NuoNuo: Hippocampal memory module prototype

Hopfield + Hebbian hybrid memory system for LLMs.
Two nights of experiments (16 iterations), validated on LongMemEval (ICLR 2025).

Architecture:
- Single-hop: Two-Stage Hopfield (NN top-20 → softmax settle)
- Multi-hop: Hebbian W matrix with WTA pattern separation
- 64% on LongMemEval (500 questions), retrieval-only, no LLM dependency
- 4ms latency @ 20K memories, ~1GB VRAM

Key findings:
- Hopfield attention solved noise tolerance (20% → 100% vs flat Hebbian)
- WTA pattern separation enables 20K+ capacity
- Multi-hop associative chains (6 hops, CosSim=1.0) — RAG can't do this
- MiniLM-L6 is optimal (discrimination gap > absolute similarity)
- Paraphrase cue augmentation: 55% → 100% on synthetic, 36% → 64% on benchmark
- SNN encoder viable (CosSim 0.99) but not needed for current architecture

pyproject.toml

@@ -0,0 +1,25 @@
+[project]
+name = "nuonuo"
+version = "0.1.0"
+description = "SNN-based hippocampal memory module for LLMs"
+requires-python = ">=3.12"
+dependencies = [
+    "torch>=2.10,<2.11",
+    "snntorch>=0.9",
+    "numpy",
+    "matplotlib",
+    "sentence-transformers>=3.0",
+    "openai>=1.0",
+    "requests>=2.33.1",
+]
+
+[tool.uv]
+index-url = "https://pypi.org/simple"
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
+
+[tool.uv.sources]
+torch = { index = "pytorch-cu128" }