MAXTOKEN framework introduces hybrid architecture and hierarchical memory systems to extend AI model output beyond typical token limits

Researchers present MAXTOKEN, a framework comprising seven layers designed to maximize token output while maintaining coherence and economic viability. The framework combines a hybrid SSM-Transformer architecture (Mamba-3's linear-time sequence processing with sparse attention), Infini-Attention for unbounded input via compressive memory, and a Generative State Engine (GSE) enabling unbounded output.

The framework addresses limitations of existing solutions like chunking and retrieval-augmented generation by integrating adaptive speculative decoding, hierarchical KV cache management (a memory optimization technique), and a three-objective training protocol for long-range consistency at the system level.

An extension called MAXTOKEN-Code introduces specialized components for code generation: a Logical State Engine (LSE), Syntax-Weighted Infini-Attention (SWIA), and a Logical Consistency Verification (LCV) module. The work includes mathematical proofs for key claims, each scoped to stated assumptions.