Standard instruction tuning uses "single-turn" data. v4’s training set was unique: 60% multi-turn debates, 30% collaborative storytelling, and 10% structured coding interviews. This makes the model exceptionally good at:
Forget HBM3e. The Artax-ttx3 uses a hybrid 3D-stacked memory called GigaBand SRAM-Plus. With a total bandwidth of 12 TB/s and a capacity of 288GB on-package, the v4 can hold an entire MoE (Mixture of Experts) model locally. The "Mega Multi" aspect shines here: each model expert resides in a dedicated physical partition, preventing cache polution. Artax-ttx3-mega-multi-v4
Most models claim 200k context but begin "confabulating" around 50k. Artax-ttx3-mega-multi-v4 maintains 92% coherence at 200k tokens and degrades gracefully to 78% at 256k. This is achieved via YaRN (Yet another RoPE extensioN) combined with a novel sliding window that prioritizes emotional beats over structural words. Standard instruction tuning uses "single-turn" data
For the hardware enthusiasts, here is what the v4 is packing: The Artax-ttx3 uses a hybrid 3D-stacked memory called
While not a medical device, law firms use the "Mega" variant to review depositions. The 256k context fits entire trial transcripts. The model can identify witness testimony contradictions that human paralegals miss, purely due to the Temporal Residual Vector tracking.
As of Q2 2025, the Cydonia Group has announced a roadmap. v5 will introduce "True Multi-Modality" (image generation via diffusion in the latent space) and a reduced parameter count (27B) using knowledge distillation. The goal is to make the temporal memory architecture runnable on a single 24GB GPU.
Furthermore, the "Artax" branch is merging with the "Phoenix" project to create a model that never forgets—a continual learning LLM that updates its weights locally without retraining from scratch.