https://arxiv.org/abs/2506.01963

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2506.01963
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Machine Learning

arXiv:2506.01963 (cs)
[Submitted on 9 May 2025]

Title:Breaking Quadratic Barriers: A Non-Attention LLM for Ultra-Long
Context Horizons

Authors:Andrew Kiruluta, Preethi Raju, Priscilla Burity
View a PDF of the paper titled Breaking Quadratic Barriers: A
Non-Attention LLM for Ultra-Long Context Horizons, by Andrew Kiruluta
and 1 other authors
View PDF HTML (experimental)

    Abstract:We present a novel non attention based architecture for
    large language models (LLMs) that efficiently handles very long
    context windows, on the order of hundreds of thousands to
    potentially millions of tokens. Unlike traditional Transformer
    designs, which suffer from quadratic memory and computation
    overload due to the nature of the self attention mechanism, our
    model avoids token to token attention entirely. Instead, it
    combines the following complementary components: State Space
    blocks (inspired by S4) that learn continuous time convolution
    kernels and scale near linearly with sequence length, Multi
    Resolution Convolution layers that capture local context at
    different dilation levels, a lightweight Recurrent Supervisor to
    maintain a global hidden state across sequential chunks, and
    Retrieval Augmented External Memory that stores and retrieves
    high-level chunk embeddings without reintroducing quadratic
    operations.

Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as:  arXiv:2506.01963 [cs.LG]
          (or arXiv:2506.01963v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2506.01963
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Andrew Kiruluta [view email]
[v1] Fri, 9 May 2025 00:25:46 UTC (175 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Breaking Quadratic Barriers: A
    Non-Attention LLM for Ultra-Long Context Horizons, by Andrew
    Kiruluta and 1 other authors
  * View PDF
  * HTML (experimental)
  * TeX Source
  * Other Formats

view license
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2025-06
Change to browse by:
cs
cs.CL

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Reddit logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] alphaXiv Toggle
alphaXiv (What is alphaXiv?)
[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] GotitPub Toggle
Gotit.pub (What is GotitPub?)
[ ] Huggingface Toggle
Hugging Face (What is Huggingface?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
[ ] Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)
[ ] IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack