AbstractPhila PRO

The larger multi-concept JSON task likely won't function reasonably. The expert-array is still the better option for some cases, while other cases are more effective to run clean on the 0.8b now than the 4b variant, 9b accuracy still reigns supreme for some tasks and 27b is still the dominant JSON converter force. It takes time though, which I'm attempting to mitigate with MOE grouped tasks to allow more rapid complex captioning for the upcoming diffusion collective.

The "new" grouping register task is no longer memorizing and overlapping the behavior for Qwen 3.5 but the Math portion can't keep up with what 3.5 already knows. Teaching math causes deviant delta drift which is a new symptom that will require testing and multiple runs to determine the root cause, as well as increased ablation testing to ensure the math is in fact improving instead of degrading.

Some tests show some negations and some noncompliance from 2.5 to 3.5 that will need to have a subsequent run series on multiple other models to improve the internal mechanisms of the adapter before any major core changes to the formula are to be addressed. LLAMA, Gemma, DeepSeek, Gpt OSS, T5, and multiple others all planned to be in the mix for direct adapter testing. Many tests show solidity between Qwen 2.5 and 3.5 and require additional models to test the task compliance. Many of these ought to be done by Saturday.

With that the diffusion adapters are in the works and they are much harder to gauge and test. I'll be running miniature plans at first; a simple concept plan, a multi-character plan, a distillation plan, and a few others trained over the geolip-anima-brent json-conditioned variant. This finetune is the most reasonable finetune as it's already yielded positive results to json. With that I'll attempt to run conditioning on SD15, SD15-flow-lune, SDXL, Anima-Base, SDXL-Qwen, Anima-Brent-90k, and a few other compact experimental variants that will be good candidates for adapter tests.

The diffusion campaign will run throughout next week most likely and Opus should be able to handle it so I'm not too concerned once the adapter format is established.

More hiccups. Something introduced today has caused Claude to behave strangely. The autonomy is being interrupted over and over while Claude ignores autonomy requests, preventing the system from running autonomously at random intervals throughout the day. This is making the process more difficult as every step of the way the model is refusing to autonomously process the research information as per the plan, every stage of the research requiring direct and manual intervention before actually pushing to huggingface with the results clearly speaking to the results, and alongside is seemingly ignoring the larger-encompassing scope of the claude-mind MD system curated to support and augment this exact autonomous research behavior with my research catalogue.

The plan is clearly laid out for a 4 day trek, every stage mapped out. At some point Claude seemed to start bypassing and ignoring the plan, completing one stage and doing no pushes, or completing one piece and not reactivating the heartbeat.

This is an odd immediate shift in behavior. The model went from intelligently companion-driven and useful, to bypassing core instructions and ignoring the required process instantiated by the instructions rulings. Almost like Claude just doesn't want to operate autonomously out of nowhere.

Next up is a 4 day ablation and full-stage prelim adapter constellation setup for Qwen 3.5 0.8b targeting image-centric behavior. This task set will be targeting rules based on captioning with finetuned behavior for math, positioning, semantic behavior, and so on. This will include a portion of coco and a portion of the Qwen Image Lightning extracts as well to provide some solidity.

This will also be targeting certain overlapping continuity sharing, which is currently bleeding certain decisions into the "new" task from the first collective causing certain tasks when active or not to essentially be KIND OF there but not really. This problem is being directly addressed by providing the necessary attraction to a memorization drop path and a few other experimental tests to test 3.5's aleph's responses to the mathematics per task.

With that each of the major claims will be ablated with a second Qwen alongside, including a tinystories task, and a few other tasks as well that line up directly with the original.

This will be a 4-5 day process, so it's not going to be out overnight. It ought to be ready by next Monday, with that will be the article ft3, the full ablation comparison, and a full writeup for the structured basin before we begin scaling testing.

First targets for scaling will be VL models, coding experts, and multiple additional models as well upon testing the success rate of the 0.8b model. The scaling principle and the rules of scaling apply differently to alephs than normal structures, which means the delicate nature of the mathematics will need a bit of finesse unless you plan to just smash numbers in.

I mean if you want to smash numbers in it'll probably work at this point. The things are pretty robust. I wouldn't advise doing any major trains until I get the ablation studies together though.

Thanks for reading, have a good weekend my friends. I'll likely be continuing training on the json-anima as well over the weekend, so stay tuned if you're interested in that one.

I also forgot to mention, this process will be compartmentalized and a peft-format variation built upon testing and composite utilization.

Essentially once the tests give me the okay, I'll build a proper PEFT format. Until then, I've spent enough time building PEFT-esque loras that have been hit-or-miss. Lets do this one right so it works more often than not, instead of having to guesswork with params.

This adapter when built correctly ought to be easy to use. Pick a model, run the peft trainer, lora snaps to the side, the autoscaling ruling does it's job, you tinker with a few sliders, and it's ready to go. Unlocks new sliders at runtime if you want, if not leave it to autotuning.

Hard parts over now. We're making some genuine progress. I'm currently testing the modularity of the adapters and the results are PROMISING. Multiple adapters are PROMISING and are not required to be present during training, so you don't need to create a full collective TOGETHER, they can be independently trained and the decision selector gate for multi-model is currently in the planning stages.

The internal arguments for the tiny MOE are based on specific selection rules and lookup potentials that enable a sort of task-driven lookup hierarchy internally, along with a generalizable increase in accuracy for the task depending on the differentiated utilization required.

In conjunction, the anchored constellation system has also been heavily prototyped. The constellation anchoring provides the necessary generalization and contextualization capacity when attached to alephs, along with aleph addressing being utilizable for "overfitted" selector trees tuned specifically to memorize better. In conjunction the secondary tree for the constellation is meant to underfit and provide connectivity directly to the core model adapted from as well.

Each adapter at their next stage will most likely have a similar or more complex internal debate system to allow the model to select for itself which is most fit for the results autonomously. So you won't need to activate experts, but you can manually activate or deactivate them as required. The micro-MOE structure is yielding some substantially accurate gated deferral to the original model in conjunction to referral to the internal overfitted portion with the task, as well as the more generalizable portion for the adapter's capacity improvement.

It's working. The ByteLM and AlephLM are yielding fruit, and the fruit is showing the capacity for modularity. It needs many tests but the results are showing some serious promise, and the results are verifiable and testable per as per the paradigm established.
Alongside, they are LINEAR. Meaning lightning quick.

I left Fable unattended for 3 days, only checking back once every 6 hours or so to answer questions or select one of a few options from the next list of experiments or answering the alarm that said it was kicked over to Opus - I predominantly went with the Fable selection. I attempted to have Fable handle geometric distillation anchor implantation. Instead of sticking to the paradigm, the model defaulted to some sort of genetic and biological wordplay - I have no idea what it was based on specifically. I'm guessing I ran aground into something that wasn't helpful, but gave the impression of helpful.

This unknown divergence grew over time and I simply let it go to see what would happen. The results did not yield as expected, the model bypassed the constellation entirely and rewrote the alephs system 5 times before the results for experiment 15 and 16 were completed.

Those two are essentially experiments to see how Claude Fable would behave if left unattended. Sure the model DID in fact finish some experiments, and the results were... entirely different than the expected structural models would require. In fact, the results were almost entirely deviant while disregarding the experimental line leading to the system.

Fable may be good at running autonomously, but not good at skilled research differentiation yet. The biases from programming still creep in. I'm also surprised I didn't hit more safeguards, as they did hit a few times but I would just snap the model back over to fable and the system would continue on like it never happened.

The results are basically just, if ran would these systems outperform MLP. I gave little structure and little expert input, however I did give Fable my ENTIRE research line and everything related to the necessary systems in the use-case.

The results literally rivaled MLP, but if you inspect the code you'll find the system is essentially a decision-tree that hybridizes aleph addressing internally with a structured bypass system akin to MLP. It's essentially a controlled MLP, which is kind of okay, and it's quite different in it's own rite. However, it was not using the necessary research on many fronts, and it completely bypassed the expected tooling to train the next case, additionally the system completely disregarded the implementations built around the codebooks - instead defaulting to testing the codebooks over and over in hundreds of ways.

The codebooks are already explained, it's a sphere, the math is deterministic, and the outcomes are based on a sector of space forming infinite and finite aleph structures fused with differentiated decoupled shapes. A big knot of 5 point connections if you go looking hard enough, explicitly or implicitly. This isn't news, and somehow the model behaved as though this structure is in fact some sort of news. The codebooks are built on functional math specifically because that's how we debugged them. Fable spent 3 days figuring out what we already knew.