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Level 0: The Resonance Manifesto

Abstract

Contemporary Artificial Intelligence is built on synchronous processing, clock cycles, global orchestration, and continuous computation. These constraints are not laws of physics; they are historical artifacts of digital engineering.

The Semantic Event Protocol (SEP) proposes a different foundation.

Note: "Resonance" is the project name. The protocol itself is called the Semantic Event Protocol (SEP). Instead of computing at fixed intervals, devices compute only when meaning changes. Instead of transmitting raw data, nodes exchange semantic deltas. Instead of relying on centralized models, each device maintains local cognitive autonomy. The result is a distributed intelligent mesh where silence is the default, and computation occurs only at the emergence of meaningful events.

This approach has been demonstrated in small-scale controlled experiments (single author, no external replication). The findings suggest promising directions, but require independent validation and scaling before deployment.

1. Introduction​

Modern computing is time-driven: CPUs, GPUs, and TPUs execute operations every cycle regardless of information value. Neural networks recompute entire layers even when activations are silence-dominant. Sensors emit redundant frames. Distributed systems depend on periodic pings, heartbeats, and synchronization.

This architecture is incompatible with:

  • Planetary-scale edge intelligence
  • Privacy-by-default device ecosystems
  • Extreme energy constraints
  • Local-first autonomy
  • Responsive systems that awaken only to relevant change

The Semantic Event Protocol (SEP) proposes a different computation model based on one axiom: intelligence emerges from changes in meaning, not from the passage of time.

Preliminary experiments suggest this approach may be feasible with current technology, though significant scaling and validation work remains.

2. Core Axiom​

Intelligence is triggered by meaning, not by time.

Nodes do not compute because a clock ticks. Nodes compute because something changes in the semantic space.

Computation becomes: Event-driven, Semantic, Asynchronous, Distributed.

The Paradigm Shift​

3. The Breakthrough: Hyperdimensional Computing​

After extensive research spanning phases M2.5 through M3, we have discovered that Hyperdimensional Computing (HDC) provides the mathematical foundation for semantic-first distributed intelligence.

What is HDC?​

HDC operates in ultra-high-dimensional spaces (10,000 dimensions) using ternary vectors 1 with 70% sparsity. This enables:

  1. Extreme Compression: 32× compression (17.5MB → 271KB) while preserving semantic meaning
  2. Cross-Architecture Knowledge Transfer: 93% efficiency transferring knowledge between completely different model architectures (DistilBERT → GPT-2)
  3. Perfect Compositional Generalization: 100% accuracy on unseen combinations
  4. Efficient Distributed Training: Multi-node synchronization via semantic packets

Why HDC Works for Resonance​

HDC provides three critical properties:

  1. Semantic Preservation: Even with extreme compression, the meaning is preserved
  2. Architecture Independence: Knowledge can transfer between different model types
  3. Composability: Semantic vectors can be combined algebraically to create new meanings

4. Protocol Invariants​

Invariant 1: Silence is the Default State​

Nodes remain inactive unless a semantic event occurs. No periodic heartbeats are required at the semantic level. Silence is meaningful and expected.

Demonstrated in controlled experiments: HDC compression reduced synchronization traffic from 17.5MB to 271KB per round in our 2-node test setup.

Invariant 2: Events Carry Meaning, Not Raw Data​

The fundamental unit is the Semantic Event:

E = (context, Δmeaning, confidence, provenance)

Events communicate change in semantic space, not raw sensor outputs or model states.

Observed in our benchmarks: HDC semantic packets achieved 93% cross-architecture knowledge transfer efficiency on SST-2 sentiment task.

Invariant 3: Local Cognitive Autonomy​

Each node maintains its private semantic embedding space. Local cognitive autonomy does not require shared embeddings or centralized models.

Demonstrated: Ternary HDC encoders operated locally with 70% sparsity in our experiments, suggesting potential for device-level autonomy.

Invariant 4: Semantic Distance and Threshold​

A semantic event MUST be emitted when the distance d between the current state and the last transmitted state exceeds a threshold θ:

d(M_t, M_{t-1}) > θ

Observed: HDC clustering achieved 4.66% better coverage than random sampling in our synthetic composition task.

Invariant 5: Semantic Deltas​

Nodes exchange only changes in meaning, not raw input or full state.

Observed: 32× compression ratio in our LoRA weight quantization experiment suggests semantic deltas may be more efficient than raw state transfer.

Invariant 6: Trust is Provenance​

Provenance metadata provides local confidence. There is no global root of trust (Authority).

Implementation: Each semantic packet includes provenance metadata tracking the origin and transformation history.

5. The Resonance Stack​

The architecture is layered to separate physical sensing from cognitive reasoning.

New in this revision: Layers L1 and L3 now explicitly use HDC for semantic encoding and compression, based on proven experimental results.

6. Semantic Event Lifecycle​

The lifecycle of information in the system follows a strict reduction path:

  1. Sensory Change (Δs): Detected by DVS/Audio.
  2. Semantic Shift (Δσ): Crossing the threshold.
  3. HDC Encoding (Δμ): 10,000-d ternary vector encoding.
  4. Event Creation (E): Packaging the delta with provenance.
  5. Compression & Sharing (E↑): 32× compression and propagation to the mesh.

7. Topology: The Quiet Mesh​

The network topology is dynamic and sparse. Nodes form a mesh where connections are maintained, but traffic is zero until a meaningful event propagates.

Demonstrated: In our 2-node setup, distributed training converged with 271KB per synchronization round.

8. Experimental Validation​

The Resonance Protocol is not a theoretical exercise. Every core claim has been validated through systematic experimentation:

M2.5 Series: Data Efficiency​

  • M2.5a: HDC-based data curation competitive with Sentence Transformers
  • M2.5b: Curriculum learning: HDC-guided sharp curriculum achieves 100% accuracy

M2.6: Compositional Generalization​

  • Result: 100% accuracy on unseen attribute combinations
  • Significance: HDC enables perfect compositional reasoning

M3 Series: Distributed Intelligence​

  • M3a: Raw distributed training (2 nodes, 17.5MB/round)
  • M3b: HDC compression (32× reduction to 271KB/round)
  • M3c′: Cross-architecture knowledge transfer (93% efficiency, DistilBERT → GPT-2)

For detailed experimental results, see the Research Documentation section.

9. Conclusion​

The Semantic Event Protocol (SEP) proposes a semantic-first, event-driven architecture for distributed intelligence.

What we have demonstrated in controlled, small-scale experiments:

  • 32× compression of LoRA weights via ternary HDC quantization (2-node setup, SST-2 task)
  • 93% cross-architecture knowledge transfer efficiency (DistilBERT → GPT-2, SST-2 sentiment)
  • 100% compositional generalization (synthetic attribute-object task, HDC vs 21% for small transformer)
  • Distributed training convergence via 271KB semantic packets (2 nodes, Alpaca subset)

Limitations:

  • Single author, no external replication
  • Synthetic or narrow datasets (SST-2, small Alpaca subset, toy composition tasks)
  • Small scale (2-10 nodes in simulation, not production edge devices)
  • No validation on real-world safety-critical systems
  • No hardware proof-of-concept for neuromorphic/memristor integration

These findings suggest a possible path toward distributed semantic computing, but significant research, scaling, and independent validation are required before production deployment.

The clock stops. The resonance begins.