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Causal Trace Example (Python RTT/Inside)

Purpose#

Demonstrate how RTT‑Inside reconstructs a semantic causal trace for a Python execution sequence. This example shows how declared intent, runtime signals, semantic forms, drift indicators, and invariant checks combine into a single RTT causal object.

Scenario#

A Python function executes across several steps:

  1. Declared intent (source code)
  2. Runtime event (function call)
  3. Semantic form extraction (AST + object graph)
  4. Drift detection (unexpected branch)
  5. Invariant evaluation (candidate invariant emerges)
  6. Final causal stitching

RTT‑Inside converts this into a coherent causal lineage.

Operator Chain#

intent      = INTENT_PY(source_code)
signal      = TIF_PY(runtime_event)
mgmt        = MAN_PY(interpreter_action)
flow        = FFF_PY(execution_step)

form        = PY_FORM_SCAN(ast_node, object_graph)
normalized  = PY_FORM_NORMALIZE(form)
delta       = PY_FORM_COMPARE(normalized, prior_form)

drift       = PY_DRIFT_DETECT(runtime_state, expected_state)
severity    = PY_DRIFT_CLASSIFY(drift, delta)
bounded     = PY_DRIFT_BOUND(severity, normalized)

invariant   = PY_INVARIANT_CHECK(bounded, evidence)
decision    = INVARIANT_REGISTRY_I2(candidate, evidence)

resolved    = CRE_PY(intent, signal, mgmt)
lineage     = CSL_PY([resolved, decision])
time        = CET_PY(lineage)

output      = RTT_PY(time)

Interpretation (Student‑Readable)#

1. Intent#

INTENT_PY captures the semantic intent encoded in the source code.

2. Runtime Signals#

TIF_PY interprets runtime events (calls, returns, exceptions).

3. Semantic Forms#

PY_FORM_SCAN extracts a semantic form from AST + object graph.

4. Drift Detection#

PY_DRIFT_DETECT identifies divergence between expected and actual execution.

5. Invariant Evaluation#

PY_INVARIANT_CHECK determines whether a semantic pattern is stable.

6. Registry Decision#

INVARIANT_REGISTRY_I2 accepts, sandboxes, or rejects the candidate invariant.

7. Causal Stitching#

RTT stitches all signals into a single causal lineage.

Result#

RTT produces a semantic causal object describing:

  • how the function executed
  • what semantic forms emerged
  • where drift occurred
  • how invariants were evaluated
  • how the final state was reached

This is the canonical Python causal‑trace example used for teaching semantic reasoning and execution‑aware causality.

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