David Mimno

Information science professor at Cornell, machine learning

Why they matter

Information science professor at Cornell, machine learning with public evidence across AI research.

AI signals
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past 30d
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Discussões
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past 30d
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7d ago
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He teaches information science at Cornell. http://mimno.infosci.cornell.edu

Articles & links

↻ David Mimno reposted
Sung Kim @sungkim.bsky.social

This seems to be the most popular alternative to jev. Laya: Multilingual, non-autoregressive System 1 decision model. huggingface.co/convaiinnova... Laya Repo: github.com/NandhaKishor... Laya MLX: huggingface.co/aac6fef/laya... Laya MLX Repo: github.com/mizorewww/la...

GitHub - NandhaKishorM/laya github.com View on Bluesky →
↻ David Mimno reposted
Sung Kim @sungkim.bsky.social

This seems to be the most popular alternative to jev. Laya: Multilingual, non-autoregressive System 1 decision model. huggingface.co/convaiinnova... Laya Repo: github.com/NandhaKishor... Laya MLX: huggingface.co/aac6fef/laya... Laya MLX Repo: github.com/mizorewww/la...

convaiinnovations/laya · Hugging Face huggingface.co
AI Weekly's analysis →
  • Convai Innovations released Laya, a 421M-parameter decision head built on ModernBERT-large, under Apache 2.0 with three checkpoints.
  • p50 latency of 32.8ms on a Tesla T4 versus TypeSafe Jev's 236-276ms is the model's headline performance claim.
  • Zero-shot typed-decisions accuracy is 0.362 (near random); the 0.766 figure requires fine-tuning on that benchmark's own training split.
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↻ David Mimno reposted
Sung Kim @sungkim.bsky.social

This seems to be the most popular alternative to jev. Laya: Multilingual, non-autoregressive System 1 decision model. huggingface.co/convaiinnova... Laya Repo: github.com/NandhaKishor... Laya MLX: huggingface.co/aac6fef/laya... Laya MLX Repo: github.com/mizorewww/la...

aac6fef/laya-mlx · Hugging Face huggingface.co
AI Weekly's analysis →
  • aac6fef released laya-mlx, a native MLX FP16 port of the 0.4B-parameter convaiinnovations/laya decision encoder, running on Apple silicon without PyTorch.
  • On an Apple M3 Max the port matched upstream PyTorch MPS FP32 on argmax in 63 of 63 decision distributions, with a max probability difference of 0.0054443.
  • The 843 MB Apache-2.0 checkpoint handles choice, ordinal score, and boolean noul questions inside a 512-token total context.
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↻ David Mimno reposted
Sung Kim @sungkim.bsky.social

This seems to be the most popular alternative to jev. Laya: Multilingual, non-autoregressive System 1 decision model. huggingface.co/convaiinnova... Laya Repo: github.com/NandhaKishor... Laya MLX: huggingface.co/aac6fef/laya... Laya MLX Repo: github.com/mizorewww/la...

GitHub - mizorewww/laya-mlx: Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API. github.com
AI Weekly's analysis →
  • Laya MLX reports 13.4 ms median latency for English typed decisions and 7.4 ms for the multilingual checkpoint on an M3 Max.
  • The runtime skips text generation entirely, running ModernBERT-large and mmBERT-base encoders through MLX with no PyTorch or cloud dependency.
  • It handles three question shapes: choice probabilities, rubric-based scores, and proposition probabilities, all local and Apple Silicon only.
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↻ David Mimno reposted
Melanie Walsh @mellymeldubs.bsky.social

If you liked Bears Will Be Boys, you might enjoy our new FAccT paper! We prompted LLMs to complete 24K stories about animal characters where gender is unstated. We found that.. bears are *still* boys. And female animal characters disappeared while "neutrality" increased. arxiv…

Neutrality Bites: Gender Representation in AI-Generated Animal Stories arxiv.org
AI Weekly's analysis →
  • Across 23,800 AI-generated stories, feminine animal characters appeared in just 2.2% versus 40.6% masculine.
  • Models avoided assigning any gender 19% of the time on average; gender-neutral 'it/its' pronouns appeared in 38.2% of stories.
  • The authors argue AI neutrality can erase marginalized identities rather than protect them, challenging a common alignment assumption.
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↻ David Mimno reposted
Daniel van Strien @danielvanstrien.bsky.social

A 15.9M-parameter specialist OCR model outperforms many VLMs hundreds of times larger. On a 2,165-page historical eval dataset, Kraken PP-OCRv6 ranks #4 on reading CER and #1 when long-s, ligatures and case are preserved. huggingface.co/spaces/fineb...

BHL OCR Leaderboard - a Hugging Face Space by finebooks huggingface.co
AI Weekly's analysis →
  • rednote-hilab's dots.mocr (3B) leads the FineBooks BHL leaderboard at 97.6% reading accuracy across 2,165 expert-transcribed pages.
  • Sub-2B models OvisOCR2 (0.9B, 96.9%) and PaddleOCR-VL-1.6 (1B, 96.1%) come next at under $0.50 per thousand pages.
  • Ground truth covers only Antiqua typefaces in English, French, German and Latin; Fraktur, handwriting and multi-column layouts are excluded.
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↻ David Mimno reposted
Daniel van Strien @danielvanstrien.bsky.social

OCR for Japanese manga, Swedish handwriting or Arabic print? There’s a growing range of OCR models on @hf.co: VLMs, dedicated text recognisers and complete OCR pipelines. I’ve gathered 41 models into four collections, with short notes to help you choose: huggingface.co/collect…

OCR on the Hub - a davanstrien Collection huggingface.co
AI Weekly's analysis →
  • davanstrien's OCR on the Hub groups models into four sub-collections covering documents, languages and scripts, handwriting and archives, and text recognition pipelines.
  • The languages sub-collection covers Thai, Japanese, Vietnamese, Arabic, Korean and Devanagari; handwriting spans Swedish, Norwegian, German Kurrent, Tibetan, and Hebrew-script manuscripts.
  • The text recognition sub-collection features Kraken and PaddleOCR pipelines for documents, manga and text in photographs.
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↻ David Mimno reposted
Emma Harvey @emmharv.bsky.social

✨New Work✨ forthcoming at #AIES2026: 1️⃣ "Data Annotation as Measurement" by me, @allisonkoe.bsky.social, and @kizilcec.bsky.social explores how data annotation can go wrong, and how measurement theory can help improve it. 🔗: arxiv.org/pdf/2608.07297

arxiv.org View on Bluesky →

Recent commentary

It's possible for Jev/Laya/Decision Models to be not that big a deal as tech and massive as a new paradigm. Here's why I'm really excited from an NLP history perspective (thread)

View on Bluesky · ♥ 76 ↻ 19 ↩ 1 · 15d ago

I’m all for serious penalties for hallucinated citations. But there’s a reason one of the rules of my lab is “Don’t try to solve Library Science”.

View on Bluesky · ♥ 47 ↻ 3 ↩ 3 · 143d ago

Cars are a surprisingly good model for public acceptance of AI. Some people love them and tinker with them. Others hate and reject them. Most find their own car useful. But everyone viscerally hates everyone else's car: traffic, parking, dangerous driving, pollution, etc.

View on Bluesky · ♥ 30 ↻ 4 ↩ 3 · 29d ago

Can anyone suggest even one single reason anyone would ever want to watch an AI generated video beyond gee-whiz novelty? Who wants this?

View on Bluesky · ♥ 19 ↻ 2 ↩ 7 · 141d ago

“Never trust an AI executive with a bunker” -Brent Hecht

View on Bluesky · ♥ 6 ↻ 0 ↩ 0 · 146d ago

I googled a quote I half remembered ("one bite from the sunny side of the peach"). No results, but I thought I'd try the AI mode. It said this was Diderot criticizing Rousseau. I asked for a source, and it pointed me to Scott's Waverley (the correct answer it seems). What happened to Diderot?

View on Bluesky · ♥ 3 ↻ 0 ↩ 0 · 113d ago

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