12 papers
p-Spin Glass Network Efficient Single-Batch Continual Learning
Vladimer Khasia
Modern sequence models heavily rely on massive memory footprints and large-batch stochastic optimization, barriers that restrict sample efficiency and continual learning. We introd…
HoloByte: Continuous Hyperspherical Distillation for Tokenizer-Free Modeling
Vladimer Khasia
Sequence modeling universally relies on discrete subword tokenization to circumvent the computational intractability of native byte-level attention. However, thi…
BASIS: Balanced Activation Sketching with Invariant Scalars for "Ghost Backpropagation"
Vladimer Khasia
The activation memory required for exact backpropagation scales linearly with network depth, context length, and feature dimensionality, forming an O(L * BN ) spatial bottleneck (w…
Beyond Attention: True Adaptive World Models via Spherical Kernel Operator
Vladimer Khasia
The pursuit of world model based artificial intelligence has predominantly relied on projecting high-dimensional observations into parameterized latent spaces, wherein transition d…
Hybrid Dual-Path Linear Transformations for Efficient Transformer Architectures
Vladimer Khasia
Standard Transformer architectures rely heavily on dense linear transformations, treating feature projection as a monolithic, full-rank operation. We argue that this formulation is…
HAS-VQ: Hessian-Adaptive Sparse Vector Quantization for High-Fidelity LLM Compression
Vladimer Khasia
Post-training quantization is essential for deploying Large Language Models (LLMs) on resource-constrained devices. However, standard integer quantization (e.g., INT4) fundamentall…