Attention Is Not Enough
The transformer architecture is remarkable. In the five years since Attention Is All You Need, it has eaten machine learning wholesale. But it carries a structural flaw that matters enormously when you're trying to run inference on a 4W ARM chip at the edge of the decision loop: quadratic complexity in sequence length.
For most cloud workloads, this is a solved problem — throw hardware at it. But for always-on edge intelligence, the thermodynamics simply don't work. A model checking sensor fusion every 50ms on a wearable cannot afford O(n²) attention.
Our Approach
We revisit state space models — specifically a modified Mamba block — and ask a different question: not can we replace attention, but where can we route certain computations through SSMs while preserving the relational reasoning that makes attention powerful?
The answer, somewhat surprisingly, is most of the network. Local temporal patterns, sensor autocorrelations, repetitive signal structures — these are better served by efficient recurrence. Global relational reasoning — cross-sensor fusion, anomaly flags, context retrieval — still benefits from attention.
Our hybrid architecture interleaves SSM blocks and sparse attention heads in a 7:1 ratio, trained end-to-end. The result: 94% of full-attention quality on our sensor fusion benchmark, at 8% of the FLOPs.
Benchmark Results
| Architecture | Accuracy | FLOPs (per token) | Latency (M55 NPU) |
|---|---|---|---|
| Full Transformer | 100% (ref) | 1.0× | 47ms |
| Mamba-only | 81% | 0.06× | 4ms |
| Ours (Hybrid) | 94% | 0.08× | 5.3ms |
Implications
This isn't about replacing transformers in the cloud. It's about making structured world models viable at the edge — where decisions happen in milliseconds, where the silicon budget is measured in milliwatts, and where the cost of getting it wrong is physical.
Open Questions
We are releasing the architecture specification and benchmark suite. Model weights follow once audit is complete.