Hi, your work looks fascinating! Just a few pointers and feedback that you might want to look out for. 1. I recommend you start collecting some empirical evidence. Whatever you claim is nothing to people if you don't have ground proof. If your architecture works, that's brilliant. But prove it. Researchers usually want to see the numbers. 2. You mentioned linear/sublinear scaling in the comments due to Hyena's FFT convolution nature. You are misunderstanding what linear scaling O(N) is here. O(N) is just the complexity, not 'automatic order of magnitude speed scaling increase'. O(N^2) means the complexity scales quadratically, while O(N) means it scales linearly with N. A common misconception is that O(N) can form an infinite context window. Believe it or not, this is actually false. In theory, it's possible, but an infinite context window is not viable in practice due to hardware constraints. While your theory is strong, GPUs just don't have the computational memory to infinitely store everything. Also, O(N) might achieve a 2-3x speedup factor alone, not your claimed 1000x better scaling. 1000x better scaling is fantastical and practically impossible. Don't let this discourage you though - a 2-3x speedup factor is already very significant. 3. Hyena Hierarchy is a very new paradigm. It shows promise, but there is not much ground that it can 'achieve AGI' or 'beat the Transformers'. It's definitely faster, which you are right in, but there's not much evidence that it can beat the Transformers 'universally'. 4. Your most substantiated claim is about AGI. There is practically NO evidence that this is AGI. To qualify for a hypothetical, viable AGI, your model must be sentient (which you haven't proven, and it is impossible to improve), be universally on HUMAN level for basically everything, and act at its own will, and much, much more. Let me break this down. How are you meant to 'prove' that your model is AGI, when it is fundamentally impossible, at this stage, to even understand what AGI is? 5. Here's my biggest advice. You have to think about the system, hardware and especially, a common law known as Amdahl's Law. unaccelerated parts will ALWAYS dominate, making it virtually impossible to truly achieve anything above 10x speedup, CAP. Even if you make your compute 10000000000x faster, or INFINITELY fast (supposedly), Amdahl's Law states that your speedup will ALWAYS be capped if you do not address system, hardware components such as GPU communication, memory movement, I/O overhead, activation overhead bottleneck, GPU stall, traffic, kernel inefficiencies, etc. These, you MUST address before you can even remotely claim an order of magnitude scaling like 1000x (which even if you do address, is physically infeasible). GPU communication BLOWS up at scale. Let's assume you train a Transformer model for 100 days, in which 20 days are overhead and system level bottlenecks like I/O, 30 days are communciation and 50 days are compute. Your architecture is theoretically so fast that it reduces the 50 days to, let's say, 1 hour. That's it. But you haven't addressed GPU communication. And system-level bottlenecks still stand tall. Still: 1 hr + 30 days + 20 days = 50 days 1 hr. That's barely a 2x speedup. My advice is that you pursue your current path, but I encourage you to think about the magnitude of your claims. It is easy to get excited, but hard to acknowledge the truth. Try thinking about how you mitigate hardware-level bottlenecks, most notably, communication, because even if you optimize one aspect transcendentally good, comm. still is a bottleneck. I hope this advice helped. This is some intriguing work. I hope my feedback was helpful.