Description

Large Language Models (LLMs) have achieved remarkable progress in natural language
and code generation, yet remain fundamentally constrained by two interrelated limitations: output token caps (typically 8k–32k tokens) and quadratic attention complexity
that makes long-range reasoning economically prohibitive. Existing solutions—chunking,
retrieval-augmented generation, and long-context transformers—each address only a subset
of the problem while introducing new failure modes such as information loss across chunk
boundaries, degraded retrieval quality, or unsustainable memory costs.
We introduce MAXTOKEN, a complete framework for building AI systems that maximize token output to users while maintaining coherence, economic viability, and acceptable
latency. The framework comprises seven interlocking layers: (1) a hybrid SSM-Transformer
architecture combining Mamba-3’s linear-time sequence processing with sparse attention;
(2) Infini-Attention for unbounded input via compressive memory; (3) a Generative State
Engine (GSE) with hierarchical memory enabling unbounded output; (4) adaptive speculative decoding; (5) hierarchical KV cache management; (6) a three-objective training protocol
for long-range consistency; and (7) an application-level session protocol.
We extend this to MAXTOKEN-Code, introducing a Logical State Engine (LSE),
Syntax-Weighted Infini-Attention (SWIA), and a Logical Consistency Verification (LCV)
module. We provide rigorous mathematical proofs for all key claims, with each theorem
scoped precisely to its stated assumptions.

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