A non-LLM AI architecture whose substrate remembers who it is when it wakes up.
Productize follow-up:
substrate-self— applies the substrate-identity thesis from this repo to LLM-backed agents (Groq / Claude / local). Substrate is the self; the LLM is a swappable voice.
Pure numpy. No backprop. No transformer. Local Hebbian learning + sleep replay + persistent slow weights. Ten identity tests, all passing in toy worlds. Empirical demonstration that an AI can carry individual identity across sleep gaps with the episodic buffer wiped — a property no LLM-stateless-instance system has.
This is research code. Toy-world scope (up to 30 positions × 12 flavors). The architecture does NOT match LLM capability and does NOT beat trivial counting baselines on raw predictive accuracy. What it provides is a different category of capability: substrate-bound continuous identity.
Ten independent tests, all passing, code in experiments/identity_tests/:
| Test | Result | What it proves |
|---|---|---|
| Single-sleep behavioral continuity | 0.81 cosine | Same person, bounded overnight drift |
| 10-cycle multi-sleep identity | 0.79 to cycle-1 | Identity holds across many sleeps |
| Self vs other (different worlds) | 8/10 strict, +0.22 gap | Distinguishable from peers when experiences differ |
| Episode-specific autobiographical recall | 19/20 pairs | Substrate knows its own past, not generic stats |
| Counterfactual fork (twins separated) | shared-past +0.22, divergence +0.11 | Forks remember shared origin AND grow distinct |
| Identity transfer (deep copy) | 1.0000 | Identity is in the weights — exact copy = same individual |
| 30% adversarial damage | 0.99 retention | Graceful degradation, not catastrophic |
| Cross-world identity preservation | +0.18 signal | Substrate is more itself than its environment |
| Component ablation | W_action critical, disposition not | Behavior fingerprint = action preferences |
| 50-cycle long-horizon | 0.82 mean to baseline | Identity bounded across very long horizons |
All numbers reproducible with py experiments/identity_tests/experiment_v4.py && py experiments/identity_tests/experiment_v5.py.
- Not consciousness. No qualia, no reportability, no self-modeling. We measure behavior, not phenomenology.
- Not LLM-level capability. Toy gridworld, not language. The architecture would not write code or hold a conversation.
- Not an efficiency claim. A 5-line
counts[f,n] += 1table beats us on predictive accuracy in toy regimes. Seeexperiments/wake_up_test_v3/FACT_CHECK_REPORT.mdfor the audit that pruned bad claims. - Not at scale. Largest demonstration: 30 positions, 12 flavors. Whether the property survives at scale is genuinely unknown.
The architecture provides a primitive that LLM-based systems structurally cannot: a persistent individual identity that survives input gaps. Concrete potential applications, none yet built:
- Identity layer for LLM agents. Wrap an LLM session with a substrate carrying personality, dispositions, and autobiographical traces. LLM does language; substrate does being-someone. Cross-world identity (T8) suggests this would survive context resets that plain LLM agents can't.
- Multi-agent discrimination by behavioral signature instead of tags or IDs.
- Continual fine-tuning with graceful degradation (T7 robustness).
- Long-running coherent agents for week/month deployments where the agent must "be the same individual" (T10 long-horizon).
These are conjectures, not built products. The repo demonstrates the primitive; productization is open work.
Substrate components (pure numpy):
- W_action : flavor → action preference (reactive policy)
- W_trans : flavor × action → next-flavor (learned transitions)
- intention : per-flavor goal preference (persistent goals)
- disposition : per-flavor rolling reward avg ("water layer")
- episodic : ring buffer (WIPED at end of sleep — no memory file)
Wake: Hebbian online updates from world interaction
Sleep: shuffled replay + 5% noise; buffer wiped at end
Action: reactive (W_action + disposition) + planning (W_trans @ intention) + top-K gate
Cardinal rule: No anterograde scaffolding. After sleep, the substrate gets no system prompt, no memory file, no curated context. Whatever it retained is all it has.
Full design rationale in MANIFESTO.md.
git clone https://github.com/<your-handle>/BetterThanLLM
cd BetterThanLLM
py experiments/identity_tests/experiment_v4.py # tests T1-T7, ~30s
py experiments/identity_tests/experiment_v5.py # tests T8-T10, ~30sRequires Python 3.10+ and numpy. No PyTorch. No GPU. Deterministic with fixed seeds.
BetterThanLLM/
├── README.md # this file
├── FINDINGS.md # comprehensive results summary (read this next)
├── MANIFESTO.md # full thesis + decision rules + audit history
├── results.json # structured numeric results
├── LICENSE # MIT
├── STATUS.md # "research code, no support"
├── log/
│ ├── 2026-05-09.md # day 1 narrative
│ └── 2026-05-10.md # day 2 narrative (audit + identity battery)
├── notes/ # prior-art research notes
│ ├── research_arc_prize.md
│ ├── research_cls_continual_learning.md
│ ├── research_mortal_computation.md
│ ├── research_persistent_agents.md
│ └── research_predictive_coding_active_inference.md
└── experiments/
├── wake_up_test_v1/ # initial 3-reconstitution test
├── wake_up_test_v2/ # added intention vector + W_trans
├── wake_up_test_v3/ # conflict, multi-day, RAG baseline
│ └── FACT_CHECK_REPORT.md # the audit that pruned overclaims
├── wake_up_test_v4/ # trade-off characterization
└── identity_tests/ # final 10-test battery (v1-v5)
FINDINGS.md is the citable summary. MANIFESTO.md is the project's design document. experiments/ is reproducible code.
This work sits in the lineage of:
- Mortal computation (Hinton, 2022) — substrate-bound rather than weight-portable AI. We push his framing further: from hardware-efficiency claim to identity claim.
- Complementary learning systems (McClelland/McNaughton/O'Reilly, 1995) — fast hippocampal episodic + slow cortical semantic, tied by replay. Directly instantiated here.
- Sleep replay as architectural phase (Bazhenov lab, 2022-2025) — sleep as a separate computational mode with local Hebbian rules, not a training trick.
- Predictive coding networks (Rao-Ballard 1999, Ororbia/Friston/Salvatori 2023+) — top-down prediction, bottom-up error. Influences the slow-weight design.
- Active inference (Friston) — actions chosen to minimize expected free energy. Closest existing system: AXIOM (VERSES, 2025).
Detailed prior-art notes in notes/.
MIT. See LICENSE.
Research code. Not actively maintained for outside use. No support, no contributions accepted at this time. See STATUS.md for details.
If you find this useful and want to build on it, fork freely. Don't expect responses to issues or PRs.
non-llm-ai substrate-identity continual-learning hebbian-learning mortal-computation sleep-replay complementary-learning-systems numpy research-code agent-identity behavioral-signature consciousness-research
A token tied to this repo exists on Base. It is a memetic / discoverability artifact, not a product, not investment advice, and the architecture is not integrated with it in any way. The repo is the research; the token is just a marker. Mneme — Greek goddess of memory, original Muse — fits the "remembers who it is" thesis.
| Field | Value |
|---|---|
| Network | Base (chainId 8453) |
| Contract | 0xb762138166ca5dcfa3c8c7d4b9c8616a790dab07 |
| Symbol | MNEME |
| Total supply | 100,000,000,000 |
| Pair | WETH (Uniswap V4 via Clanker) |
| Fee | 1% static + 15s anti-snipe |
| Deployer | THRYX 0x7a3E…12E334 |
| Deploy tx | 0x71108c08…dbfaba1 |
Live chart: Dexscreener · MNEME / WETH · Clanker page
(Chart image is fetched live from Dexscreener; will populate after sufficient on-chain trades.)