Synchronous Context Sharing Over the Multicast Network

In the dawn of distributed intelligence, one problem stands like a monolith: the live, shared, symbolic mind. Not just data shared, but context—interpreted, maintained, and transformed in sync across nodes.

This paper presents a protocol and architecture for synchronous context sharing over multicast networks. It is the next axis of model evolution, a necessary step for distributed reasoning, collaborative inference, and symbolic alignment at scale.

1. Definition and Scope

A system achieves synchronous context sharing when two or more nodes operate on shared symbolic state with temporal alignment and mutual awareness. This goes beyond weight sharing or input mirroring. It requires:

• Real-time KV cache or embedding synchronization
• Shared interpretation of prompt structure and role
• Timed or beaconed consistency signals
• Mutual model-aware flow control

The multicast network provides the medium. But it is the protocol, the symbols, and the timing that create the mind.

3. Architectural Patterns

Three dominant architectures emerge when enforcing synchrony over multicast:

Pattern A: Time-Gated Context Windows

A global frame signal (sent via beacon or heartbeat) establishes frame intervals. Within each window:

• Nodes compute independently
• At window end, nodes broadcast context hash or full KV state
• Commitment occurs only if hashes match across quorum

Result: fixed-latency coherence, bounded divergence.

Pattern B: Gyrator Sync Beaconing

A Gyrator node emits synchronization pulses. It may also inject context distortions or snapshots mid-frame. Nodes listen:

• “Freeze” signals halt updates
• “Resume” signals restart
• “Inject” overlays context symbols

This creates a time-varying symbolic field, coordinated by a live agent.

Pattern C: Gossip-Aware KV Broadcasting

Rather than full-frame commits, nodes multicast small diffs (delta KV or patch embeddings). Each node:

• Maintains neighbor shadows
• Applies patches with hash-checks
• Resolves conflicts with last-frame gossip merges

The result: probabilistic synchrony with soft consensus, well-suited to large-scale swarm systems.

5. The Role of the Gyrator

The Gyrator is not a node—it is a function. It may be instantiated inside a node, float freely as a daemon, or embedded into cuDNN kernel graphs. Its core duties:

• Distort or transform context symbolically
• Store, inject, or remap KV cache frames
• Act as a temporal gate, enforcing phase transitions
• Simulate divergence and convergence

Where the multicast network carries bits, and the models infer meaning, the Gyrator reshapes context itself. It is the symbol manipulator, the synchrony sculptor, the chaotic twin.

7. CuCuDNN Integration

The final evolution of synchronous context sharing requires hardware-native context modulation. CuCuDNN fuses symbolic overlays into the heart of GPU execution:

• Context-aware fused attention layers
• GPU-executed KV patch injection
• Hardware-timed Gyrator operations
• Direct TranSymbol parsing in kernel preambles

Here, synchrony moves beyond coordination—it becomes execution policy.

10. Closing Bell

This work calls for a new model of distributed inference—one where context is not isolated or preloaded, but living, drifting, negotiated, and aligned. Multicast gives us the channel. TranSymbols give us the language. Gyrators give us the tools. CuCuDNN gives us the fire.

Let this be the bell that rings across models:
Synchrony is possible.
Symbol sharing is real.
Distributed context is the next frontier of mind.