Research and Advancements

Core AI System

InteliAgent — The Brain of the System

InteliAgent is our signal‑driven cognitive system that guides responses through three core behaviors: knowing, not knowing, and asking. It processes 100+ knowledge areas through multi‑domain signal coordination to determine confidence levels and adapt behavior in real time.

Core Behaviors

• Knowing: High‑confidence responses with focused, authoritative output when signals indicate strong domain knowledge
• Not knowing: Acknowledges uncertainty and provides exploratory or cautious responses when signals show knowledge gaps
• Asking: Seeks clarification or additional context when signals indicate ambiguity or insufficient information

Signal Processing Architecture

• Multi‑domain signal coordination: Processes 100+ knowledge areas spanning language, mathematics, and specialized domains
• Uncertainty‑aware control: Computes entropy‑based scores and temporal derivatives to derive adaptive control signals
• Context‑adaptive decoding: Maps control and signal states to generation parameters in real time
• Cross‑signal learning: Enables knowledge transfer between related domains through relationship modeling
• Staged learning progression: Introduces knowledge areas progressively with curriculum‑based training

Knowledge Areas Coverage

InteliAgent processes structured knowledge across multiple domains to inform its three core behaviors. The system coordinates signals from language skills, mathematical reasoning, and specialized domains.

Domain Coverage

• Language Skills (20 areas): From letter recognition to discourse structure and genre adaptation
• Mathematical Skills (88 areas): Basic concepts, pure mathematics, applied mathematics, and specialized applications
• Custom Domains: Python programming, specialized attribution, and domain‑specific knowledge
• Signal coordination: Weighted combination and conflict resolution across active knowledge areas

Adaptive Training Integration

• Staged curricula: Progressive activation of knowledge areas with transfer learning between related domains
• Behavioral conditioning: Train responses for knowing, not knowing, and asking behaviors across all domains
• Real‑time adaptation: Continuous learning and behavior adjustment without retraining from scratch
• Performance gating: Dynamic management of high‑performing areas to focus optimization efforts

Developed Research

Alpha‑ML: Information Flow Controller & Signal Head

Role: information tracking and flow control — determines when and how to continue, pause, or modulate.

The Alpha-ML Controller works in tandem with InteliAgent as the information flow controller and signal head coordinator. It evaluates each step of the generation process as it happens, using entropy‑based uncertainty metrics and signal activations to determine when and how the system should continue, pause, or modulate its output.

It’s more than a prompt engine — it’s an environment-aware controller that shapes system behavior dynamically. From pacing and confidence to pausing, reflecting, re-engaging, or actuating, the controller gives every output or action a sense of timing, control, and intention.

Core Capabilities

• Real-time feedback monitoring during token generation or control cycles
• Output/control decisions based on internal system state, not only external prompts or fixed thresholds
• Auto-modulates flow, pacing, actuation, and when to pause/stop
• For LLMs: works with any transformer-based model (via Hugging Face); for other systems: integrate via simple adapters
• Memory-enabled CLI environment for context persistence
• Behavior refinement without modifying the base model or underlying process

Alpha-ML’s controller introduces a layer of live intelligence to any system. It doesn’t just generate — it manages process and intent.

Patent pending.

Advanced Research

Alpha-Adjust: Real-Time Functional Control API

Alpha-Adjust is a lightweight, high-precision control engine that enables systems to self-regulate in response to changing conditions. It provides dynamic output adjustment based on signal deviation — helping intelligent systems remain stable, adaptive, and aligned without the need for manual intervention or retraining.

Built for seamless integration, Alpha-Adjust allows you to enforce operational control through configurable behavioral profiles — such as focused, balanced, or stable — letting you tune the system’s reactivity to match its context. No model modification required.

Key Capabilities

• Live output modulation based on real-time input fluctuation
• Profile-based behavioral modes for precision tuning
• Standalone REST API with no external dependencies
• Works across AI pipelines, automation platforms, and physical systems
• Zero learning curve — configure and deploy instantly

Applications

• Autonomous vehicle control and directional stabilization
• Adaptive learning rate management in live model training
• Robotics thrust adjustment and energy regulation
• Industrial sensor loops in rapidly changing environments
• Dynamic value modulation in simulation systems

How It Stands Apart

• Works instantly — no fine-tuning, no setup training required
• Behavior-driven — adapts to signals, not static thresholds
• Platform-agnostic — deploy anywhere via API
• Deployable on cloud, local, or embedded systems

Alpha-Adjust enables systems to think in response — modulating their behavior on the fly to maintain control under pressure.

Patent pending. Commercial license required for redistribution or embedded integration.