πŸ“„
Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference paper by Han Zhao, Min Zhang, Wei Zhao, Pengxiang Ding, Siteng Huang, Donglin Wang
⭐ 57.7
πŸ“„
Paper

Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference

by Han Zhao, Min Zhang, Wei Zhao, Pengxiang Ding, Siteng Huang, Donglin Wang 2403.14520
Free2AITools Nexus Index
57.7
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 87
P: Popularity 64
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

In recent years, applying multi-modal large language models (MLLMs) in various fields has achieved remarkable success. However, as the foundation model for many downstream tasks, MLLMs comprise the well-known Transformer network, which has a less efficient quadratic computation complexity. In this study, we introduce Cobra, a multi-modal large-scale language model built upon a state-space model, which has demonstrated significant potential in efficiently handling long sequences with fast infe...

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Semantic Scholar 108 Citations
Paper Information Summary
Entity Passport
Registry ID 2403.14520
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_2403_14520,
  author = {Han Zhao, Min Zhang, Wei Zhao, Pengxiang Ding, Siteng Huang, Donglin Wang},
  title = {Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2403.14520}},
  note = {Accessed via Free2AITools.}
}
APA Style
Han Zhao, Min Zhang, Wei Zhao, Pengxiang Ding, Siteng Huang, Donglin Wang. (2026). Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference [Paper]. Free2AITools. https://arxiv.org/abs/2403.14520

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 87
Popularity (P) 64
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference: Authority (A:87), Popularity (P:64), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
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πŸ“ Executive Summary

"In recent years, applying multi-modal large language models (MLLMs) in various fields has achieved remarkable success. However, as the foundation model for many downstream tasks, MLLMs comprise the well-known Transformer network, which has a less efficient quadratic computation complexity. In this study, we introduce Cobra, a multi-modal large-scale language model built upon a state-space model, which has demonstrated significant potential in efficiently handling long sequences with fast infe..."

❝ Cite Node 

@article{Zhao2026Cobra:,
  title={Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference},
  author={Han Zhao and Min Zhang and Wei Zhao and Pengxiang Ding and Siteng Huang and Donglin Wang},
  journal={arXiv preprint arXiv:2403.14520},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Han Zhao Min Zhang Wei Zhao Pengxiang Ding Siteng Huang Donglin Wang

πŸ”— Full Paper

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Read the full paper on arXiv

πŸ“Š Research Signals

πŸ“ˆ108CitationsSemantic Scholar
πŸ›οΈ87AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈinfrastructure opsField

🏷️ Research Topics

transformer architecture
πŸ“¦Data Source: semantic_scholar
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πŸ†” Identity & Source

id
2403.14520
slug
2403.14520
source
semantic_scholar
author
Han Zhao, Min Zhang, Wei Zhao, Pengxiang Ding, Siteng Huang, Donglin Wang
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

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citations
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