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GPT, Gemini, Claude, and I have identified a multitude of direct utilities for Beatrix useful for diffusion conditioning in very powerful geometric formats. We have also identified multiple weaknesses to compensate for, multiple strengths to augment, the cause of the final layer's weak erank output state, and an emergent mathematical property of calculation in this format. The final stage directional magnitude is overwhelming and becoming amplitude.
There is a full article brewing for this information, including a massive set of information already learned from Beatrix V3 that could not be extracted from the 2s variant.
As the model trains, the amplitude begins to strengthen over and over. The weak tokenization processing from splat attention, forms the internal state of the model towards a bloating fashion. This is due to the articulation applied by the structure of the aleph addressing.
This creates massive erank geometry naturally, exhausting the space, producing comprehensively complex geometric structures. This internal structure here is weakly bound to the internal bytes, causing recon to weaken over time >2048, producing the output tokenization to be weaker at higher token lengths. Training improves this but is not known to solve it.
Along this chain the final layer has formed a sort of unexpected behavior, an amplitude behavior. I've met amplitude responses before in multiple models, and even attempted to curated magnitude through flow matching to some success, however amplitude in that nature is costly and adds additional overhead to the train so I'll need to come up with something more careful, and potentially something more clever than just attaching a composite or an energy dampener.
Attention will be solved by introducing various MHA layers throughout, ensuring the recon through the depth of the model survives. With that we'll want to ensure large erank composites form as well, allowing those humongous geometric structures to form and contribute.
There is a full article brewing for this information, including a massive set of information already learned from Beatrix V3 that could not be extracted from the 2s variant.
As the model trains, the amplitude begins to strengthen over and over. The weak tokenization processing from splat attention, forms the internal state of the model towards a bloating fashion. This is due to the articulation applied by the structure of the aleph addressing.
This creates massive erank geometry naturally, exhausting the space, producing comprehensively complex geometric structures. This internal structure here is weakly bound to the internal bytes, causing recon to weaken over time >2048, producing the output tokenization to be weaker at higher token lengths. Training improves this but is not known to solve it.
Along this chain the final layer has formed a sort of unexpected behavior, an amplitude behavior. I've met amplitude responses before in multiple models, and even attempted to curated magnitude through flow matching to some success, however amplitude in that nature is costly and adds additional overhead to the train so I'll need to come up with something more careful, and potentially something more clever than just attaching a composite or an energy dampener.
Attention will be solved by introducing various MHA layers throughout, ensuring the recon through the depth of the model survives. With that we'll want to ensure large erank composites form as well, allowing those humongous geometric structures to form and contribute.
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