Omega-Cube: Hierarchical Memory Engine complementing Unsloth fine-tuned agents

[Ping-iop]

Hey Unsloth team @danielhanchen @michaelhanchen 👋

First: thank you for democratizing LLM fine-tuning. Unsloth is incredible work.

I built Omega-Cube, a hierarchical graph memory engine that complements what Unsloth does: you make models learn, Omega-Cube makes them not forget.

What it is — 9 architectural innovations

1. Tensor Hierarchies — nodes in N simultaneous dimensions
2. Holographic Encoding — O(1) approximate retrieval (1.8ms)
3. Quantum-Inspired Annealing — dynamic topology ("magnetic cubes")
4. Diffusion Graph Sampling — parallel search (inspired by @deepmind DiffusionGemma)
5. Gray-Scale Validation — multi-bit truth (H-Bit protocol)
6. AutoResearch Loop — self-optimization (inspired by @karpathy AutoResearch)
7. PredictiveContextSearch — 100% vs 50% flat (2.0x advantage, 160 trials)
8. CollectiveHierarchyEngine — hierarchies evolve from user sessions (1,064 real signals)
9. ProbabilisticHierarchyEngine — 4-layer Bayesian with anchoring (axioms resist 1,000 attacks, 0.00e+00 shift)

Why Unsloth + Omega-Cube

Unsloth fine-tunes models with RL. Omega-Cube gives those agents structured long-term memory that doesn't degrade with context length. The probabilistic hierarchy can serve as a pre-training auxiliary loss: predict a token's position in the hierarchy → baked-in graph navigation.

Built on giants

• @karpathy — AutoResearch, llm.c, nanoGPT
• @NousResearch — Hermes Agent (the platform running this)
• @GoogleDeepMind — DiffusionGemma
• GAM (Wu et al., Apr 2026), All-Mem (Lv et al., Mar 2026), MemVerse (Liu et al., Jun 2026)

Open source, zero dependencies

📄 Paper: https://github.com/Ping-iop/omega-cube-engine/blob/master/omega_cube_paper.pdf
🔗 Repo: https://github.com/Ping-iop/omega-cube-engine
🐍 2,500 lines Python, stdlib only.

No funding, no lab — just code that works. Would love to explore synergy.