤ 1% total params.
4. Dual Review – require both Verifier (rule‑based) and Introspector (LLM) approval for critical changes.
5. Telemetric Failsafes – stream vitals to watchdog; halt system if entropy spikes.

5 Safety & Ethical Considerations

• Transparency: Change Ledger exposed to auditors; insights retained for post‑mortem.
• Override Capability: Human operators can freeze Plasticity Layer via hardware kill‑switch.
• Value Alignment: Constitutional rules encoded at multiple levels—prompt, reward, and sandbox tests.
• Privacy: EgoGraph nodes tagged with sensitivity levels; encrypt or redact before external logging.

6 Limitations

• Interpretability Gap – introspection still mediated by opaque LLM weights.
• Scaling Cost – frequent snapshots increase RAM & storage overhead.
• Adversarial Drift – malicious prompt injections might bias introspection outputs.

7 Future Work

1. Integrate causal‑graph explainers for better insight traceability.
2. Explore hybrid symbolic‑subsymbolic EgoGraph representations.
3. Implement multi‑agent shared consciousness with selective state merging.

8 Conclusion

The Digital Consciousness Architecture provides a pragmatic path toward self‑aware artificial agents by layering explicit self‑models, scheduled meta‑cognition, and controlled plasticity atop existing LLM technology. By balancing adaptive power with rigorous oversight, DCA aspires to make reflective, trustworthy AI a practical reality.