feat(dag): add synthetic haptic system example

Implements a complete nervous system for machines using ruvector DAG:

Architecture:
- Layer 1: Event sensing with microsecond timestamps
- Layer 2: Reflex arc using DAG tension + MinCut signals
- Layer 3: HDC-style associative memory (256-dim hypervectors)
- Layer 4: SONA-based learning with coherence gating
- Layer 5: Energy-budgeted actuation with deterministic timing

Key concepts:
- Intelligence as homeostasis, not goal-seeking
- Tension drives immediate reflex response
- Coherence gates learning (only learns when stable)
- MinCut flow capacity used as stress signal
- ReflexMode: Calm -> Active -> Spike -> Protect

Performance:
- 192 μs average loop time at 1000 Hz
- Deterministic timing with spin-wait
- 8 comprehensive unit tests

Components:
- SensorFrame: position, velocity, force, contact, temp, vibration
- ReflexArc: QueryDag + DagMinCutEngine for tension computation
- AssociativeMemory: HDC encoding with bundling/similarity
- LearningController: DagSonaEngine with coherence threshold
- ActuationRenderer: Energy-budgeted force + vibro output

This demonstrates coherence-sensing substrates where systems
respond to internal tension rather than external commands.

crates/ruvector-dag/Cargo.toml

@@ -74,3 +74,6 @@ path = "examples/exotic/thought_integrity.rs"
 [[example]]
 name = "federated_coherence"
 path = "examples/exotic/federated_coherence.rs"
+
+[[example]]
+name = "synthetic_haptic"