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:

Declared intent (source code)
Runtime event (function call)
Semantic form extraction (AST + object graph)
Drift detection (unexpected branch)
Invariant evaluation (candidate invariant emerges)
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.