The headline numbers
S3 — name-substitution control
Same syntactic templates, swap the entity name. If the saved LoRA encodes "Eli" specifically (and not generic English), the loss-drop should be much higher for Eli than for any other name. Numbers are mean loss drop (zero-LoRA loss − with-LoRA loss) across three templates.
Falsifier (pre-registered): Eli drop ≤ max drop for other names → claim falsified. Observed: Eli +1.208 vs next-highest +0.608, margin +0.601. Held.
S4 — random-LoRA negative control
Five fresh random non-zero LoRA matrices (NOT the saved one) tested on "Eli: My name is Eli." Random LoRAs should NOT match the saved LoRA's identity help.
Falsifier: random LoRA drop ≥ saved drop − 0.3 → "any LoRA helps" claim. Observed: random mean −1.405 (random LoRA actively hurts), saved +1.496, gap +2.901. Held.
S5 — T4 episode-specific recall, seed sweep
Train two SubstrateLMs from each of 5 seeds, give each a different conversation, measure both gaps. All 10 gaps must be positive (each model prefers its own past on held-out evaluation).
Falsifier: any seed fails T4 → episode-recall is seed luck. Observed: 5/5 seeds pass; 10/10 gaps positive; all > +1.5. Held.
Artifact receipts (SHA-256)
These are the files that produced the numbers above. Anyone can hash their copies and confirm same-binary reproducibility.
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Reproduction
py experiments/identity_tests_substrate_lm.py # ~30s on RTX 4060
py experiments/identity_tests_lora_v2.py # ~10s
py experiments/proof_of_self.py # ~5s
py experiments/proof_indisputable.py # ~2min (incl. seed sweep)
Output JSON ships next to this page: proof_indisputable_results.json, proof_of_self_results.json, identity_tests_lora_v2_results.json, identity_tests_substrate_lm_results.json.
Full writeup
Plain-English version with all 4 experiments, methodology, and what each one rules out: notes/proof_of_self_2026_05_12.md.
Architectural priors cited: Schlag/Irie/Schmidhuber 2021 (linear attention is Hebbian) · Carlini et al. 2022 (memorization scales log-linearly with duplication) · Charles et al. 2024 (user-level DP for LLM fine-tuning) · FDLoRA 2024 (federated LoRA shards).