# AMR Decision Substrate Demo

This demo is robotics-adjacent, not a robotics stack.

It uses simulated abstract situation records to show CORE as a decision and
accountability substrate around a bounded AMR-style proceed / stop / refuse
choice. The inputs are not camera, LiDAR, odometry, SLAM, localization, motor,
or fleet-control data.

Claims-ledger framing: this is a preparation artifact over simulated records.
It is not deployment readiness, not perception, not motion planning, and not
motor control. The demo proves only its local trace/refusal/replay surface over
these fixtures. It does not imply a CORE expert domain, a robotics capability
claim, or working vision/motor. Per the ledger, text is the active capability;
audio is substrate with the gate CLOSED; vision and motor are proposed only.

What is real CORE here:

- `ChatRuntime`
- `CognitiveTurnPipeline.run(...)`
- recognition-side typed refusal propagation
- `CognitiveTurnResult.trace_hash`
- CORE Trace Protocol canonical JSONL events
- `verify_chain(...)` replay validation

What is simulated:

- the AMR situation record
- the tiny policy reducer that maps already-abstracted facts to
  `PROCEED`, `STOP`, or `REFUSE`

The demo refuses under-determined input instead of guessing. It also runs the
same scenarios twice through fresh runtime instances and asserts byte-identical
trace JSONL.

Run from the repository root:

```bash
UV_PROJECT_ENVIRONMENT=/tmp/core-amr-decision-uv uv run python demos/amr_decision_substrate/run_demo.py
```

Artifacts are written to:

```text
demos/amr_decision_substrate/out/
```

The important artifact is `summary.json`; `trace_a.jsonl` and `trace_b.jsonl`
are the two replay runs that must match byte-for-byte.