feat(mincut): Cognitive Frontier Examples + Graph Integrity Vision

[ruvnet]

Summary

This PR implements the Cognitive Frontier research specifications and establishes the architectural vision for Graph Integrity as a First-Class Signal.

New Examples

• Temporal Hypergraph (911 lines) — Time-varying hyperedges with causal constraint inference
• Federated Strange Loops (920 lines) — Multi-system mutual observation with spike-based consensus

Novel Research Contributions

1. Spike-Based Distributed Consensus — Using neural synchrony instead of message passing
2. Emergent Role Specialization — Clusters naturally specialize based on mutual observation
3. Hierarchical Self-Organization — Leadership emerges from strange loop dynamics
4. Collective Meta-Cognition — Federation-level self-awareness through Level 3 neurons

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Graph Integrity Vision

This work establishes min-cut as a continuous structural integrity signal for self-healing systems.

The Five Layers

Layer	What It Enables	Where It Lands
Deterministic Exact Dynamic MinCut	Early warning before failures show in outputs; auditable witnesses	RuVector/AgentDB retrieval graphs, Claude Flow agent gating
Dynamic Contractions & Sparsifiers	Massive scale monitoring with O(n log n) edges preserving cut structure	Similarity graphs, service meshes, swarm coordination
Directed Sparsifiers & Cuts	Integrity for causal pipelines; deadlock/fan-in/fan-out detection	Agent pipelines, distributed call graphs, event workflows
Spectral Sparsification	Complementary stress signal; catches gradual weakening before snap	Graph embeddings, anomaly detection, stable learning
Dynamic Connectivity Cores	Cheap trigger layer; only wake heavy analysis when core changes	Runtime scaffolding for always-on graph maintenance

The Integrity OODA Loop

┌─────────────────────────────────────────────────────────────────┐
│                    GRAPH INTEGRITY OODA LOOP                     │
├─────────────────────────────────────────────────────────────────┤
│  OBSERVE: Maintain compact evolving graph core                   │
│  • Dynamic connectivity (O(log n) per update)                    │
│  • MinCut sparsifier (O(n log n) edges)                          │
│  • Incremental boundary tracking                                 │
├─────────────────────────────────────────────────────────────────┤
│  ORIENT: Compute two complementary signals                       │
│  • Cut fragility: λ (exact), boundary edges, witness            │
│  • Spectral stress: λ₂ (algebraic connectivity)                 │
├─────────────────────────────────────────────────────────────────┤
│  DECIDE: Apply policy based on (λ_cut, λ_spectral, Δλ/Δt)       │
│  • λ > threshold: normal operation                               │
│  • λ dropping: slow writes, increase monitoring                  │
│  • λ critical: freeze risky ops, isolate subgraph                │
├─────────────────────────────────────────────────────────────────┤
│  ACT: Execute recovery, then cautiously expand                   │
│  • Shed connections causing fragility                            │
│  • Reroute through stronger paths                                │
│  • Gradually restore scope with hysteresis                       │
└─────────────────────────────────────────────────────────────────┘

What This Changes

Before (Traditional)	After (Graph Integrity)
Watch outputs → React when broken	Watch structure → Act before degradation
Human investigates root cause	Automatic witness explains everything
Manual mitigation, hope it works	Automatic scope reduction, graceful degradation
Catastrophic collapse	Systems degrade gracefully, then recover

Day-to-Day Outcomes

• ✅ Fewer cascading failures
• ✅ Faster recovery without human intervention
• ✅ Less compute spent on monitoring (sparsifiers)
• ✅ Decisions that are explainable and testable (witnesses)
• ✅ Systems that degrade gracefully instead of collapsing

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Implementation Details

Temporal Hypergraph (5 Phases)

Phase	Components
Phase 1	TemporalInterval, TemporalHyperedge, TimeSeries, Allen's 13 interval relations
Phase 2	TemporalIndex, TemporalHypergraphDB with time-range queries
Phase 3	CausalLearner with STDP-like spike-timing learning
Phase 4	TemporalQuery (AT TIME, DURING, CAUSES, Allen queries)
Phase 5	TemporalMinCut, CausalMinCut for intervention planning

Federated Strange Loops (4 Phases)

Phase	Components
Phase 1	ClusterObservation, ClusterRegistry, ObservationProtocol
Phase 2	FederationMetaNeuron (Level 3), CrossClusterInfluence
Phase 3	SpikeConsensus (novel!), pairwise synchrony computation
Phase 4	PatternDetector with 5 EmergentPattern types

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Test Plan

• cargo build --example temporal_hypergraph compiles
• cargo build --example federated_loops compiles
• cargo run --release --example temporal_hypergraph — All 5 phases execute
• cargo run --release --example federated_loops — All 4 phases execute
• Temporal MinCut evolution tracking works
• Causal inference from spike timing works
• Spike-based consensus reaches agreement
• Emergent pattern detection identifies convergence/specialization/hierarchy

Run Commands

cd examples/mincut && cargo run --release --example temporal_hypergraph
cd examples/mincut && cargo run --release --example federated_loops

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References

• El-Hayek, Henzinger, Li (Dec 2025) — arXiv:2512.13105 — Deterministic exact dynamic min-cut
• Hofstadter (1979) — Gödel, Escher, Bach — Strange loops
• Allen (1983) — Interval algebra
• Spielman-Teng — Spectral sparsification
• OODA Loop — Boyd's military decision cycle

🤖 Generated with Claude Code