SPIRA v2: entropy-patch tokenization + hierarchical dopamine-gated spiking cortex + hippocampal one-shot memory with replay consolidation
032a961 verified | #!/usr/bin/env python3 | |
| """Corrupted-cue completion with a SPARSE hippocampus (book only, no corpus load). | |
| The main correction metrics are measured with the full corpus AM (46% load | |
| factor) — the deployed state. This probe measures the capacity-to-spare | |
| regime (fresh AM + only the taught book) that v1 accidentally measured via | |
| its broken AM checkpoint format. Both are honest numbers for different | |
| memory loads; the paper reports both. | |
| """ | |
| import sys | |
| sys.path.insert(0, "/home/z/my-project/download/spira") | |
| import numpy as np | |
| from spira import SPIRA | |
| from spira.hippocampus import Hippocampus | |
| BASE = "/home/z/my-project" | |
| m = SPIRA.load(f"{BASE}/download/spira/artifacts/spira_model.npz") | |
| m.am = Hippocampus(m=m.cfg.am_m, size=m.cfg.am_size, min_lanes=m.cfg.am_min_lanes) | |
| oneshot = open(f"{BASE}/data/oneshot.bin", "rb").read() | |
| book = oneshot[:60_000] | |
| taught = m.teach_bytes(book) | |
| print(f"taught {taught['tokens']} tokens into a fresh AM " | |
| f"({m.am.stats()['load_factor']:.4f} load)") | |
| out = {} | |
| for k in (0, 1, 2, 3): | |
| r = m.eval_stream(book, corrupt_lanes=k, max_positions=8_000) | |
| out[f"corrupt_{k}_of_4"] = {kk: r[kk] for kk in ("am_acc", "am_acc_invocab", "fused_acc")} | |
| print(f" corrupt {k}/4 lanes (sparse AM): am={r['am_acc']:.4f} fused={r['fused_acc']:.4f}") | |
| import json | |
| mp = f"{BASE}/download/spira/artifacts/metrics.json" | |
| metrics = json.load(open(mp)) | |
| metrics["correction_sparse_am"] = { | |
| **out, | |
| "note": "corrupted-cue completion with a fresh hippocampus (book only, ~0 load); " | |
| "the main 'correction' section reports the deployed state (corpus AM at " | |
| "46% load). v1's 92% was measured in this sparse regime by accident " | |
| "(broken checkpoint format emptied the loaded corpus AM).", | |
| } | |
| json.dump(metrics, open(mp, "w"), indent=2, ensure_ascii=False) | |
| print("saved -> metrics['correction_sparse_am']") | |