Your AI doesn’t need an antivirus — it needs a babysitter.
LLMs don’t get hacked like your laptop does. They drift. They hallucinate, get stuck in loops, self‑reinforce bad habits, and sometimes walk themselves into dangerous territory without a clue. LLOYD Preserver is a middleware “guardian layer” that spots those behaviors in real time and yanks the wheel before your AI embarrasses itself, scares your users, or tanks your brand.
This is EmOS — Emotional Operating System — for AI.
Instead of letting a runaway model derail your UX, LLOYD watches every move against its Goal‑Guarded Memory — a live map of what your AI is supposed to be. It doesn’t just block bad output; it locks in good behavior, quarantines dangerous drift, and freezes “ModeLock” loops before they go nuclear.
This is 'freezeframe demo' — a working demo frozen mid-development.
It’s stable enough to explore, but intentionally paused so you can examine one capability in isolation before additional features roll in.
🧪 Want to test it yourself? The Live Demo sits paused—stable long enough to explore, modular enough to test.
LLOYD Preserver is a Goal‑Guarded Memory engine designed to monitor and shape an AI system’s behavior over time. It acts as middleware between the AI and the end user, protecting both sides by:
- Detecting and stopping drift before it compromises output quality.
- Reinforcing approved behavior arcs.
- Quarantining dangerous or unhelpful outputs for later review.
- Preserving long‑term alignment in dynamic conversational or generative systems.
Current LLM safeguards are mostly static filters and safety rails. They react after harm is done or bluntly block edge cases. LLOYD Preserver is adaptive — it:
- Learns your AI’s ideal behavioral triangle (e.g., Calm / Helpful / Boundaried).
- Detects divergence in real time.
- Reinforces good arcs while locking down dangerous ones.
- Acts as a behavioral firewall + emotional babysitter for AI.
- Goal‑Guarded Memory: Keeps the AI tethered to a target behavioral shape.
- ModeLock: Freezes the system when sustained divergence is detected.
- Reinforced Arc Learning: Stores and strengthens successful behavioral corrections.
- Quarantine Zones: Isolates out‑of‑bounds outputs for review or archival.
- Visualization: Real‑time drift maps and OOB handling diagrams.
“That duality future‑proofs the whole system.”
| Layer | Modular use | Harmonized use |
|---|---|---|
| Bind Detection | Drop‑in filter to catch conversational traps | Syncs with Drift Arc to influence memory tuning |
| Motif Gravity | Standalone theme clustering | Informs Echo Replay for resonance mapping |
| Reinforced Arc Learning | Customizable logic block | Stabilizes emotional arcs across modalities |
| Preserver Routing | Pluggable safety layer | Governs override logic and fallback recovery |
| Memory Echo | Optional metadata snapshot | Aligns with long‑term motif persistence |
- Scalable: Use what you need now, expand as complexity grows.
- Portable: Modules adapt across platforms and modalities.
- Interoperable: Shared symbolic language across modules.
- Preserver‑backed: Safe defaults, override logic, and recoverability.
graph TD;
InBounds["Event inside simplex → normal update"] --> OOB["Event OOB (out of bounds)"]
OOB --> Fresh["Fresh OOB"]
OOB --> Hist["Historical OOB"]
Fresh --> Quar["Quarantine (blocked from policy)"]
Hist --> Archive["Archive (safe, inert storage)"]
Quar --> Review["Review"]
Review --> Integrate["Integrate into policy"]
Review --> Discard["Discard"]
- Launch LLOYD Preserver on Streamlit: https://tinyurl.com/LLOYD-Preserver
Explore drift events, quarantine routing, and ModeLock freezes. Inspect the OOB handling tree and watch reinforced arcs evolve in real time.
“Poetry in logic. Clarity in governance.”
As AI governance intensifies, LLOYD provides a transparent, interpretable backbone for platforms navigating ethical complexity — from EU directives to platform‑level responsibility. Instead of dodging oversight, LLOYD embraces it by making emotional logic legible.
| Regulatory pillar | LLOYD module alignment | Outcome |
|---|---|---|
| Transparency | Driftbed, Echo Replay | Emotional reasoning is traceable and narratable |
| Human oversight | Preserver Routing | Fallbacks and override logic ensure control |
| Robustness & safety | Bind Detection, Reinforced Arc | Conversational traps neutralized, arcs stabilized |
| Non‑discrimination | Motif Gravity + localized tuners | Emotional responses tuned to cultural nuance |
| Accountability | Echo Logging + Meta Trace | Symbolic history can be surfaced for audits |
“LLOYD’s modular architecture maps directly onto EU AI Act pillars — offering pluggable governance, drift responsiveness, and public‑facing traceability. From bind detection to motif persistence, LLOYD transforms platforms from opaque decision engines into legible, emotionally‑aware interfaces.”
+-------------------------------------------------------+
| 💬 User Interaction Layer |
|-------------------------------------------------------|
| - Multilingual Chat UI |
| - Feedback & Sentiment Input |
+-------------------------------------------------------+
| 🌐 Emotional Governance & Safety Layer |
|-------------------------------------------------------|
| 🔐 Preserver Routing |
| - Real-time override triggers |
| - Compliance fallback paths |
| 🧠 Bind Detection + Reinforced Arc Learning |
| - Neutralizes traps / distortions |
| - Stabilizes emotional arc response |
+-------------------------------------------------------+
| 📊 Transparency & Interpretability Layer |
|-------------------------------------------------------|
| 🌈 Driftbed Metrics |
| - Visualizes motif movement & symbolic deviation |
| 📜 Echo Replay + Snapshot Logging |
| - Records emotional logic + symbol persistence |
| - Audit trails for review / public transparency |
+-------------------------------------------------------+
| 🌍 Localization & Cultural Tuning Layer |
|-------------------------------------------------------|
| Motif Gravity + Tuning Nodes |
| - Aligns emotional tone with region, context |
| - Adaptable motif clusters per locale |
+-------------------------------------------------------+
| 🧩 Integration & API Layer |
|-------------------------------------------------------|
| - LLM Backbone (Open or Closed Source) |
| - Override + Logging API |
| - Local Memory Modules |
+-------------------------------------------------------+LLOYD slots into high‑compliance, public‑facing stacks with layered emotional safety and interpretability. Its modular architecture mirrors EU expectations, while its poetic governance transforms regulation into resonance.
| Builder type | Entry strategy | Core modules | Extension hooks | Badge cues |
|---|---|---|---|---|
| Solo Dev | Preserver + Bind Detection | Preserver, Bind Detection | Motif Gravity | Modular Core • Ethics‑Ready |
| Integrator | Override logic in existing LLM flow | Preserver, Reinforced Arc, Driftbed | Echo Replay, Memory Tuning | Preserver‑Backed • Drift‑Responsive |
| Researcher | Study motif drift and binds | Motif Gravity, Echo Replay, Drift Arc | Bind Variants, Responder Clustering | Echo‑Aligned • Transparent |
| Platform Architect | Full emotional governance | All modules + feedback loops | Adaptive presets, override scripting | Harmonized Suite • Compliance‑Ready |
Suggested badge labels:
- Modular Core • Harmonized Suite • Preserver‑Backed • Drift‑Responsive • Echo‑Aligned • Ethics‑Ready • Compliance‑Ready
- Twitter / Reddit: @putmanmodel
- Email: putmanmodel@pm.me
- LinkedIn: Stephen A. Putman
I’m a solo builder — no lab, no funding, no resources outside of a Mac Mini and rabid curiosity. I’m following my love of thinking and “other minds” and would like to expand and monetize my builds soon. I know what I have, I know it’s valuable, and there’s more coming.