View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (26)

[1]
Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism
2025Aviv Bick, Eric P. Xing et al.
[2]
M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models
2025Junxiong Wang, Wen-Ding Li et al.
[3]
Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners
2025Daniele Paliotta, Junxiong Wang et al.
[4]
Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing
2025Aviv Bick, Tobias Katsch et al.
[5]
Gated Delta Networks: Improving Mamba2 with Delta Rule
2024Songlin Yang, Jan Kautz et al.
[6]
The Mamba in the Llama: Distilling and Accelerating Hybrid Models
2024Junxiong Wang, Daniele Paliotta et al.
[7]
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models
2024Aviv Bick, Kevin Y. Li et al.
[8]
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
2024Tri Dao, Albert Gu
[9]
Zamba: A Compact 7B SSM Hybrid Model
2024Paolo Glorioso, Quentin Anthony et al.
[10]
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
2024Shengding Hu, Yuge Tu et al.
[11]
RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval
2024Kaiyue Wen, Xingyu Dang et al.
[12]
Simple linear attention language models balance the recall-throughput tradeoff
2024Simran Arora, Sabri Eyuboglu et al.
[13]
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
2024Jongho Park, Jaeseung Park et al.
[14]
Repeat After Me: Transformers are Better than State Space Models at Copying
2024Samy Jelassi, David Brandfonbrener et al.
[15]
Zoology: Measuring and Improving Recall in Efficient Language Models
2023Simran Arora, Sabri Eyuboglu et al.
[16]
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
2022Tri Dao, Daniel Y. Fu et al.
[17]
Training Verifiers to Solve Math Word Problems
2021K. Cobbe, Vineet Kosaraju et al.
[18]
Measuring Massive Multitask Language Understanding
2020Dan Hendrycks, Collin Burns et al.
[19]
Data Movement Is All You Need: A Case Study on Optimizing Transformers
2020A. Ivanov, Nikoli Dryden et al.
[20]
PIQA: Reasoning about Physical Commonsense in Natural Language
2019Yonatan Bisk, Rowan Zellers et al.

Showing 20 of 26 references

Founder's Pitch

"Transform Transformer models into memory-efficient hybrids that maintain retrieval capabilities, using fewer attention heads."

Efficient TransformersScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

1/4 signals

2.5

Series A Potential

3/4 signals

7.5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/11/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.