SEMI-CAPS: First Principles of Perception Architecture
The SEMI-CAPS architecture organizes perception into eight first principles for psychophysical computing: stimulus, encoding, modality, integration, context, attention, prediction, selection.
Generalizing artificial intelligence one step (function) at a time.
A working-paper series on Higher-Order Function (HOF) cognition.
The SEMI-CAPS architecture organizes perception into eight first principles for psychophysical computing: stimulus, encoding, modality, integration, context, attention, prediction, selection.
A psychophysical validation framework for cognitive models using signal detection theory, JND, and reaction time to address hallucination and improve LLM robustness.
HOF cognitive supertransformation, a unified framework uniting transformers, RAG, fine-tuning, and HOF cognition through multi-agent orchestration, reliability, and security.
A comparison of RAAFT against LoRA, full fine-tuning, and static inference across reasoning generalization, temporal adaptability, stability, and long-term efficiency.
HORA represents rank as dynamic higher-order functions rather than static low-rank matrices, enabling context-aware, layer-specific fine-tuning of large models.
RAAFT combines higher-order rank adaptation with atomic function tokens for dynamic, context-aware, parameter-efficient fine-tuning of large neural networks.
How HOF SWIFT attention mechanization decomposes similarity search into modular atomic functions for scalable, hardware-aware retrieval across CPU, GPU, and TPU.
A technical look at Functional Atomic Recomposition (FAR): dynamic retrieval, recursive dependency validation, and Dynamic Assembly Blueprinting for adaptive AGI reasoning.
Why Cursor, Devin, and Replit fall short on full-stack SaaS, and how HOF Cognitive FAR preserves context, resolves dependencies, and scales multi-agent reasoning.
How HOF cognitive computing curbs token explosion via abstraction, modular reasoning, and functional composition, unlike the exhaustive reasoning behind o3.
SWIFT attention mechanization tackles FAR's hardware bottlenecks by balancing dense and sparse matrices and precompiling higher-order functions across hardware.
HOF cognition applies Weber-Fechner, JND, and Signal Detection Theory to make distributed vector representation more efficient and scalable than o3 reasoning.
How HOF cognition evolves from Functional Atomic Decomposition (FAD) to Functional Atomic Recomposition (FAR), recomposing atomic units into adaptive higher-order reasoning.
A step-wise approach to transforming traditional machine learning methods into autonomous HOF cognitive pipelines through functional decomposition, orchestration, and meta-learning.
HOF Adaptive Cognitive Computers (HACC) learn, adapt, and grow over time. Walk through an agent that optimizes resource allocation, with a Cognitive DSL example.
Applying higher-order function principles to Meta's Segment Anything Model (SAM) for modular pipelines, dynamic adaptation, and parallelism, with PyTorch examples.
Encoding cognitive atomic function graphs into distributed vector spaces for parallel reasoning, elastic scaling, and fault tolerance beyond centralized o3-style traversal.
Build a distributed representer in Rust with ndarray: text vectorization, a self-attention mechanism, and serializable distributed representations, with full code.
How a chatbot uses Functional Atomic Decomposition and the Input-Process-Output framework to plan, generate, run, validate, and package Hello World code in every programming language.
A proposal for Higher-Order Function cognitive computing that generalizes across integration, unsupervised-learning, and context-management challenges by composing reusable higher-order functions rather than scaling transformer self-attention.
How Higher-Order Function cognition combines with self-supervised reinforcement learning, Flash Attention, and self-attention by distributed representers to enable more efficient, scalable AI reasoning.
How HOF cognitive SWIFT attention mechanization uses FlashAttention, sparse attention, low-rank approximation, and mixed precision to scale attention across CPUs, GPUs, and TPUs, with C code examples.
A YAML-based cognitive DSL that models an agentic workflow as higher-order functions and atomic functions, with a continuous improvement loop for the Hello World in Every Language project.
A framework proposing psychophysical memory recall as a higher-order function that uses Flash Attention and functional atomic decomposition to selectively recall only the relevant memory traces, generalizing to long-horizon tasks without exhaustive context.
Why RAG pipelines fall short for AGI, and how modeling memory recall as a Higher-Order Function adds continuous execution, generation, and validation loops.
Functional Atomic Decomposition breaks higher-order cognitive functions into reusable atomic functions, indexed in vector space for dynamic recomposition — generalizing across tasks by recombining reusable atomic functions rather than retraining the whole model.
How psychophysics — signal detection theory, Weber's Law, thresholds — informs HOF cognition and machine learning models of decision-making under uncertainty.
How distributed intermediate representations power ML, AI, and big data — Spark RDDs, TensorFlow graphs, Horovod gradients, MapReduce, and Flink state.
How Higher-Order Functions add validation gates, retry loops, context management, and multi-agent coordination to keep LLM agent workflows reliable and coherent.
A step-by-step Rust build of a flash-attention mechanizer with HOF cognition and distributed representation, served over hyper and tested with curl requests.
How HOF cognitive transformation reworks traditional ML methods—decision trees, k-means, gradient boosting—into modular pipelines, with PyTorch and TensorFlow code.
How Flash Attention cuts the quadratic cost and memory bottleneck of transformer self-attention via kernel fusion and block-sparse patterns, aiding distributed representation learning.
A survey of why classic ML—reinforcement learning, ensembles, Bayesian methods, evolutionary algorithms—stays relevant for AGI, especially through a higher-order function cognitive lens.
How self-attention—query, key, value, and softmax weighting—can enhance higher-order function cognition through dynamic contextualization, long-range dependencies, and hierarchy.
Tracing HOF cognition from lambda calculus and LISP through Scheme, ML, and Haskell to cognitive architectures and its proposed role in adaptive, context-aware AGI.
How HOF cognition combines functional atomic decomposition, high-dimensional vector indexing, attention, and metacognition to generalize reasoning by recomposing reusable atomic functions rather than scaling a monolithic model.
Functional atomic decomposition breaks higher-order cognitive functions into reusable atomic functions, indexed in vector space for dynamic recomposition — generalizing across tasks by recombining reusable atomic functions rather than scaling a single model.
Evaluating whether SML (Semantic Modeling Language) qualifies as a true DSL—syntax, domain abstractions, boilerplate reduction, independence—for business-intelligence data modeling.