System: TranSymbolics / Gyrator Plug-in Framework
While a transformer model provides the raw intelligence, a robust conversational system requires a dedicated engine to manage the lifecycle of its context. The Gyrator Framework is this engine. It sits between the application and the model, acting as a symbolic gatekeeper responsible for the integrity, security, provenance, and transportability of conversational state. It ensures that context—the volatile "shadow" of the model's recent thought—is captured, preserved, and restored with perfect fidelity.
The framework is built on five foundational pillars that define its function. These pillars emerged from solving practical challenges, from failed network transfers to the need for reversible data transformations.
Principle: Context must be treated as an atomic, indivisible unit. Losing a single token or KV tensor breaks the chain of memory. A partial context is a corrupted context.
Implementation: The framework enforces this by bundling all components of state into a coherent package. A complete save operation by the Gyrator is not just the KV tensors; it's a "closed symbolic loop" that includes:
config.txt: Metadata containing model parameters, shapes, and dtypes to prevent mismatches on restore.keylog.txt: A log of provenance, recording the session ID, agent, or prompt that originated the context.Principle: Any transformation applied to the context for security or compression must be perfectly reversible. The original state must be recoverable without data loss.
Implementation: The Gyrator includes specific functions for reversible transformations. When a function like gyrobf() applies an XOR obfuscation, it is mandated to document the exact operation in an `obfchain.txt` file. The `gyrodeobf()` function then reads this chain in reverse to perfectly reconstruct the original state, treating context as a traceable artifact, not an opaque blob.
Principle: Context must have a clear origin and identity. In multi-agent or multi-session systems, knowing where a piece of context came from is essential for correct interpretation and routing.
Implementation: This is the primary role of `keylog.txt`. By stamping every saved context with a unique identifier, the Gyrator turns anonymous data into an accountable symbolic history, enabling advanced features like multi-agent conversation merging and session-specific memory.
Principle: Contexts should not be monolithic silos. The framework must support the ability to layer or merge multiple contexts, like combining chapters of a symbolic narrative.
Implementation: The `config.txt` metadata includes tags to indicate context type (e.g., "base" or "overlay"). A dedicated `contextmerge()` function uses these tags to perform intelligent, shape-aware merging, allowing, for example, a short-term conversational memory to be overlaid onto a long-term personality base.
Principle: Context is not bound to a single process or machine. It must be able to travel safely and efficiently across networks or between systems while keeping its meaning intact.
Implementation: To solve failures with protocols like UDP, the Gyrator framework adds a transport layer that includes packet framing and sequence IDs. It also employs functions like `gyrcmp()` for CuPy-based delta and XOR compression, making the context payload byte-safe, compact, and resilient for network transit.
These five pillars ensure that the Gyrator's handling of context is Integral, Reversible, Traceable, Composable, and Transportable. They form the backbone of TranSymbolics context logic, enabling symbolic AI systems to work reliably across time, memory, agents, and security boundaries.