Framework Overview

This proposal introduces a framework for large language models to interact with environments autonomously, simulating human-like decision making without direct human participation. The system operates through a structured interface between operator and environment objects.

Universal Non-Human Framework Diagram

Core Architecture

Operator Structure

The human operator is modeled as a composite entity containing:

  • Subject - psychological decision-making core
    • Ego component with confidence weighting
    • Superego component with confidence weighting
    • Memory system storing state transitions and temporal snapshots
  • Object Self - physical representation
    • Current state bundle
    • Physical descriptors of the operator instance

Environment Modeling

The environment consists of multiple object instances following Platonic instantiation:

  • Object Structure - abstract template defining:
    • State bundle specifications
    • Physical descriptor templates
  • Object Instances - concrete manifestations with:
    • Current state values
    • Physical descriptors
    • Observability metrics relative to operator

Interaction Channels

Observation Channel

Operator observes environment objects

  • Operator instance perceives target object instance
  • Both entities transition to next temporal state
  • Observation data written to operator’s subject memory
  • Target object’s observability index increases
  • Bidirectional state evolution recorded

Action Channel

Operator acts upon environment objects

  • Operator instance directly manipulates target object instance
  • Immediate state transition triggered for both entities
  • Target object’s state and observability modified
  • Operator’s memory and internal state updated
  • Complete interaction history maintained

Technical Implementation

State Management

  • Temporal Progression - discrete timestamp-based state transitions
  • Memory Persistence - comprehensive logging of all state changes
  • Confidence Weighting - dynamic ego/superego balance affecting decisions
  • Observability Tracking - quantified awareness levels between entities

Decision Framework

The ego-superego mixture with confidence weighting enables:

  • Autonomous decision making without human oversight
  • Adaptive behavior based on accumulated experience
  • Weighted consideration of immediate vs long-term consequences
  • Dynamic adjustment of interaction strategies

Applications

This framework enables LLMs to:

  • Autonomously navigate complex environments
  • Build persistent memory of interactions
  • Adapt behavior based on environmental feedback
  • Simulate realistic human-like decision patterns
  • Maintain consistent state across extended interactions

Future Development

Potential extensions include multi-agent interactions, hierarchical object structures, and integration with reinforcement learning for optimized decision weighting.