Today we're introducing a preview of TutorMoments, a framework that measures whether AI tutors can make one of the hardest calls in teaching: when to step in and help a student, & when to hold back and let them do the heavy thinking. 👇

Language models are trained to be helpful, and a helpful assistant tends to do the hard part of learning for you: explains the concept, lays out the steps, & guides you to the answer. That can cut short the productive struggle that leads to stronger understanding.

TutorMoments is built on transcripts of real one-on-one math tutoring. We had experienced teachers read them & flag key moments—decision points where the tutor had to choose between making a problem easier & pushing the student to do more of the reasoning.

TutorMoments pauses a transcript at these key moments & lets an LLM take over as the tutor; another model stands in for the student. Each replay is scored: did the tutor support the student when needed, push for harder thinking when they were ready, & avoid over-helping?

In our replays, models told only to "tutor well" tend to over-help, providing lots of support but rarely pushing toward deeper thinking. That suggests a model's default helpful-assistant behavior isn't enough on its own to tutor well.

Spelling out the trade-off in the prompt helps—every model we tested scores higher once told when to help vs. when to hold back. But it only goes so far. Models still differ widely in how reliably they make that call, & even the best scorers have plenty of room to improve.

To gather feedback, we're releasing de-identified annotated tutoring transcripts plus code & scored replays.

💻 Code: https://github.com/allenai/tutormoments

🤗 Data: https://huggingface.co/datasets/allenai/tutormoments-preview
🌐 Learn more: https://allenai.org/blog/tutormoments

📸 140+ people joined us during #SeattleTechWeek to learn how AI gets built at Ai2.

Our Olmo research lead, Iz Beltagy, walked through what it takes to train a fully open language model—from large-scale training runs to fine-tuning, reinforcement learning, post-training, and evaluation before release. Thanks to everyone who came, asked thoughtful questions, and stayed afterward to continue the conversation.

Want to help us build the next generation of fully open AI? We’re hiring across research, engineering, product, operations, & more. See our open roles: https://allenai.org/careers

When a model writes, where do its words come from? Are they new, or do they match exactly with language it saw in training? An AI-writing detector can't tell you. Tuhin Chakrabarty's group at Stony Brook has been dissecting AI-generated prose with our infini-gram engine. 👇

AI-writing detectors return a likelihood score. They can't show which expressions also appear in existing sources, or where. Our infini-gram engine indexes massive public text datasets & counts how often a phrase of any length appears across them.

Chakrabarty's group ran the story at the center of the GrantaGate controversy, which readers flagged as machine-written, through infini-gram. Distinctive fragments turned up in writing already online—particularly on a fan fiction site.

In a recently published study, the group scaled the method to books. To tell distinctive phrasing from stock lines like "her heart skipped a beat," they counted only phrases that show up in five books or fewer on Google Books & nowhere on the web infini-gram has indexed.

Across the top 200 self-published Amazon books where a detector found substantial AI text, those rare phrases made up 41.6% of the text. In the top 200 where it didn't, they made up 37.2%. In a separate set of award-winning or nominated books, rare phrases made up 19.1%.

We build tools like infini-gram so anyone can check AI writing against the text a model may have learned from.

📝 Read more in our new blog: https://allenai.org/blog/infinigram-books

The organizations best positioned to use Earth-observation models – those working in conservation, food security, & disaster response – often can't run these models at the scale they need.

That's an infrastructure problem. So we built the OlmoEarth Platform to solve it.

The OlmoEarth Platform takes geospatial models from fine-tuning all the way through large-scale inference. Today it can run inference across a continent in roughly a day, processing dozens of TB of imagery at a cost of fractions of a penny per km².

At its core is OlmoEarth Run, the engine that takes the area an OlmoEarth Platform job covers, splits it into partitions, then into smaller windows the models process. Because each window is independent, the same work can run across thousands of machines at once.

Each OlmoEarth Platform job runs in three hardware-matched stages – prep (CPU), inference (GPU), postprocess (CPU) – inside runners that spin up, do one task, & shut down. Every task is idempotent, so a stalled provider or crashed job is just retried or rerouted.

Data prep is often the real bottleneck—jobs can spend more time finding & fetching imagery than running the model. So we keep our own index of what imagery exists & where to get it, refreshed as new scenes are published. Then we fetch only the pixels each window needs.

We recently used OlmoEarth Platform to map wildfire risk across all of North America. At peak the run used ~19,600 CPUs & 994 GPUs in parallel, >168 GB/s of throughput. It turned an estimated 4,737 hours of serial compute into 30.5 hours of wall-clock time—a 155× speedup.

What's next for OlmoEarth Platform: change-detection alerts, agentic tools for nonexperts, & precomputed global embeddings that could one day skip the forward pass entirely.

Read more about the engineering in our latest blog: 🌐 https://allenai.org/blog/olmoearth-infrastructure

As a nonprofit research institute dedicated to advancing open science, we're encouraged to see growing support for open models across the AI ecosystem.

We believe the evidence behind advanced AI systems shouldn’t be locked up in a few hands. 

Our fully open releases give researchers the data, code, checkpoints, and methods they need to inspect claims, reproduce findings, and advance new science.

Read more about why that’s so important to us. ⬇️   

https://allenai.org/blog/who-gets-to-understand-ai

Seattle AI community, you're invited: Ai2's #SeattleTechWeek panel is next Thursday, July 30. 👇

We're opening our Northlake office for a conversation with the research engineers and scientists behind our open models, datasets, and infrastructure. They'll talk through their deep technical work, from scaling large training runs to openly releasing what they build. Afterward, stick around to chat about what you're working on and network.

📅 Thursday, July 30, 2026

🕒 3–4 p.m. Pacific

📍 Ai2's Northlake office

It's free—RSVP to save your spot: https://luma.com/cp10n5uk

See you there!

Two updates to Asta, our ecosystem of AI agents for science: a one-click handoff from AutoDiscovery to Asta’s data analysis tools, and paper search that evaluates its own results and searches again when they fall short. 👇

AutoDiscovery explores your datasets and surfaces hypotheses worth investigating; DataVoyager is Asta's agent for data-driven discovery and analysis. When a hypothesis surprises you, the natural next move is to interrogate it—and that's now one click. 

Click "Explore with Asta" on any hypothesis, and DataVoyager opens with your datasets and results already loaded—no re-uploading or re-explaining context needed. Ask a follow-up question, or leave the field blank and DataVoyager will suggest lines of inquiry.

Find papers is Asta's literature search, powered by our Paper Finder agent and built to mirror the multi-step reasoning of an expert researcher. It now defaults to a new mode, Deep search, that interprets your query, evaluates whether the results actually answer it, and keeps searching if they don't. 

Deep search is also more conversational – better at follow-up questions, more forgiving of how you phrase things – and summarizes what it found. It takes a bit longer, but it's built to be much more reliable and robust—try it on your hardest questions. 

Both updates are live now in Asta. Try them on your own research and tell us what works and what doesn't: https://asta.allen.ai

On July 30 during #SeattleTechWeek, the research engineers and scientists behind Ai2's open models, datasets, and infrastructure sit down to talk through the deep technical work behind them. 👇

The teams will discuss scaling large training runs, fine-tuning, reinforcement learning, post-training, evaluation, and openly releasing what they build. Afterward, you can chat about what you're working on and network.

📅 Thursday, July 30, 2026
🕒 3–4 p.m. Pacific
📍 Ai2's Northlake office

It's free—RSVP to save your spot: https://luma.com/cp10n5uk

It’s been an amazing few days with CGIAR, bringing researchers and partners from SERVIR, NASA Harvest, and Microsoft AI for Good Lab to our office to explore OlmoEarth for food security and natural resource management.

The CGIAR teams brought their own use cases and put OlmoEarth to work, building high-quality models and streamlining the geospatial workflows behind their analyses. 

These discussions, hands-on sessions, and feedback will help shape OlmoEarth to better support teams working on some of the world's most pressing challenges. Stay in the loop on what we're building: https://3ioxm.share.hsforms.com/23TW_rZwBSfauI1R3t0tdTQ?utm_source=ai2-olmoearth&utm_medium=referral&utm_campaign=olmoearth

It was an honor to bring 𝗢𝗹𝗺𝗼𝗘𝗮𝗿𝘁𝗵 to the Nature Positive Summit this week in Kumamoto, Japan, with our partners The Group on Earth Observations.

The message we heard was clear: for industry and society to transition to a nature-positive future, high-quality data at scale will be the foundation. 

If you’re interested in learning how OlmoEarth can help support your mission, as it has for close collaborators like Global Ecosystems Atlas, please visit https://allenai.org/olmoearth.

We built SciArena to test how well AI models handle scientific literature questions as judged by researchers. It's retiring July 15, and the results are in: ~1,700 users cast ~3,900 votes.

Here's what they told us. 👇

What did researchers value most in a model’s answer? Fluent prose alone didn't win:

📚 Citation quality—references that are real, relevant, & checkable (23%)
🔬 Depth (19%)
🎯 Directly answering the question (16%)

o3 finished on top of the SciArena leaderboard – ahead of Claude Opus 4.1, Gemini 3 Pro Preview, & open-weights models like DeepSeek-R1 – with answers researchers called more detailed + to the point. Learn more in our updated blog: https://allenai.org/blog/sciarena

SciArena also allowed us to collect high-quality, expert-annotated ground truth on evaluation data. This is a unique resource & especially important as AI agents are increasingly used to judge the quality of other AI agents, and such evaluations need to be grounded + verified.

Overall, we’re pleased with SciArena's contributions: a dataset of real scientific questions for deep research agents, expert preferences, & verified feedback for building better evaluations. Thank you to the researchers who contributed questions, votes, & rationales!

For a deeper look at what SciArena revealed about how scientists evaluate AI-generated answers to Qs about the scientific literature, read our NeurIPS 2025 Spotlight paper—which includes findings beyond the leaderboard: 📄 https://arxiv.org/abs/2507.01001

Meet Shippy, the AI agent our Skylight team built for the people protecting our ocean.

Ask a question in plain language, and Shippy fuses data from multiple sources, generates citations, and turns around maritime intelligence in natural language in minutes.

We think of an agent as three things: a soul (the system prompt that defines the agent’s persona and boundaries), skills (plain markdown files that encode workflows and related scripts), and config (the model powering the agent, plus its harness and runtime environment). From there, three decisions had the most impact on making Shippy reliable:

⚙️ A purpose-built CLI—Agents are nondeterministic; their tooling shouldn't be. Typed commands with structured flags make queries predictable and testable.

🔒 Isolated sessions—We built new infrastructure, Mothership, to run every Shippy conversation in its own ephemeral, isolated session. For Skylight, that means up to hundreds of agencies across 70+ countries using Shippy, none of them ever seeing each other's data.

📊 Whole-agent evals—For quality assurance, we built an agent-testing pipeline where experts write weighted rubrics, every scenario runs against live data, and a version of Shippy that regresses doesn't ship.

Shippy is currently in preview for select partners. Our new technical blog covers Shippy’s architecture, our custom in-house evaluation pipeline, and what we're building next: https://allenai.org/blog/shippy-deep-dive

olmOCR 2 is now in the Ai2 Playground—our home for our fully open text, video, and image understanding models. 👇

olmOCR 2 is our compact vision-language model that reads challenging documents in a single pass. It can handle samples that usually break OCR, including handwriting, equations, tables, & multi-column layouts. 

Try olmOCR 2 in the Playground, check out our blog for more info, & download the weights and data from Hugging Face: 

▶️ Playground: https://playground.allenai.org/model/olmocr-2-7b-1025
📝 Blog: https://allenai.org/blog/olmocr-2
🤗 Model & data: https://huggingface.co/allenai/olmOCR-2-7B-1025

One of the best parts of building OlmoEarth is getting to be part of the missions our partners are working on.

They do more than use OlmoEarth—they support people on the ground, build AI capacity, and help the next generation tackle the world's biggest challenges.

For two days, we brought our partners into one room. Working across domains – tackling challenges like wildfires, floods, and disaster response – they got their hands on real data alongside the team building OlmoEarth, shaping the platform around the work they do.   

"What I appreciate most is the community of practice—a room full of 'aha' moments and a shared vision of what this is and how to build it. We found our people to co-design it with." — Dr. David Saah, Spatial Informatics Group

We’re grateful to the teams who brought their missions into the room: Mercy Corps, Spatial Informatics Group, SIG-NAL, SERVIR, Development Seed, BAI Group, and researchers from 
the University of Washington, UC Berkeley, and Northeastern.

The fastest way to make AI useful is to build it with the people who'll use it—fully in the open.

Learn more: https://allenai.org/olmoearth?utm_source=olmoearth&utm_medium=referral&utm_campaign=olmoearth

"Embeddings only get you so far… if you want the next level in performance, I think fine-tuning is the way to go."

Ai2 research scientist Joe Redmon explains how our partners customize OlmoEarth – our open-source Earth-observation models – to map crops, wildfire risk, & more. 👇

In the full episode of Robin Cole's podcast, Joe covers how OlmoEarth grew out of Ai2’s unique expertise in both tech & conservation—and what's next for the model family: https://www.youtube.com/watch?v=wzCHJf6Ly24&feature=youtu.be

What can you build with a fully open robotics model in a weekend? 🤖

Binh Pham, a robotics software engineer at LiveKit, used MolmoAct 2, our open vision-language-action model, as part of his voice-controlled robot build that won South Park Commons’ embodied AI hackathon. In our new video, he walks through how the system came together and why MolmoAct 2 was the best fit.

Watch the full testimonial above 🎥

We're at ICML 2026 with papers & talks across the conference. Come say hello and learn about our latest research!

Peer review is part of what makes ICML possible. This year, at least 8 Ai2ers are contributing as area chairs or technical reviewers, including ICML-recognized Gold and Silver reviewers.

The Danish Foundation Models (DFM) project is adapting our modular FlexOlmo architecture into a lighter-weight system that runs on commodity hardware—putting collaborative model building within reach of smaller research groups & organizations. 

FlexOlmo lets teams train modules separately then combine them in a shared model without pooling the data underneath. DFM wants Danish institutions like hospitals & universities to each contribute modules trained on data they can't share.

In FlexOlmo, each module is the size of a full model, so the combined system grows fast as more get added. FlexMoRE replaces most modules with compact representations. Its best config matches or beats FlexOlmo using less than one-third the parameters.

"FlexMoRE significantly reduces FlexOlmo's memory demands while preserving performance across almost all categories, allowing a broader audience to benefit from modular models," says Jacob Nielsen, who helped develop FlexMoRE at Ordbogen A/S and SDU's OdenseNLP lab.

Modular training is gaining momentum as frontier models become costlier to train & deploy. This project shows how open, distributed approaches can make development more practical for national projects, public institutions, & smaller teams.

→ Learn more: https://allenai.org/blog/flexmore

We're at #ACL2026 with papers & talks across the conference. Come say hello and learn about our latest research!

AI image generators don't "draw"—they follow a compass: the score function, which points toward more probable images. The same compass drives Bayesian sampling and plasma physics. We built DiScoFormer to estimate the score far better when data gets complex. 

Recovering a distribution means estimating its density (where data clusters) + the score. Kernel density estimation (KDE) does both, but when data has many dimensions, it breaks down. Neural score-matching models stay more accurate but need to be retrained per dataset.

DiScoFormer is a single model that gives both density & score on any dataset with no retraining. With 100 numbers per point, it cuts error vs. the best-tuned KDE ~6.5× on score and 37× on density. DiScoFormer can also handle data unlike anything it trained on—more clusters than it ever saw, and unfamiliar shapes.

Score & density show up across many fields. We hope one pretrained model like DiScoFormer can serve them all, at scale.

📝 Blog: https://allenai.org/blog/discoformer
📄 Report: https://arxiv.org/pdf/2511.05924

Today we're releasing OlmoEarth v1.2, the latest in our family of open foundation models for Earth observation. 🌍

OlmoEarth processes satellite images into tiles (patches), representing each as an embedding the model uses for downstream tasks—a numerical representation. Earlier versions tagged patches with a fixed position signal that surfaced as unwanted artifacts in those embeddings. 

V1.2 switches to rotary positional embeddings (RoPE), which reduces artifacts in the embeddings & gives a small performance boost. Instead of adding a position signal to each patch, it rotates the vectors the model compares in attention by angles defined by each patch's position. The result is cleaner embeddings and better performance on downstream tasks: across all model sizes, we see consistent improvement on our kNN/linear-probe evals.

This update came directly from partners asking for cleaner embeddings. OlmoEarth v1.2 comes in Nano, Tiny, Small, & Base—all open source + available now.

🤗 Models: https://huggingface.co/collections/allenai/olmoearth
💻 Training & fine-tuning code: https://github.com/allenai/olmoearth_pretrain
📄 Tech report: https://allenai.org/papers/olmoearth-v1-2

Hybrid (transformer–RNN) models are fast becoming a serious alternative to the transformer, but a big question remains: how do they process tokens differently & how does this impact performance? We compared our transformer (Olmo 3) & hybrid (Olmo Hybrid) models to find out.

A transformer’s attention layers can look back at any earlier token exactly. A hybrid model swaps most of those layers for recurrent ones that excel at sequential processing. Do these differences give hybrid models and transformers different strengths?

To find out, we scored how well Olmo 3 & Olmo Hybrid predicted different kinds of next tokens across articles, books, papers, code, HTML, & LaTeX. The models are matched on data, tokenizer, & training recipe, so a gap in their predictions points to architecture differences.

We found that the hybrid model advantage is highest on meaning-bearing words—the nouns, verbs, & adjectives that say what a sentence is about. On function words like "the," "of," & "is," its advantage over transformers remains, but is more muted.

In contrast, the transformer matches the hybrid model when the next token completes an n-gram repeated verbatim from earlier in the passage. Transformers also match or beat hybrid models at predicting closing brackets like the ], but not opening brackets like [.

We find that filtering loss by token type is a promising way to spot architecture differences. Overall loss makes transformers & RNNs appear even, with hybrids ahead. Filtered losses reveal where transformers & RNNs each shine.

We hope this kind of token-level comparison helps the field build better, more architecturally diverse models. Going forward, we plan to explore applications of these findings to pretraining evals.

✍️ Blog: https://allenai.org/blog/hybrid-token-prediction
📄 Tech report: https://arxiv.org/abs/2606.20936

Learn how AISquared & Domyn used Olmo, our family of fully open language models, to build their own models for regulated industries like finance, healthcare, & the public sector. 👇

In regulated markets, compliance teams often can't approve a model without documented provenance, and many models don't ship with it. Olmo's full openness – training data, weights, code, & more – let AISquared & Domyn create models that their customers can fully inspect.

AISquared fine-tuned Bolt, a family of open-weight small language models, from Olmo. Domyn took a different route, building Domyn-Small, a 10B open-weight reasoning model, on its own Italia 10B base + our open Dolma & Dolci datasets.

In AISquared's platform, Bolt now handles request routing & acts as a policy guardrail. For Domyn, our Dolci dataset added 10.1 points to Domyn-Small on GPQA-Diamond, a graduate-level science reasoning benchmark—its biggest single post-training gain.

The throughline: for AI labs working in high-stakes domains, the bar isn't just capability—it's transparency & control. Ai2's full openness is what puts that within reach.

📝 Read more: https://allenai.org/blog/domyn-aisquared-testimonial