How does that contribute to AI safety (if at all)?
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CKSM is a sequence model architecture derived from the kinetic dynamics of the NMDA receptor. It is deterministic, runs in constant memory regardless of context length, and learns continuously without retraining. The goal is to found a company around it and sell specialized inference to enterprises that need a model that genuinely improves over time on their domain.
Finish the research paper and run the CKSM-tiny experiment on WikiText-103, comparing against Mamba-3 at identical training FLOPs on a single RTX 4090. This is the first falsifiable empirical result. If it passes, the architecture scales.
Living expenses to work on this full time.
Solo. 22 years old, Paris, no degree. In the past year I built Tachyon, a lock-free shared memory IPC library with 56ns p50 round-trip latency across 7 languages, and Kinapse, a recurrent spiking neural network running a full forward pass in under 5 microseconds on a single CPU core. Both are public on GitHub: github.com/riyaneel/Tachyon and github.com/riyaneel/Kinapse.
The kinetic gate provides no measurable perplexity gain at tiny scale. This is a defined hard stop before any large-scale compute is spent. The architecture gets revised.
Zero.
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Riyane El Qoqui
8 days ago
@damc4 Current safety relies on behavioral patching to suppress a model's probabilistic urge to guess. CKSM handles safety at the base architectural level. By replacing probabilities with strict geometric thresholds, the model physically cannot compute a response if the conditions aren't met. It structurally abstains. Safety becomes a mathematical consequence of the system itself rather than a post-training filter.
Damian
5 days ago
@riyaneel I don't understand your answer. What are "strict geometric thresholds"? What conditions?
In order for you to know if it's worth your time to respond, I'll say that conditional on me knowing how it contributes to AI safety, there is around 5% of chance that I'm going to donate $1000 (otherwise, the chance is 0).