MAXTOKEN A Unified Framework for Unbounded Output Generation and Repository-Scale Code Understanding
Skip to main
You are using an outdated browser. Please upgrade your browser to improve your experience.
New blog post on the May 13–15 incident. We sincerely apologize for the incident, the disruption it caused, and any concern it raised.
Published May 24, 2026
| Version v1
Preprint
Open
MAXTOKEN A Unified Framework for Unbounded Output Generation and Repository-Scale Code Understanding
Authors/Creators
choukri
Description
Large Language Models (LLMs) have achieved remarkable progress in natural language<br>and code generation, yet remain fundamentally constrained by two interrelated limitations: output token caps (typically 8k–32k tokens) and quadratic attention complexity<br>that makes long-range reasoning economically prohibitive. Existing solutions—chunking,<br>retrieval-augmented generation, and long-context transformers—each address only a subset<br>of the problem while introducing new failure modes such as information loss across chunk<br>boundaries, degraded retrieval quality, or unsustainable memory costs.<br>We introduce MAXTOKEN, a complete framework for building AI systems that maximize token output to users while maintaining coherence, economic viability, and acceptable<br>latency. The framework comprises seven interlocking layers: (1) a hybrid SSM-Transformer<br>architecture combining Mamba-3’s linear-time sequence processing with sparse attention;<br>(2) Infini-Attention for unbounded input via compressive memory; (3) a Generative State<br>Engine (GSE) with hierarchical memory enabling unbounded output; (4) adaptive speculative decoding; (5) hierarchical KV cache management; (6) a three-objective training protocol<br>for long-range consistency; and (7) an application-level session protocol.<br>We extend this to MAXTOKEN-Code, introducing a Logical State Engine (LSE),<br>Syntax-Weighted Infini-Attention (SWIA), and a Logical Consistency Verification (LCV)<br>module. We provide rigorous mathematical proofs for all key claims, with each theorem<br>scoped precisely to its stated assumptions.
Files
MAXTOKEN_v4_Corrected.pdf
Files<br>(320.2 kB)
Name<br>Size
Download all
MAXTOKEN_v4_Corrected.pdf
md5:23b93a654433a34db62006fec65d56cc
320.2 kB
Preview
Download
Additional details
References
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS), 30.
Views
Downloads
Show more details
All versions<br>This version
Views
Total views
Downloads
Total downloads
Data volume
Total data volume
0 Bytes<br>0 Bytes
More info on how stats are collected....
Versions
External resources
Indexed in
OpenAIRE
Communities
Keywords and subjects
Keywords
Large Language Models, Unbounded Generation, State Space Models, InfiniAttention, Repository-Scale Code Understanding.
Details
DOI
DOI Badge
DOI
10.5281/zenodo.20360523
Markdown
[](https://doi.org/10.5281/zenodo.20360523)
reStructuredText
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.20360523.svg<br>:target: https://doi.org/10.5281/zenodo.20360523
HTML
Image URL
https://zenodo.org/badge/DOI/10.5281/zenodo.20360523.svg
Target URL
https://doi.org/10.5281/zenodo.20360523
Resource type<br>Preprint
Publisher<br>Zenodo
Languages
English
Rights
License
Creative Commons Attribution 4.0 International
The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited.
Read more
Citation
Export
Technical metadata
Created
May 24, 2026
Modified
May 24, 2026
Jump up
This site uses cookies. Find out more on how we use cookies
Accept all cookies<br>Accept only essential cookies