--- license: apache-2.0 --- # 5/7/2026: Converting to five prototypes I think I fixed the downstream codebook error from missing components or faulty types, so that's likely fixed now. There are two core sister components that will require creating and with that 3 model design concepts. ## SVAE Centrifuge aka "The Shredder" This component's primary purpose is to take in the center-mass of the SVAE and blend it with it's own, then capture using a different decoder than the SVAE was trained on to begin with. A secondary decoder's job is to capture the three-band extrapolation of the system when bias is introduced using CV cayley-menger processing. As redundant as this sounds, the current encoder/decoder paradigm has proven that it can work. **The Johanna Grandmaster Experiment** Johanna's decoder was retrained into grandmaster to denoise, which was a model directly trained to inference using the omega noise battery - 16 types of noise, adversarial, cooperative, and simple combined together to create the decoder of Grandmaster. Grandmaster's decoder is Johanna's decoder, but the decoder itself was conditioned using Fresnel's omega tokens and then trained to reconstruct clean images. Experts: * Fresnel -> Imagenet trained to recon nearly perfect. * Johanna -> 16 types of noise trained to recon nearly perfectly. Both are about 17m params each in their larger prototype stages with d16 as their bottleneck size. Trained no kl_divergence or anything else that would corrupt the internals of the system with invalid pathways. * Clean Image -> Into Fresnel * Sigma Noised Image -> Into Johanna * Capture Fresnel's Omega tokens center-mass while Fresnel reconstructs. * Capture and train masked loss * Maximize recon capacity towards clean and discourage noise Outcome: https://huggingface.co/AbstractPhil/geolip-SVAE/tree/main/v30_grandmaster  The experiment shows marked denoise improvement in a direct pragmatic visual-sense over time.  This is a direct visual representative outcome. Johanna saw noisy images with a frozen encoder, Johanna's decoder retrained to denoise images into closer-to-grandmaster image states. The outcome yielded enough to further experimentation, so I now I present the first component. **What is this** This is a training process. Predominantly, Grandmaster had one encoder and one decoder. This will be no different, where the developer can include as many encoders or decoders as they like, and the process will utilize a specific format of loss related to how that behavioral system is meant to adjudicate using the distillation process. **Risks** * Overtraining bias that causes the memory to shred the information into less than useful data. * Faults with overlapping data presenting themselves down the chain that require adjustment. * Overlapping datatypes trained on the same structure causing structural faults unknown currently. ## SVAE Crusher This is a distillation-centric component concept. This component's primary purpose is to crush Nth amount of decoders into a processing decoder array that takes a single encoder's inputs. ### frozen encoder, generational decoders We align each using procrustes to ensure the analysis is solid, and then we take the output of the encoder and feed it into all the semi-frozen decoders. The encoder's job is to accept the procrustes rebounded information from the omega tokens. We UPDATE the decoders along an axial update relational system using a memory bank attached to the encoder directly, which gives us a relational lookup of the changes. Likely something directly aligned with ADAM for the early experiments - 1 update per 1000 steps using gradient accumulation and averaging. Smaller batch sizes are likely the best option for this but experiments will test the optimal amount and size. adam 1000 / relative assumed step THEN WE FUSE the decoders together using generational interpolation and bias, and then select the next generation of decoders for the next generation of the learning and train more of them. **The Genetic Experiments** This variation is based on the genetic experimental learning, where each subsequent grown state of a model has shown attribution elements based on the growth of the originals that were souped to fuse it. There's a complete article on this exact process. https://huggingface.co/blog/AbstractPhil/geometric-memory-ft3 Consensus Distillation is the necessary paradigm required to make this system function. Generational progression changes that embed those changes into an encoder's bias, while the decoders are left frozen. I will be attempting multiple tests of this. * Frozen encoder, generational decoders * generational encoders, frozen decoder * generational encoders, generational decoders ### From the article * interpolated systems of best, average, worst, and the "cletus" clause * best set of runs only * worst set only until final stages * divergent non-shared data of the same time * not necessarily survival of the fittest, but survival of the pattern **The Hypothesis** Each component's core updates will establish a format of embedding that can be retroactively transplanted to the reset position, and then from that reset position we feed different data. Each generation of this component's memory will expand further to enable a distillation effect of more data into the memory bank. Thus forming consistent embeddings, rather than patchwork or sampled later embeddings. If this holds, the embeddings can potentially be given a reusable definition. **The Potential Gains** Encoding MULTIPLE layers of an LLM into a single battery responder of a larger size, rather than having a single battery or multiple battery samples per layer. Hardware constraints alone this will provide a large surplus of training potential, including access to larger arrays of datasets by simply extracting LLMs of their layers while having conversations, and encoding those sets of layers into embedding lookup pathways for those LLMS. **Potential Medical Uses** This is a direct extension to the Ryan Spearman process, which will potentially enable more accurate lookups and a more accurate memory distilled student model in conjunction with higher-speed and better recall than a constellation could provide. **The Risks** * Generational bias from averaging, which is a common tail/head killer for soup models. * Faults from soup transfer without enough attention. * Incorrect recon loss potentially introducing non-omega biases that mimic omega symptoms ## Model Component 1: The Memory Oscillator This is the first attempt at an SVAE **attention mechanism**. This variation will use centrifuged dissonance rupture sampling and prediction aka CDRS Flow Matching. This will take the SVAE inputs and turn them into flow-matched directional inputs. With that we'll attempt to use the spinner to shred the directions and use a form of ODE diffusion to replicate the purpose of them. This may or may not need the codebook. The processing system will be a bit different, have timesteps, and have internal structural baesian biases meant to target specific elemental nicities that may not exist and could potentially get in the way. Essentially, the idea is to create a lightning rod for the patterns. We feed complex patterns, and with that we need a conduit to actually map the data to something uniformly potent. **Structure** Uncertain so far. This will be based on the results of the first two. I predict this to be a viable choice for direct diffusion prediction to teach alternative prediction and pattern types using the structure. Similar to actual transformers, this is meant to predict and weight specific rulings based on that. **Heads** Standard attention has commonly 1 per 64 dims of behavior, however this model represents 1 per patch, which means a 1024 patch model houses 1024 potential attention heads. Average, aggregation, and behavioral adjudication says the d16 process of geometry needs to be curated correctly for the downstream utility as of currently. This is meant to provide opinions that are unilaterally useful, rather than vague or entirely up to chance based on which input is which, and which input is not which. **Internals** This is not QKV in the traditional sense. More than likely it's almost entirely going to be V oriented, where everything internal is adjudicated using the mechanisms in order to both guarantee data-type in, and data-type out. Which will allow massive compression and conjuctive storage internally based on the specific paradigm trained with. Hundreds of experiments worth, thousands of tests worth, show that modifying the V is a very tricky business. So, everything has to be perfect or else the model itself simply feeds noise. **Compression** The idea isn't to compress the data itself, it's to compress the processing mechanisms meant to store the data into a uniformly and unilaterally documentable state. Something that doesn't simply exist within a state of a model, but can directly be accessed and tested for measurable differentiation directly. **Why Thousands of Heads???** Simply put, not every idea can have a single response. There are often many elements we take into account for every equation, every single element of structure can't be made accountable for every element of every structure if the structured elements aren't represented in a usefully understood format. Thousands of heads, were a byproduct when attempting to teach geometry. They can judge based on importance, structure, syntax, whatever is necessary and the direct tasking training - when refined - should be directly controllable. ASSUMING I get the logistics worked out for the routing and I resolve the direct interaction with the standard transformers. **The Weights** Specifically spectral in nature, will require alpha, delta, gamma, and specifically sinusoidal timestep interpolation for this prototype. This will specifically measure how effective timestep interpolation is with standard transformers, to see if we can directly capture useful embeddings and encodings over time through this measured system. This will also include an omega to represent the utilization of the difficult-to-analyze symphonic nature of the internals of every model. If this omega element holds, the structure should be directly adjustable by radial scalar magnitude given a bit of tinkering, or potentially a better sampling function that is to be determined as of right now. **The Risks** Not many, monotone behavior, structural collapse, faults with the attention bias, etc. Standard risks. ## Model Component 2: The Embedding Solidifier This is the first attempt at a real embedding system for downstream training. These embeddings are a little different. Based entirely on directional similarity rather than cosine similarity, as measuring cosine similarity via the codebook and the actual model yields little. Even when masking yielding only the portions that change mostly, the models still only yield little tidbits of cosine difference, and that's not the core problem. The core elemental embeddings are already a structural potential BUILT IN the architecture. Meaning they are already implicitly learned rather than an explicit guarantee. This structure makes them difficult to track and a bit more nebulous than a traditional embedding, so I'll be attempting to codify the embedding process for Omega in an orderly and reasonbly understandable way. ### This one is pending the results of component 1 ## Model Component 3: Oscillation Scattered Similar to how the SVAE attention mechanism is designed, this will be the directly linear variation. Intentionally compact and meant to house the necessary complexity...
