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Latest AI research, explained simply

New AI, tech and chip research in plain words, with a link to every source, plus the people worth following.

  • 4 picks today
  • 8 top papers
  • Updated 19 Oct 2026

Agents · Hugging Face Daily Papers · 14 Oct

New studynot peer reviewed yet

FDE-Gym: The Last Mile of AI Delivery

A new study says AI agents do much worse on realistic customer jobs, where the request is vague and hidden rules must be discovered, than on clean tests.

Read the source

How AI works · Hugging Face Daily Papers · 12 Oct

New studynot peer reviewed yet

Virtual Head Attention

A new study says a trick can make AI handle long documents with less memory and compute while keeping answer quality.

Read the source

Today in research

4 picks, each explained in plain words and linked to its source. Older days are in the drawers below.

  • New studynot peer reviewed yet

    FDE-Gym: The Last Mile of AI Delivery

    A new study says AI agents do much worse on realistic customer jobs, where the request is vague and hidden rules must be discovered, than on clean tests.

    Hugging Face Daily Papers (285 upvotes) ·

    Read more
  • New studynot peer reviewed yet

    LEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents

    A new study says a cheap AI helper can do most computer tasks and call a stronger one only when unsure, which cuts cost.

    Hugging Face Daily Papers (8 upvotes) ·

    Read more
  • New studynot peer reviewed yet

    Data World: Scaling Data Environments from Scratch

    A new study says AI can build its own practice data tasks to get better at finding insights in data, without copying a bigger model.

    Hugging Face Daily Papers (28 upvotes) ·

    Read more
  • New studynot peer reviewed yet

    Virtual Head Attention

    A new study says a trick can make AI handle long documents with less memory and compute while keeping answer quality.

    Hugging Face Daily Papers (10 upvotes) ·

    Read more
Yesterday 5 picks
  • Explainer

    Open-weight models trail closed models in cyber capabilities by 6-7 months

    Epoch AI says the best free-to-download model, Z.ai's GLM-5.3, matches where paid models stood 6 to 7 months ago on its cyber test.

    Epoch AI ·

    Read more
  • Apple Machine Learning Research

    Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio

    Apple shows how several Mac Studios can team up to run very large AI models at home.

    Apple Machine Learning Research ·

    Read more
  • New studynot peer reviewed yet

    Behavioral Compositionality of Sparse Autoencoder Representations

    A new study says the building blocks we use to peek inside AI models only partly combine like Lego; steering the model needs extra pieces.

    Hugging Face Daily Papers (104 upvotes) ·

    Read more
  • Allen Institute for AI

    HiRO-ACEv2: Simulating extreme weather at 3 km resolution

    Ai2 says its AI weather model captures extreme rain and wind at about 1/300th the cost of the usual physics simulator.

    Allen Institute for AI ·

    Read more
  • New studynot peer reviewed yet

    LEASH: Uncertainty-Aware Weak-to-Strong Escalation for Computer-Use Agents

    A new study says a cheap AI agent can do the job and call in a stronger one only when unsure, saving cost.

    Hugging Face Daily Papers ·

    Read more

Today's top papers

Most upvoted papers

Hugging Face Daily Papers, 19 Oct. All new studies, not peer reviewed yet. Number = upvotes.

  1. 1FDE-Gym: The Last Mile of AI Delivery285Read more
  2. 2Behavioral Compositionality of Sparse Autoencoder Representations106Read more
  3. 3Can World and Action Modeling Benefit Each Other under Unified Causal Training?40Read more
  4. 4OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling32Read more
  5. 5Data World: Scaling Data Environments from Scratch28Read more
  6. 6Virtual Head Attention10Read more
  7. 7FlashMask-3: Efficient and Expressive Mask-Aware Distributed Attention9Read more
  8. 8LEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents8Read more

From the labs

The newest post from each research lab blog we follow.

  • Google Research

    Grounding AI agents in biological reality: Building blocks for autonomous wet labsRead more
  • Microsoft Research

    Proof-based general-purpose computing with MagnetarRead more
  • Apple Machine Learning Research

    Local and Distributed Large LLM Inference with MLX and RDMA on Mac StudioRead more

Who to follow

30 people worth following for new research, with links to their own posts where we have them.

See all 30 people

Researchers

People doing the research, many with their own blogs.

  • Geoffrey Hinton'Godfather of AI', 2024 Nobel Prize in Physics, now warns about AI risksX profile
  • Yoshua BengioTuring Award winner, leads Mila and the International AI Safety ReportWebsite
  • Yann LeCunTuring Award winner, deep learning pioneer, ex-Meta chief AI scientistX profile
  • Fei-Fei LiStanford professor who built ImageNet, co-founded World LabsX profile
  • Demis HassabisGoogle DeepMind CEO, 2024 Nobel Prize in Chemistry for AlphaFoldX profile
  • Ilya SutskeverOpenAI co-founder, now runs Safe Superintelligence (SSI)X profile
  • Jeff DeanGoogle's chief scientist, GeminiX profile
  • Andrej KarpathyOpenAI co-founder, ex-Tesla AI head, best plain explainer videosX profileBlog feedYouTube
  • Andrew NgCoursera and DeepLearning.AI founder, weekly newsletter The BatchX profileWebsite
  • Percy LiangStanford, leads model testing (HELM) and open-model researchX profile
  • François CholletMade Keras, created the ARC-AGI testX profile
  • Jim FanNVIDIA, robots and AI agentsX profile
  • Noam BrownOpenAI, AI that reasons (o1), poker AIX profile
  • Lilian WengThinking Machines co-founder, famous long blog explainersX profileBlog feed
  • Sebastian RaschkaExplains how LLMs are built, monthly paper round-upsX profileBlog feed
  • Nathan LambertAi2, open models and training, newsletter InterconnectsX profileBlog feed
  • Melanie MitchellSanta Fe Institute, clear-eyed takes on what AI can't doBlog feed

Explainers and influencers

People who read new papers and explain what they mean.

  • AK (Ahsen Khaliq)Posts new AI papers all day; runs Hugging Face Daily PapersX profile
  • Ethan MollickWharton professor, how AI changes work, blog One Useful ThingX profileBlog feed
  • Simon WillisonCo-made Django, tests every new model the day it landsX profileBlog feed
  • Jack ClarkAnthropic co-founder, weekly paper digest Import AIX profileBlog feed
  • swyx (Shawn Wang)Latent Space podcast and newsletter for AI buildersX profileBlog feed
  • Dwarkesh PatelLong podcast interviews with top AI researchersX profileBlog feed
  • Lex FridmanBig podcast with tech and AI leadersX profileYouTube feed
  • Two Minute Papers (Karoly Zsolnai-Feher)YouTube: new papers explained in minutesYouTube feed
  • Yannic KilcherYouTube: walks through papers line by lineX profile
  • Gary MarcusBest-known AI critic, keeps the hype honestX profileBlog feed

Hardware and chip voices

People who follow AI chips, data centers and computer design.

  • Dylan PatelSemiAnalysis founder, AI chips and data centersX profileBlog feed
  • Ian CutressChip analyst, TechTechPotato on YouTubeX profile
  • Jim KellerLegendary chip designer (AMD, Apple, Tesla), runs Tenstorrent
How we pick and check research
  • We pull 44 free sources every day: 9 paper feeds (Hugging Face Daily Papers and arXiv), 5 peer-reviewed journals, 9 lab blogs, 8 explainer sites, 13 researchers' own feeds.
  • Preprints (mostly from arXiv) are labeled New study and not peer reviewed yet: a new study says something, it is not proven.
  • Peer-reviewed means other scientists checked the paper before a journal printed it.
  • We never write a finding we can't source. Every item links to where it came from, and numbers come only from the paper itself.
  • The plain explanations come from our daily research run, which reads each pick. Top papers from the raw pull show only their title and source.
  • Top papers are ranked by Hugging Face community upvotes on the day we pulled them.
  • People are listed only as sources to follow, with links to their own posts. We never quote them.
  • Skipped: hacking tools, deepfakes and voice cloning, NSFW and the rest of our skip list.
All 44 sources
  • Hugging Face Daily Papers
  • arXiv: AI
  • arXiv: machine learning
  • arXiv: language (LLMs)
  • arXiv: vision
  • arXiv: robotics
  • arXiv: chips and hardware
  • arXiv: security
  • arXiv: quantum
  • Nature
  • Nature Machine Intelligence
  • Nature Electronics
  • Science
  • PNAS
  • Google Research
  • Google DeepMind
  • Microsoft Research
  • Apple Machine Learning Research
  • OpenAI
  • NVIDIA Technical Blog
  • Allen Institute for AI (Ai2)
  • Berkeley AI Research (BAIR)
  • MIT News: AI
  • Epoch AI
  • IEEE Spectrum
  • Quanta Magazine
  • ScienceDaily: AI
  • Chips and Cheese
  • ServeTheHome
  • Import AI (Jack Clark)
  • The Gradient
  • Andrej Karpathy
  • Lilian Weng
  • Sebastian Raschka
  • Nathan Lambert
  • Melanie Mitchell
  • Ethan Mollick
  • Simon Willison
  • swyx (Shawn Wang)
  • Dwarkesh Patel
  • Lex Fridman
  • Two Minute Papers (Karoly Zsolnai-Feher)
  • Gary Marcus
  • Dylan Patel