The fundamental bottleneck of modern LLMs is context length degradation. As the sequence of tokens grows, the attention mechanism's computational cost squares, and its ability to retrieve precise information from the distant past degrades exponentially.
The Mechanism of Flux
Chronometric Flux Gating (CFG) introduces a novel temporal routing mechanism. Rather than maintaining a static, ever-expanding attention matrix, CFG categorizes information into distinct "temporal flux" states. Information is gated based on its enduring relevance, allowing the model to compress transient dialogue while preserving core factual anchors in a highly condensed, permanent memory state.
Immediate conversational context, decaying smoothly as temporal distance increases.
High-value factual data compressed and gated into a non-decaying representational state.
This allows for effectively infinite context windows without the prohibitive memory footprint, enabling models to maintain coherent narratives over millions of tokens.