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Latest comment: 11 days ago by Alexandra Goncharik in topic COI edit request: updates in history and lead (October 2026)

Reference: sources to demonstrate notability

[edit]
Latest comment: 1 month ago1 comment1 person in discussion

Just for reference, these are the sources used to demonstrate the notability of the company in the AfC draft (WP:THREE):

Best free sources:

  1. ScienceNews — analysis of Liquid AI's core technology, including independent commentary from Harvard and Intel.
  2. WIRED — technical analysis with commentary from an OpenAI researcher.
  3. BBC Science Focus — includes substantial commentary from independent computer scientist Peter Bentley.

Best paywalled sources:

  1. The Information — coverage featuring unaffiliated machine-learning experts, including criticism and no company quotes.
  2. Boston Business Journal — analysis of Liquid AI's business model from an independent economist.
  3. Boston Globe (2024) — coverage featuring an expert from Northeastern University.

Alexandra Goncharik -sms- 13:31, 10 October 2026 (UTC)Reply

COI edit request: updates in history and lead (October 2026)

[edit]
Latest comment: 11 days ago2 comments1 person in discussion

Hello! Due to my COI status, I cannot add this information directly, so I'd be grateful if an independent editor could review it and update the lead and the History section.

It cites five new sources to verify factual details (none of them would establish notability, but it was already established during the AfC review with other sources):

  • three primary sources (two company's technical papers on arXiv, used per WP:ABOUTSELF + one blog post by AMD, an investor and technology partner of the company);
  • two news articles (by The Next Web and VentureBeat).

Proposed changes are shown in green and underlined:

Lead

[edit]
Preview of the proposed update (the new sources are #2,7-8-9)

Liquid AI is an American artificial intelligence company, anspun off from MIT spin-off, that develops liquid foundation models running directly on devices such as phones, laptops, vehicles and wearables, without relying on a cloud connection.[1][2] It was founded in 2023 by Ramin Hasani, Mathias Lechner, and Alexander Amini, together with the MIT computer scientist Daniela Rus.[3]


The company’s research originated in liquid neural networks, a class of models inspired by the nervous system of the roundworm Caenorhabditis elegans.[4] Unlike the conventional transformer-based neural networks, whose parameters are fixed once training is complete, the company's liquid models can continue to adapt after training and use fewer neurons, so that they need less memory and computing power.[5][6][3]

Liquid AI also developed STAR, an automated method for designing neural network architectures, and the LFM2 model family, which combines short convolutional layers with a small number of attention layers.[7][8] Its models are used in Mercedes-Benz vehicles in North America.[9] And in 2026 the company announced Liquid Context, an on-device system developed with Qualcomm for Snapdragon processors.[2]

References

  1. ↑ Hulick Gargolinski, Kathryn (2025-02-26). "More brainlike computers could change AI for the better". ScienceNews. Archived from the original on 2026-05-20. Retrieved 2026-08-27.
  2. 1 2 Constantin, Ana Maria (2026-09-24). "Liquid AI puts a personal context layer for AI agents on Snapdragon chips". The Next Web. Retrieved 2026-10-10.
  3. 1 2 Pressman, Aaron (2024-09-30). "In AI Arms Race, Boston's Liquid AI claims to have a cheaper, more efficient app". The Boston Globe. Archived from the original on 2024-10-05. Retrieved 2026-08-27.
  4. ↑ Pressman, Aaron (2023-12-06). "Boston's answer to ChatGPT? MIT spinoff Liquid AI has a radical new approach". The Boston Globe. Archived from the original on 2025-07-23. Retrieved 2026-08-27.
  5. ↑ Knight, Will (2024-10-23). "Liquid AI Is Redesigning the Neural Network". WIRED. Retrieved 2026-08-27.
  6. ↑ Holmes, Aaron (2024-08-13). "Moonshot Bets Test Investors' AI Appetite". The Information. Archived from the original on 2024-09-30. Retrieved 2026-08-27.
  7. ↑ Franzen, Carl (2024-12-02). "Liquid AI's new STAR model architecture outshines Transformer efficiency". VentureBeat. Retrieved 2026-10-10.
  8. ↑ Hort, Joshua (2026-01-05). "Liquid AI & AMD Show the Future of On-Device AI With Local Private Meeting Summarization". AMD. Retrieved 2026-10-10.
  9. ↑ Maffei, Lucia (2026-08-03). "Why this Boston AI unicorn is betting on a different business model than OpenAI's". Boston Business Journal. Retrieved 2026-08-27.
Wiki Markup (to copy + paste instead of the old lead)
'''Liquid AI''' is an American artificial intelligence company, spun off from [[MIT]], that develops liquid foundation models running directly on devices such as phones, laptops, vehicles and wearables, without relying on a cloud connection.<ref name="ScienceNews 2025">{{cite news |last=Hulick Gargolinski |first=Kathryn |title=More brainlike computers could change AI for the better |url=https://www.sciencenews.org/article/brainlike-computers-ai-improvement |archive-url=https://web.archive.org/web/20260520021836/https://www.sciencenews.org/article/brainlike-computers-ai-improvement |archive-date=2026-05-20 |url-access=subscription |url-status=live |work=ScienceNews |date=2025-02-26 |access-date=2026-08-27}}</ref><ref name="Thenextweb2026">{{cite web |last=Constantin |first=Ana Maria |title=Liquid AI puts a personal context layer for AI agents on Snapdragon chips |date=2026-09-24 |website=The Next Web |url=https://thenextweb.com/news/liquid-ai-liquid-context-qualcomm-snapdragon-vision-dspark |access-date=2026-10-10}}</ref> It was founded in 2023 by Ramin Hasani, Mathias Lechner, and Alexander Amini, together with the MIT computer scientist [[Daniela Rus]].<ref name="Boston Globe 2024" />

The company’s research originated in liquid neural networks, a class of models inspired by the nervous system of the roundworm ''Caenorhabditis elegans''.<ref name="Boston Globe 2023" /> Unlike the conventional [[transformer (deep learning)|transformer-based neural networks]], whose parameters are fixed once training is complete, the company's liquid models can continue to adapt after training and use fewer neurons, so that they need less memory and computing power.<ref name="WIRED 2024">{{cite news |last=Knight |first=Will |title=Liquid AI Is Redesigning the Neural Network |url=https://www.wired.com/story/liquid-ai-redesigning-neural-network/ |work=WIRED |date=2024-10-23 |access-date=2026-08-27}}</ref><ref name="The Information 2024">{{cite news |last=Holmes |first=Aaron |title=Moonshot Bets Test Investors' AI Appetite |url=https://www.theinformation.com/articles/moonshot-bets-test-investors-ai-appetite |archive-url=https://web.archive.org/web/20240930164125/https://www.theinformation.com/articles/moonshot-bets-test-investors-ai-appetite |archive-date=2024-09-30 |url-access=subscription |url-status=live |work=The Information |date=2024-08-13 |access-date=2026-08-27}}</ref><ref name="Boston Globe 2024" />

Liquid AI also developed STAR, an automated method for designing neural network architectures, and the LFM2 model family, which combines short [[convolutional layer|convolutional layers]] with a small number of [[Attention (machine learning)|attention layers]].<ref name="Venturebeat2024">{{cite web |last=Franzen |first=Carl |title=Liquid AI's new STAR model architecture outshines Transformer efficiency |date=2024-12-02 |website=VentureBeat |url=https://venturebeat.com/business/liquid-ais-new-star-model-architecture-outshines-transformer-efficiency |access-date=2026-10-10}}</ref><ref name="AMD2026">{{cite web |last=Hort |first=Joshua |title=Liquid AI & AMD Show the Future of On-Device AI With Local Private Meeting Summarization |date=2026-01-05 |website=AMD |url=https://www.amd.com/en/blogs/2026/liquid-ai-amd-ryzen-on-device-meeting-summaries.html |access-date=2026-10-10}}</ref> Its models are used in Mercedes-Benz vehicles in North America.<ref name="Boston Business Journal 2026" /> And in 2026 the company announced Liquid Context, an on-device system developed with Qualcomm for [[Qualcomm Snapdragon|Snapdragon processors]].<ref name="Thenextweb2026" />

History

[edit]
Preview of the proposed update (the new sources are #2,3,6-7-8-9)

As of mid-2024, Liquid AI was demonstrating its software to potential investors; some machine-learning researchers considered liquid neural networks better suited to steering drones and self-driving cars than to generating text.[1]

In November 2024, the company developed STAR, an automated method for designing neural network architectures.[2][3]

In December 2024, Liquid AI raised $250 million at a valuation of more than $2.3 billion, in a round led by AMD.[4]

In January 2025, Liquid AI announced LFM-7B, a model with 7 billion parameters, which the company said outperformed conventional language models of the same size.[5] The company worked with DARPA to test one of its models in flying an aircraft.[5]

In November 2025, the company introduced the LFM2 model family, which combines short convolutional layers with a small number of attention layers to reduce memory use and improve inference speed.[6][7]

In September 2026, Liquid AI presented Liquid Context, software adapted to run on Qualcomm's Snapdragon chips used within consumer electronics. With the user's permission, it learns the user's routines and preferences from signals on the device and passes relevant information to AI agents of the user's choice.[8]

In October 2026, the company introduced its LFM3 family of compact AI models for on-device deployment of enterprise applications, including browser automation, image recognition, and voice interaction.[9]

References

  1. ↑ Holmes, Aaron (2024-08-13). "Moonshot Bets Test Investors' AI Appetite". The Information. Archived from the original on 2024-09-30. Retrieved 2026-08-27.
  2. ↑ Thomas, Armin W.; Parnichkun, Rom; Amini, Alexander; Massaroli, Stefano; Poli, Michael (2024-11-26). "STAR: Synthesis of Tailored Architectures". arXiv:2411.17800 [cs.LG].
  3. ↑ Franzen, Carl (2024-12-02). "Liquid AI's new STAR model architecture outshines Transformer efficiency". VentureBeat. Retrieved 2026-10-10.
  4. ↑ Ghaffary, Shirin; Katie Roof (2024-12-13). "Liquid AI Raising $250 Million to Build AI Inspired by Worm Brains". Bloomberg. Archived from the original on 2025-11-28. Retrieved 2026-08-27.
  5. 1 2 Hulick Gargolinski, Kathryn (2025-02-26). "More brainlike computers could change AI for the better". ScienceNews. Archived from the original on 2026-05-20. Retrieved 2026-08-27.
  6. ↑ Amini, Alexander; Banaszak, Anna; Benoit, Harold; Böök, Arthur; Dakhran, Tarek; Duong, Song; Eng, Alfred; Fernandes, Fernando; Härkönen, Marc; Harrington, Anne; Hasani, Ramin; Karwa, Saniya; Khrustalev, Yuri; Labonne, Maxime; Lechner, Mathias; Lechner, Valentine; Lee, Simon; Li, Zetian; Loo, Noel; Marks, Jacob; Mosca, Edoardo; Paech, Samuel J.; Pak, Paul; Parnichkun, Rom N.; Quach, Alex; Rogers, Ryan; Rus, Daniela; Saxena, Nayan; Schlager, Bettina; Seyde, Tim; Smith, Jimmy T. H.; Tadimeti, Aditya; Tumma, Neehal (2025-11-28). "LFM2 Technical Report". arXiv:2511.23404 [cs.LG].
  7. ↑ Hort, Joshua (2026-01-05). "Liquid AI & AMD Show the Future of On-Device AI With Local Private Meeting Summarization". AMD. Retrieved 2026-10-10.
  8. ↑ Constantin, Ana Maria (2026-09-24). "Liquid AI puts a personal context layer for AI agents on Snapdragon chips". The Next Web. Retrieved 2026-10-10.
  9. ↑ Franzen, Carl (October 27, 2026). "Liquid AI's new LFM3 models put browser agents, voice commands, and image recognition onto local devices". VentureBeat.
Wiki Markup (to copy + paste instead of the last three paragraphs in the History section)
As of mid-2024, Liquid AI was demonstrating its software to potential investors; some machine-learning researchers considered liquid neural networks better suited to steering drones and self-driving cars than to generating text.<ref name="The Information 2024" />

In November 2024, the company developed STAR, an automated method for designing neural network architectures.<ref name="arXiv2024">{{cite arXiv |last1=Thomas |first1=Armin W. |last2=Parnichkun |first2=Rom |last3=Amini |first3=Alexander |last4=Massaroli |first4=Stefano |last5=Poli |first5=Michael |title=STAR: Synthesis of Tailored Architectures |class=cs.LG |eprint=2411.17800 |date=2024-11-26}}</ref><ref name="Venturebeat2024">{{cite web |last=Franzen |first=Carl |title=Liquid AI's new STAR model architecture outshines Transformer efficiency |date=2024-12-02 |website=VentureBeat |url=https://venturebeat.com/business/liquid-ais-new-star-model-architecture-outshines-transformer-efficiency |access-date=2026-10-10}}</ref>

In December 2024, Liquid AI raised $250 million at a valuation of more than $2.3 billion, in a round led by [[AMD]].<ref name="Bloomberg 2024">{{cite news |last=Ghaffary |first=Shirin |author2=Katie Roof |title=Liquid AI Raising $250 Million to Build AI Inspired by Worm Brains |url=https://www.bloomberg.com/news/articles/2024-12-13/liquid-ai-raising-250-million-to-build-ai-inspired-by-worm-brains |archive-url=https://web.archive.org/web/20251128200717/https://www.bloomberg.com/news/articles/2024-12-13/liquid-ai-raising-250-million-to-build-ai-inspired-by-worm-brains |archive-date=2025-11-28 |url-access=subscription |url-status=live |work=Bloomberg |date=2024-12-13 |access-date=2026-08-27}}</ref>

In January 2025, Liquid AI announced LFM-7B, a model with 7 billion parameters, which the company said outperformed conventional language models of the same size.<ref name="ScienceNews 2025" /> The company worked with [[DARPA]] to test one of its models in flying an aircraft.<ref name="ScienceNews 2025" />

In November 2025, the company introduced the LFM2 model family, which combines short convolutional layers with a small number of attention layers to reduce memory use and improve inference speed.<ref name="arXiv2025">{{cite arXiv |last1=Amini |first1=Alexander |last2=Banaszak |first2=Anna |last3=Benoit |first3=Harold |last4=Böök |first4=Arthur |last5=Dakhran |first5=Tarek |last6=Duong |first6=Song |last7=Eng |first7=Alfred |last8=Fernandes |first8=Fernando |last9=Härkönen |first9=Marc |last10=Harrington |first10=Anne |last11=Hasani |first11=Ramin |last12=Karwa |first12=Saniya |last13=Khrustalev |first13=Yuri |last14=Labonne |first14=Maxime |last15=Lechner |first15=Mathias |last16=Lechner |first16=Valentine |last17=Lee |first17=Simon |last18=Li |first18=Zetian |last19=Loo |first19=Noel |last20=Marks |first20=Jacob |last21=Mosca |first21=Edoardo |last22=Paech |first22=Samuel J. |last23=Pak |first23=Paul |last24=Parnichkun |first24=Rom N. |last25=Quach |first25=Alex |last26=Rogers |first26=Ryan |last27=Rus |first27=Daniela |last28=Saxena |first28=Nayan |last29=Schlager |first29=Bettina |last30=Seyde |first30=Tim |last31=Smith |first31=Jimmy T. H. |last32=Tadimeti |first32=Aditya |last33=Tumma |first33=Neehal |title=LFM2 Technical Report |eprint=2511.23404 |class=cs.LG |date=2025-11-28}}</ref><ref name="AMD2026">{{cite web |last=Hort |first=Joshua |title=Liquid AI & AMD Show the Future of On-Device AI With Local Private Meeting Summarization |date=2026-01-05 |website=AMD |url=https://www.amd.com/en/blogs/2026/liquid-ai-amd-ryzen-on-device-meeting-summaries.html |access-date=2026-10-10}}</ref>

In September 2026, Liquid AI presented Liquid Context, software adapted to run on Qualcomm's Snapdragon chips used within consumer electronics. With the user's permission, it learns the user's routines and preferences from signals on the device and passes relevant information to AI agents of the user's choice.<ref name="Thenextweb2026">{{cite web |last=Constantin |first=Ana Maria |title=Liquid AI puts a personal context layer for AI agents on Snapdragon chips |date=2026-09-24 |website=The Next Web |url=https://thenextweb.com/news/liquid-ai-liquid-context-qualcomm-snapdragon-vision-dspark |access-date=2026-10-10}}</ref>

In October 2026, the company introduced its LFM3 family of compact AI models for on-device deployment of enterprise applications, including browser automation, image recognition, and voice interaction.<ref name="VenturebitLFM3">{{cite web |last=Franzen |first=Carl |title=Liquid AI’s new LFM3 models put browser agents, voice commands, and image recognition onto local devices |url=https://venturebeat.com/technology/liquid-ais-new-lfm3-models-put-browser-agents-voice-commands-and-image-recognition-onto-enterprise-controlled-devices |website=VentureBeat |date=October 27, 2026}}</ref>

I'm happy to adjust anything. Thank you! Alexandra Goncharik -sms- 14:57, 10 October 2026 (UTC)Reply

@Balabush: Hi! Could you please take a look at the proposed edits to the article you previously approved within AfC? I can make these changes myself once I get your go-ahead, and I'll show the results in the diff. Alexandra Goncharik -sms- 17:31, 31 October 2026 (UTC)Reply
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