MAXTOKEN A Unified Framework for Unbounded Output Generation and Repository-Scale Code Understanding

Large Language Models (LLMs) have achieved remarkable progress in natural languageand code generation, yet remain fundamentally constrained by two interrelated limitations: output token caps (typically 8k–32k tokens) and quadratic attention complexitythat makes long-range reasoning economically prohibitive. Existing solutions—chunking,retrieval-augmented generation, and long-context transformers—each address only a subsetof the problem while introducing new failure modes such as information loss across chunkboundaries, 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 acceptablelatency. The framework comprises seven interlocking layers: (1) a hybrid SSM-Transformerarchitecture combining Mamba-3’s linear-time sequence processing with sparse attention;(2) Infini-Attention for unbounded input via compressive memory; (3) a Generative StateEngine (GSE) with hierarchical memory enabling unbounded output; (4) adaptive speculative decoding; (5) hierarchical KV cache management; (6) a three-objective training protocolfor 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 theoremscoped precisely to its stated assumptions.