Minimal working example: SUBIT‑64 as internal state of an agent

1. Semantics of the 6 bits

We encode the SUBIT‑64 state as:

• O: Orientation

• P: Persistence

• I: Intentionality

• R: Reflexivity

• A: Agency

• Op: Openness

Each is binary (0/1) at the structural level.

text

State: O P I R A Op
Example: 1 1 0 0 0 1

2. Core idea of the example

We implement:

• an Agent with a 6‑bit SUBIT‑64 state

• behavior rules that depend on which bits are 1

• a development trajectory (emergence path)

• fault detection for “pathological” states

Language: Python‑style pseudocode (easily runnable with minimal edits).

3. Agent implementation

python

from dataclasses import dataclass

@dataclass
class SubitState:
    O: int  # Orientation
    P: int  # Persistence
    I: int  # Intentionality
    R: int  # Reflexivity
    A: int  # Agency
    Op: int # Openness

    def as_tuple(self):
        return (self.O, self.P, self.I, self.R, self.A, self.Op)

    def __str__(self):
        return "".join(str(b) for b in self.as_tuple())


class Agent:
    def __init__(self, state: SubitState):
        self.state = state

    def describe_state(self):
        labels = ["O", "P", "I", "R", "A", "Op"]
        bits = self.state.as_tuple()
        return "State " + str(self.state) + " (" + ", ".join(
            f"{lbl}={b}" for lbl, b in zip(labels, bits)
        ) + ")"

    def handle_request(self, user_message: str) -> str:
        """
        Behavior depends on which capacities are structurally present (bits = 1).
        This is where SUBIT‑64 becomes operational.
        """
        O, P, I, R, A, Op = self.state.as_tuple()

        # If no orientation: agent cannot even parse the request
        if O == 0:
            return "I cannot reliably interpret your request. Please restate it more concretely."

        # If no persistence: agent cannot keep context
        if P == 0:
            return "I can respond, but I will not remember previous parts of this conversation."

        # If no intentionality: agent cannot form its own goals
        if I == 0:
            return (
                "I can follow your instructions step by step, "
                "but I cannot generate a plan or goal structure on my own."
            )

        # If no reflexivity: agent cannot explain its own reasoning
        if R == 0:
            return (
                "I can propose actions and plans, "
                "but I cannot give you a meta‑explanation of why I chose them."
            )

        # If no agency: agent cannot initiate actions
        if A == 0:
            return (
                "I can analyze your situation and suggest options, "
                "but I will not take initiative without your explicit commands."
            )

        # If no openness: agent is closed to new information
        if Op == 0:
            return (
                "I can operate based on what I already know, "
                "but I am not currently integrating new information."
            )

        # Full 111111: all capacities present
        return (
            "I can interpret your request, maintain context, form goals, "
            "reflect on my reasoning, take initiative, and integrate new information."
        )

4. Development trajectory (emergence path)

We model a simple “growth” path:

python

def emergence_trajectory():
    """
    A simple valid emergence trajectory:
    100000 → 110000 → 111000 → 111100 → 111110 → 111111
    """
    states = [
        SubitState(1,0,0,0,0,0),
        SubitState(1,1,0,0,0,0),
        SubitState(1,1,1,0,0,0),
        SubitState(1,1,1,1,0,0),
        SubitState(1,1,1,1,1,0),
        SubitState(1,1,1,1,1,1),
    ]
    return states

Example usage:

python

if __name__ == "__main__":
    traj = emergence_trajectory()
    msg = "Can you help me plan my week?"

    for step, st in enumerate(traj):
        agent = Agent(st)
        print(f"\nStep {step}: {agent.describe_state()}")
        print("User:", msg)
        print("Agent:", agent.handle_request(msg))

This will show how the same request is handled differently as the agent “develops” more capacities.

5. Fault / pathology detection using the full 6‑bit space

Now we use the fact that SUBIT‑64 defines all 64 structural states, not just the 7 on the emergence ladder.

We can mark some states as “pathological” (structurally definable but non‑emergent).

python

def is_emergent_valid(state: SubitState) -> bool:
    """
    MIST dependency: each later feature requires the previous one.
    O <= P <= I <= R <= A <= Op in terms of implication.
    """
    O, P, I, R, A, Op = state.as_tuple()

    if P and not O:  return False
    if I and not P:  return False
    if R and not I:  return False
    if A and not R:  return False
    if Op and not A: return False
    return True


def classify_state(state: SubitState) -> str:
    if is_emergent_valid(state):
        return "emergent‑valid"
    else:
        return "structurally‑defined but non‑emergent (fault / pathology / artificial configuration)"

Example:

python

if __name__ == "__main__":
    # A pathological state: intentionality without orientation
    bad_state = SubitState(0,1,1,0,0,0)
    agent = Agent(bad_state)

    print("\nPathological example:")
    print(agent.describe_state())
    print("Classification:", classify_state(bad_state))
    print("Agent behavior:", agent.handle_request("What should I do tomorrow?"))

This demonstrates:

• the state is structurally representable in SUBIT‑64

• it is not emergent‑valid

• it still yields a meaningful behavioral profile (e.g., “I can’t interpret your request”)

6. What this example shows in practice

This concrete example demonstrates that SUBIT‑64:

• works as a minimal internal state space for an agent

• supports different behaviors depending on which capacities are present

• distinguishes between:

  • emergent‑valid states (developmental ladder)

  • structurally definable but non‑emergent states (faults, pathologies, artificial configs)

• is small enough (6 bits) to scale to thousands of agents in simulation