aether-10m

AETHER-10M: Associative Energy-Preserving Topological Hamiltonian Entropy-Resonator

AETHER is a next-generation post-Transformer foundation model architecture designed to solve the quadratic memory bottleneck and the linear KV-cache wall ($\mathcal{O}(N)$) of standard Attention mechanisms.

By unifying Unitary Phasor Associative Flows ($\mathbb{U}(1)^D$) with Symplectic Hamiltonian Leapfrog Deliberation, AETHER achieves strictly constant $\mathcal{O}(1)$ recurrent state memory and reduces inference energy dissipation by over 200×.

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🏛 Architecture Highlights

graph LR
    subgraph AETHER Layer Architecture
        X[Input Tokens x_t] --> U[Unitary Phasor Scan U1^D]
        U --> S[Symplectic Leapfrog Deliberator]
        S --> F[Feedforward Gated Network]
        F --> O[Output Representations y_t]
    end

1. Complex Unitary Phasor Ingestion ($\mathbb{U}(1)^D$)

Encodes tokens into continuous phase angles $\boldsymbol{\theta}t = 2\pi \cdot \sigma(W\theta x_t)$, mapping states onto a complex Riemannian manifold. State covariance updates follow: $$\mathbf{Z}_t = \boldsymbol{\alpha}t \odot \mathbf{Z}{t-1} + \boldsymbol{\beta}_t \odot (\mathbf{v}_t \mathbf{k}_t^)$$ where $\mathbf{k}_t = \cos(\boldsymbol{\theta}_t) + i \sin(\boldsymbol{\theta}_t)$. Information retrieval is performed via exact phase conjugation $\text{Re}(\mathbf{Z}_t \cdot \mathbf{q}_t^)$, guaranteeing orthogonal variable binding without Euclidean interference.

2. Symplectic Hamiltonian Leapfrog Deliberation

Processes intermediate states using conservative physical phase-space dynamics $(\mathbf{q}, \mathbf{p})$ with strict Liouville volume preservation ($\det(J) \equiv 1$): $$\mathbf{p}{n+1/2} = \mathbf{p}n - \frac{\epsilon}{2} \nabla{\mathbf{q}} \mathcal{V}(\mathbf{q}n, \mathbf{h})$$ $$\mathbf{q}{n+1} = \mathbf{q}n + \epsilon \mathbf{p}{n+1/2}$$ $$\mathbf{p}{n+1} = \mathbf{p}{n+1/2} - \frac{\epsilon}{2} \nabla{\mathbf{q}} \mathcal{V}(\mathbf{q}_{n+1}, \mathbf{h})$$

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📊 Empirical Benchmarks & Hardware Scaling

Hardware Scaling Comparison at Context Length $N = 2048$

Multi-Task Reasoning Scorecard

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🚀 Quickstart & Inference

import torch
from transformers import AutoConfig, AutoModelForCausalLM

# Load model directly with custom remote code
model_id = "gautamabhish/aether-10m"
config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

# Generate next tokens
prompt_ids = torch.tensor([[12, 45, 99, 1024]])
generated = model.generate(prompt_ids, max_new_tokens=30, temperature=0.7)
print("Generated Tokens:", generated)

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🔬 Running Standard Benchmarks via LM-Evaluation-Harness

pip install lm-eval

lm_eval --model hf \
    --model_args pretrained="gautamabhish/aether-10m,trust_remote_code=True" \
    --tasks arc_easy,gsm8k,hellaswag \
    --batch_size 16 \
    --device cpu

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📜 Citation & Research Foundations

@article{aether2026,
  title={AETHER: Associative Energy-Preserving Topological Hamiltonian Entropy-Resonator for Post-Transformer Sequence Modeling},
  author={Abhishek, G.},
  journal={Antigravity Deepmind Advanced Agentic Coding Research},
  year={2026}
}