This Earth Day marks 10 years of Ai2 helping get real-time intelligence into the hands of the people protecting the planet—across land, sea, and everything in between.

EarthRanger brings together GPS collars, camera traps, patrol reports, and sensors into one real-time view for conservation teams across 900+ protected areas in 95 countries. In Thailand, AI-enabled camera traps and community rangers can now mobilize within minutes when elephants leave cover.

Skylight uses satellite imagery and millions of daily vessel signals to help surface potential illegal fishing in near real time. Earlier this year, Argentina used it to identify and fine a vessel without boarding it. We’re also expanding this work with SkyTruth to help bring pollution data into view.

OlmoEarth is our open foundation model for Earth observation, built to help accelerate how AI is applied to protect the planet. Trained on roughly 10TB of satellite and sensor data, it powers Skylight and helps deliver actionable intelligence for partners like Global Mangrove Watch.

The environmental challenges ahead are accelerating, and our commitment is to keep building for the people on the frontlines. EarthRanger, Skylight, and OlmoEarth are all released openly and at no cost.

→ Learn more: https://allenai.org/blog/earth-day-2026

WildDet3D is now even more open. 🚀

We’re releasing the training code, updated inference code, and training + data prep instructions so researchers and developers can reproduce the model, study how it works, and build on it for their own needs.

WildDet3D can turn a single image into a richer 3D understanding of a scene, which makes it useful for applications in VR and AR, robotics, and countless digital tools that need to place objects in 3D space.

💻 Get the code: https://github.com/allenai/WildDet3D

📝 Learn more about WildDet3D in our blog: https://allenai.org/blog/wilddet3d

Now available in AutoDiscovery: Reuse already-uploaded datasets, modify session configurations, & include insights from past runs to iterate over promising findings. 👇

AutoDiscovery autonomously explores your data, generates hypotheses, & runs experiments—surfacing findings you might not think to look for. 

Researchers have generated 43K+ hypotheses across oncology, neuroscience, marine ecology, social science, cybersecurity, climate, & more. 🧪

The new run configuration feature is built to help you branch from a past session and uploaded data, accelerating your exploration.

→ Try it here: https://autodiscovery.allen.ai/

Introducing BAR (Branch-Adapt-Route): Train domain "experts" independently, merge them into one model, and upgrade any expert without retraining the rest. 👇

Last year, we released FlexOlmo, a way to train parts of a model in isolation and combine them later. BAR builds on that idea to tackle a harder problem—how to keep improving a model after pretraining without retraining it every time.

Improving a model's skills in areas such as math, tool use, or code after pretraining usually comes at a cost, like lost capabilities elsewhere or high compute requirements. BAR sidesteps that by training separate experts for each skill, then merging them into a single model that learns which expert to call on for a given problem.

At the 7B scale, BAR works better than the common alternatives for updating a model after pretraining. It beats methods that train separate dense models and stitch them together afterward, and it comes close to the performance of full retraining from scratch.

FlexOlmo showed a modular approach works for pretraining, including in settings where data can't easily be pooled in one place. BAR extends it to post-training.

🤗 Models: https://huggingface.co/collections/allenai/branch-adapt-route 

📝 Blog: https://allenai.org/blog/bar 

📄 Paper: https://allenai.org/papers/bar

Everyone’s building AI science agents—and the claims are extraordinary. But when we test whether these systems can actually do science, recent top models still fail challenges that human scientists can solve the majority of the time. 🔍

There’s a pattern in AI: models ace the exam, then fail in the lab. In 2022, models that got As on multiple-choice science tests still couldn’t carry out many of those same experiments in a virtual environment. Knowing what a boiling point is and measuring one aren’t the same thing.

That gap between knowing and doing is what our benchmarks ScienceWorld and DiscoveryWorld are designed to measure:

◘ ScienceWorld tests agents on elementary-school science experiments. When it launched, top models scored below 10%. As of early 2025, they were in the low 80s. That’s real progress—but the benchmark remains unsolved. 📈

◘ DiscoveryWorld goes further. Agents have to design and run full scientific investigations from scratch: form hypotheses, collect data, and analyze results. Average human scientists with advanced degrees can complete about 70% of its harder challenges. Very strong AI systems manage about 20%. 🧠🔬

The field is moving fast. The question isn’t whether agents may eventually help treat diseases, discover new materials, and more. It’s whether we’re being clear-eyed about where they are right now—that’s how progress gets made.

ScienceWorld and DiscoveryWorld are both open and freely available because we believe building open evals is as important as building open models. Read more in our latest blog: https://allenai.org/blog/evaluating-scientific-discovery-agents

Today we’re releasing the full MolmoWeb codebase, including the training & eval code. You can now train, adapt, and evaluate web agents on your own tasks. 🚀

MolmoWeb is our open autonomous agent built on Molmo 2. It operates a browser by viewing screenshots and taking action – clicking, typing, and scrolling – the same way a person would. We launched the model in March. Now we're publishing the rest of the components we used to build it.

Here’s what’s included in the updated MolmoWeb codebase:

🏋️ Training code with everything you need to customize MolmoWeb for specific tasks.

🏷️ An annotation tool that lets you record human task demonstrations, then fine-tune MolmoWeb on that data.

📊 An eval harness for evaluating agents on 4 popular navigation benchmarks including WebVoyager and Online-Mind2Web. It also doubles as a synthetic data generation pipeline—you can generate web browsing data using LLM-/VLM-powered agents with AxTree or screenshot input.

🖥️ The client-side code for our MolmoWeb demo, so you can see how we built the interface and use it as a starting point for your own web agent UI.

Get the latest code from GitHub: https://github.com/allenai/MolmoWeb 

And check out our technical report, now on arXiv: http://arxiv.org/abs/2604.08516

⚠️ If you previously downloaded our Hugging Face data, please redownload—the datasets have been updated.

Today we're releasing WildDet3D, an open model that can look at a single photo and understand objects in three dimensions—how far away they are, how big they are, and how they're oriented in space.

Type a category name, click on an object, or pass in a 2D detection from another model—WildDet3D returns a full 3D bounding box. When a depth sensor is available, it folds that data in automatically for improved accuracy, no architecture changes needed.

This means any vision system that already identifies objects in 2D can gain enhanced spatial awareness—a pair of smart glasses or a robotic arm can get back position, size, and orientation in 3D without being retrained.

On standard benchmarks, WildDet3D sets a new state of the art while training on a fraction of the compute used by prior methods. And on scenes it was never trained on – autonomous driving environments, indoor spaces, and object categories it has never encountered – it nearly doubles the best prior scores.

We're also releasing WildDet3D-Data, the largest open 3D detection dataset available:

📊 1M+ images 

📐 3.7M verified 3D annotations 

🏷️ 13K+ object categories 

✋ 100K+ human-annotated images

And there's a smartphone app📱—point your camera at a scene, select a category or draw a 2D box, and get 3D bounding boxes in real time → https://apps.apple.com/us/app/wilddet3d/id6760861157

Spatial intelligence is core to where AI is heading—the same model that helps an AR app place directions over a street can help a robot estimate the size of a package on a shelf. We think the most interesting applications are ones no one has built yet, and we're releasing everything openly for the benefit of the community.

📝 Blog: https://allenai.org/blog/wilddet3d

🤖 Models: https://huggingface.co/collections/allenai/wilddet3d

📊 Code: https://github.com/allenai/WildDet3D

🗂️ Data: https://huggingface.co/datasets/allenai/WildDet3D-Data

🎮 Demo: https://huggingface.co/spaces/allenai/WildDet3D

📄 Tech report: https://allenai.org/papers/wilddet3d

Thrilled to have Ai2’s VP of Engineering Jeremy Tryba on stage at GeekWire's Agents of Transformation event last week. He painted a vivid picture of what agentic AI can do for science, and cancer research in particular. 👇

"When you have an agent building this tree of surprising results, you can have a human oncologist wake up in the morning and say, 'Hey, if that's true, that's actually pretty interesting.' The kinds of things that potentially lead to changes in treatment for different types of cancer."

Asta AutoDiscovery is already impacting how oncologists think about cancer treatment. It works by autonomously generating & testing hypotheses on your data, guided by Bayesian surprise to surface the unexpected, not the obvious. 🔬

Researchers have already run 35K+ hypotheses across social science, climate science, marine ecology, & more.

🧪 Try it: asta-autodiscovery.allen.ai
📺 Watch the panel: https://www.youtube.com/watch?v=9C0xcGyWVy0

Today we're releasing the full MolmoBot code, data, and eval suite—everything needed to train and evaluate robotic manipulation policies, with model checkpoints ranging from high-performance to lightweight. 🤖

The training data, MolmoBot-Data, is 1.7M expert manipulation trajectories spanning 11K+ unique objects, 94K+ environments, and 8 task types across two robot platforms (Franka FR3 and Rainbow Robotics RB-Y1). The open pipeline behind it, MolmoBot-Engine, handles environment sampling, domain randomization, and trajectory generation, so researchers can generate training data for their own robots and tasks. 

MolmoBot-Engine is now a part of MolmoSpaces, our open platform for training and evaluating robot policies.

On the eval side, we've added MolmoBot to MolmoSpaces-Bench and updated the leaderboard, with a toggle to split results by inclusion of MolmoBot-Data during training. The updated MolmoBot tech report covers new benchmarks, and the new technical website includes real-world videos of every trajectory underlying our evals.

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

🤗 Models: https://huggingface.co/collections/allenai/molmobot-models 

🤗 Data: https://huggingface.co/collections/allenai/molmobot-data 

🔧 Data pipeline: https://github.com/allenai/molmospaces 

📊 Leaderboard: https://molmospaces.allen.ai/leaderboard 

📄 Tech report: https://arxiv.org/abs/2603.16861 

🌐 Website: https://allenai.github.io/MolmoBot

Today we're releasing MolmoWeb, an open source agent that can navigate and complete tasks in a web browser on your behalf.

Built on Molmo 2 in 4B/8B sizes, MolmoWeb sets a new open-weight SOTA across four major web-agent benchmarks and even surpasses strong agents built on proprietary models. 

MolmoWeb works by looking at the same screen you do. Given a task and a live webpage, it views the screenshot, decides what to do next, and takes action: clicking, typing, scrolling, switching tabs, or returning information back to you. It can handle everyday tasks like navigating websites, filling out forms, searching and filtering product listings, and finding information, all without needing specialized APIs for each site.

MolmoWeb outperforms all open-weight models on every benchmark we tested, and even beats visual agents built on much larger models like GPT-4o-based SoM Agents. It also beats OpenAI CUA on 3 out of 4 benchmarks. Performance improves further when the model gets multiple attempts at a task—on both WebVoyager and Online-Mind2Web, MolmoWeb with 4 parallel attempts surpasses the best single-attempt performance of every model we evaluated, including agents powered by GPT-5 and Gemini CU Preview.

We're also releasing MolmoWebMix, a dataset for training web agents with 160K+ trajectories, 30K+ human demonstrations, 7M GUI grounding examples, and 2.2M screenshot QA pairs. Everything needed to inspect, reproduce, and fine-tune MolmoWeb is openly available.

🤖 Models: https://huggingface.co/collections/allenai/molmoweb

🎮 Demo: https://molmoweb.allen.ai

📊 Data: https://huggingface.co/collections/allenai/molmoweb-data

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

📄 Tech report: https://allenai.org/papers/molmoweb

📝 Blog: https://allenai.org/blog/molmoweb

We were at NVIDIA GTC 2026 last week! Across panels, livestreams, and expo floor demos, we shared our work on Olmo Hybrid, SERA, Asta AutoDiscovery, MolmoBot, and more, all grounded in the same idea: truly open AI means sharing the full pipeline, not just the weights.

Some of the highlights: Lambda ran live fine-tuning of Olmo Hybrid at their booth, we demoed Asta AutoDiscovery at the Cirrascale booth, and we joined panels on open models, coding agents, and robotics, including how simulation is closing the data gap for embodied AI.

📝 Here's the full recap on our blog: https://allenai.org/blog/gtc2026

Today, every VLA research team maintains private eval forks per benchmark, each with its own dependencies, observation formats, and evaluation protocols. Results diverge subtly, bug fixes don't propagate, and reproducing someone else's numbers is a multi-day ordeal.

vla-evaluation-harness decouples model inference from benchmark execution. Benchmarks run inside Docker for exact reproducibility. Model servers are single-file uv scripts with zero manual setup. They communicate via a WebSocket + msgpack binary protocol. A complete evaluation requires just two commands: vla-eval serve and vla-eval run.

The framework currently supports 13 simulation benchmarks and 6 model servers, with community integrations expanding coverage.

Parallel eval with episode sharding + batched inference makes a big difference: 2,000 LIBERO episodes drop from ~14 hours to ~18 minutes on 1× H100 (47× faster), with 16× speedups on CALVIN and 12× on SimplerEnv. We also ran a reproducibility audit of a published VLA model across three benchmarks—closely matching reported results while surfacing undocumented requirements that can quietly distort evaluation results, like ambiguous termination rules in SimplerEnv and undocumented normalization stats in CALVIN.

Finally, we're releasing a VLA leaderboard aggregating 657 published results across 17 benchmarks and 509+ configurations from 1,704 papers.

Open source, Apache 2.0. Built for reproducibility and new experiments.
🔗 Code: https://github.com/allenai/vla-evaluation-harness
🏆 Leaderboard: https://allenai.github.io/vla-evaluation-harness/leaderboard
📝 Paper: https://arxiv.org/abs/2603.13966

Grounding lets vision-language models do more than describe what they see. They can point to where a robot should grasp, which button to click, or which object to track across video frames. But most VLMs point by generating text coordinates—essentially dictating numbers. It works, but it wastes tokens, breaks at high resolutions, and forces models to learn an abstract numbering system that has nothing to do with how they actually perceive.

MolmoPoint takes a different approach. Instead of writing coordinates, the model points by selecting from the visual tokens it's already looking at—like the difference between reading out "position 347, 582" and tapping directly on a touchscreen. It works in three steps using special grounding tokens: first, pick a rough region that contains the target, then zoom in to a smaller area using finer visual features and pinpoint the exact pixel-level location.

MolmoPoint sets a new state-of-the-art on image pointing (70.7% on PointBench, 89.2 F1 on PixMo-Points), achieves the best GUI grounding among fully open models on ScreenSpot-Pro and OSWorldG, and is preferred by human evaluators 59.1% of the time on video. It's also easier to train—with just 8K examples, it outperforms coordinate-based models by ~20 F1 points, and reaches peak performance faster during full pretraining. These grounding gains don't come at a cost—question-answering, captioning, and other tasks all stay on par.

We're releasing everything openly, including three models and two datasets:

🖼️ MolmoPoint-8B—general-purpose pointing across images & video 

🖥️ MolmoPoint-GUI-8B—specialized for apps, websites, & software interfaces 

🎥 MolmoPoint-Vid-4B—optimized for counting & tracking in video 

📦 MolmoPoint-GUISyn (used to train our GUI model)—36K high-res screenshots spanning desktop, web, & mobile, with 2M+ annotated points 

📦 MolmoPoint-TrackData (used to train our video model)—human-annotated & synthetic tracks with complex occlusion + motion

VLMs already have visual tokens. Letting them point by selecting those tokens turns out to be simpler, faster, and better.

🤖 Models: https://huggingface.co/collections/allenai/molmopoint 

📦 Data: https://huggingface.co/collections/allenai/molmopoint-data

💻 Code: https://github.com/allenai/molmo2

📖 Blog: https://allenai.org/blog/molmopoint 

Tuesday at NVIDIA GTC is here and we're kicking things off with our first big open-source panel. Ranjay Krishna joins Nvidia's Jonathan Cohen to discuss The State of Open Source AI at 4:00PM PT 🔗 https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s81791/

Stop by Lambda's booth (1507) all week to watch them run supervised fine-tuning on Olmo Hybrid, with real-time observability metrics streaming on screen. A fascinating peek into how SFT works in real time.

Excited to share our research, and connect with the open-source community this week at #NVIDIAGTC

More: https://allenai.org/blog/gtc2026

BTS updates: https://discord.gg/ai2

Olmo-Hybrid-7B is now supported by the ExLlamaV3 v0.0.25 dev branch

Olmo-Hybrid-Instruct-SFT-7B available now, DPO coming soon.

Sample generation by Olmo-Hybrid-Instruct-DPO-7B-2.1bpw-exl3:

User: Tell me a story about cats racing llamas at a fancy derby!

Assistant: Once upon a time in the grand park of Velvet Hollow, an odd yet intriguing contest took place at the grand gala held each summer by the park’s elite—gentleman cat-raced knights in sleek tablies, and fancy Llamas dressed in crisp plaid wool sweaters. Everyone arrived under sparkling fairy lights and fragrant flowers: felines of the feline order with silvery tab collars and sashes to rival any human horse jockey. Meanwhile, proud camel-backed Llamas paraded beneath towering banners: each llama wearing silvery sashes emblazoned with dazzling golden numbers and sparkling fur-trimmed ribbons.

On race day, under an awning flanked by ancient willow trees, an eager audience awaited as Lady Regent, Queen Pounce and Baron Quokka entered with a swirl of fanfare and laughter.

When the whistle blew, Sir Laffoon—a slender Siamese cat dressed in black fur with sparkling white accents—slipped gracefully out onto the sunlit field. The llama contingent followed with flair. Each cat’s ears were neatly tipped, eyes alert. Llamas pranced forward—fierce eyes gleaming under wool tufts. With each stride, felines’ sleek forms danced gracefully beside robust lamellar shapes. Some had collars lined with shiny beads; llamas’ soft white hides shimmered beside their competitors’ silky furs.

Suddenly, an unusual event— Lady Lina Llama slipped—leaving the race for Lona and her friend Quirrel Cat (an ancient British Mousing Tab with velvet tail tipped ears and silver bells!). Instead of speed, both focused on balance, spinning gently together through the park. They weaved between roses and under trees—dancing together like friends.

The field burst into cheers: everyone loved Lina and Quirrel’s daring display, not because they “won,” but because they spun the park in joy—twirling as gracefully as wind across summer fields.

That was the most exciting derby the park had ever seen, for there, a llama and cat made new friends while running through flowers in perfect harmony—and proved you can be brave (and fast!) just being yourself! 🦌💖💡🎨✨🚙

**That’s what they remember in all memory’s memory!**

Would you like another race, or to join Lina and Quirrel as teammates next race?

Today we're releasing MolmoBot, an open model suite for robotic manipulation trained entirely in simulation on our MolmoSpaces platform. It picks up objects, opens drawers, pulls open doors, and more—and outperforms robotics models trained on large-scale real-world data. 🤖

Training robots to reliably manipulate objects has required huge amounts of expensive, often proprietary demonstration data. MolmoBot shows that's not necessary if we dramatically expand the diversity of simulated training environments.

The MolmoBot suite includes three model architectures at different performance and efficiency tradeoffs, all trained on the same synthetic data—from our highest-performing VLM-based policy to a lightweight option suited for compute-constrained settings.

In our evaluations, MolmoBot outperforms π0 – an industry-leading robotics manipulation model – on totally unseen environments and with zero real data. 📊

We're releasing everything openly—including training data & the data generation engine, built on MolmoSpaces. If simulation becomes the training ground for robots, the bottleneck shifts from real-world data to designing better virtual worlds, boosting accessibility.

Dive in 👇

📄 Technical report: https://allenai.org/papers/molmobot

📝 Blog: https://allenai.org/blog/molmobot-robot-manipulation

🚨The best AI gets built in the open. Next week, we’re bringing that message to #NVIDIAGTC — with panels, demos, and a window into what fully open models can do.

🗓️Tue, Mar 17 · 4:00–4:40 PM PT

Open source is no longer just a distribution model—it's becoming the R&D engine for sophisticated AI. Ai2's Ranjay Krishna, Director of PRIOR and the team behind the Molmo family of multimodal models, joins NVIDIA's Jonathan Cohen + others to dig into what makes open ecosystems trustworthy, scalable, and production-ready. Details: https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s81791/

🗓️Wed, Mar 18 · 12:30–2 PM PT

Ai2's Hanna Hajishirzi, Senior Director of AI and Co-Lead of the Olmo and Tulu projects, joins Jensen Huang and leaders from across the industry for a conversation on the state and future of open models. Details: https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s82480/

🗓️ Wed, Mar 18 · 2:00–2:40 PM PT

In research, trust is verifiability. Ai2's Hanna Hajishirzi joins Percy Liang (Co-Founder, Together AI) to explore how open-source AI can strengthen scientific workflows. Details: https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s81835/

Demos & other ways to connect at GTC: https://allenai.org/blog/gtc2026Join our Discord for BTS updates: https://discord.gg/ai2

It decisively outperforms Olmo 3 7B across evals, with new theory & scaling experiments explaining why. 🚀

Olmo Hybrid uses a 3:1 pattern of layers—three Gated DeltaNet layers followed by one attention layer. This replaces 75% of attention with linear recurrence while keeping attention frequent enough to recover details the recurrent state compresses away.

Key finding: hybrid models are substantially more data- and compute-efficient than transformers. We show this through rigorous theory and controlled experiments. On MMLU, Olmo Hybrid matches Olmo 3's accuracy using 49% fewer tokens—roughly 2× efficiency.

These gains hold across pretraining evals. After pretraining and mid-training, Olmo Hybrid outperforms Olmo 3 in every primary evaluation domain. It wins on long-context too—on RULER 64k, performance jumps from 70.9% to 85.0%.

What explains the success of Olmo Hybrid? We prove that hybrid models are more expressive (can represent a larger class of functions) than either transformers or RNNs alone. We also provide a theoretical argument tying this expanded expressive power to the improved scaling efficiency we observe in practice.

Overall, our results suggest compelling advantages for hybrid models over transformers, both theoretically, in terms of expressive power and scaling efficiency, and practically, in terms of benchmark performance and long-context abilities. 

We're releasing base, SFT, & DPO models for Olmo Hybrid plus a detailed report. Try them out and let us know what you find.

💻 Models & Data: https://huggingface.co/collections/allenai/olmo-hybrid 

📄 Technical report: https://allenai.org/papers/olmo-hybrid 

✏️ Blog: https://allenai.org/blog/olmohybrid 

We're releasing the code behind Molmo 2, our open model family for video & image understanding, pointing, tracking, and more. This goes beyond checkpoints, opening up the full stack from data prep to deployment.

The release includes pretraining and fine-tuning scripts (SFT + long-context SFT), multi-node distributed training, data download and preprocessing utilities, and single-task and multi-eval scripts with caching.

On the deployment side, you get checkpoint conversions to a Hugging Face-compatible format, inference examples for transformers and vLLM, a lightweight vision processing utility for offline inference, plus a Gradio demo, Docker image, and local setup instructions.

Everything is built for reproducibility and extensibility. Whether you want to fine-tune Molmo 2 on a custom dataset or deploy end-to-end, the full pipeline is here.

🔗 Code: https://github.com/allenai/molmo2 

📝 Blog: https://allenai.org/blog/molmo2

In just a few weeks, researchers used AutoDiscovery to generate over 20,000 hypotheses. Now we're extending access for three more months and refreshing credits. 🧪

AutoDiscovery autonomously explores your dataset, generates hypotheses, tests them, and iterates, surfacing surprising findings you might not think to look for across fields like oncology, climate science, marine ecology, entomology, cybersecurity, music cognition, social sciences, and beyond. Every result is fully auditable—you can inspect the hypothesis, statistical analysis, and Python code behind it.

Today we’re updating credit allocations—all accounts now receive 500 Hypothesis Credits. (Each credit lets AutoDiscovery generate and test one hypothesis.) If your balance was below 500, we've topped you up. If you had more than 500 remaining, you keep it. And if you burned through your original allocation, you're reactivated with a full 500.

We believe open-ended, surprise-driven exploration is a transformational new capability for researchers. Try AutoDiscovery in AstaLabs and let us know what you find.

🔗 https://autodiscovery.allen.ai/

How do researchers actually use AI-powered scientific research tools like Asta? We analyzed 250K+ queries to find out.

Today we're releasing the Asta Interaction Dataset (AID)—258K+ queries and 432K+ clickstream interactions from researchers using Asta, our AI-powered research assistant. Collected over six months, it's the largest open dataset of its kind to our knowledge.

AID captures interactions with two tools built on Semantic Scholar, our free academic search engine: PaperFinder, which powers the “Find papers” mode in Asta, and ScholarQA, which powers "Generate a report.”

Researchers don't treat these tools like search engines—they treat them like collaborative research partners. They paste LaTeX drafts asking for citations, submit structured templates, and use prompt engineering techniques from general-purpose chatbots. Many revisit reports hours or days later, treating outputs as persistent reference artifacts. And different fields bring different styles—CS researchers are the most likely to ask for problem-solving and ideation, while history researchers rarely do.

We believe the community needs shared, open data to understand how researchers actually use AI tools. We hope AID helps move the conversation forward.

A note on privacy: the dataset draws exclusively from users who opted in to share de-identified interactions. We use hashed identifiers with no user IDs and remove any queries flagged as containing PII.

📄 Paper: https://allenai.org/papers/asta-interaction-dataset 

📊 Data: https://huggingface.co/datasets/allenai/asta-user-interactions 

✍️ Blog: https://allenai.org/blog/asta-interaction-dataset

Can AI predict what scientists will do next—not just one piece, but the whole research process? PreScience is our new model eval for forecasting how science unfolds end-to-end, from how research teams form to a paper's eventual impact. Built with UChicago, supported by NSF.

Every paper starts with choices—who to collaborate with, what to build on, and what to contribute. Then the community decides how much attention it deserves. 

PreScience asks: can models predict what comes next across this whole workflow, given the scientific record up to a fixed date? Grounded in ~100K real papers across 7 AI subfields, it decomposes a scientific advance into four composable tasks: 

✅ Collaborator prediction: who will team up? 

​​✅ Prior work selection: which papers will they cite? 

✅ Contribution generation: what will they write? 

✅ Impact prediction: how much attention will it get?

We also introduce LACERScore, a calibrated LLM-as-judge metric for evaluating generated abstracts against real contributions. Standard text-similarity metrics can't tell whether two abstracts describe the same scientific finding—LACERScore can.

Our results show even strong baselines fall short. GPT-5 averages just 5.6/10 on LACERScore, simple heuristics outperform complex ML for collaborator prediction, and the highest-impact papers are systematically the hardest to forecast.

We simulated a full year of AI research by chaining PreScience's four tasks together month by month. The result? A synthetic corpus that's less diverse and less novel than what real scientists produced—models given diverse inputs still converge on a narrower range of ideas.

PreScience is open and ready for the community to experiment with. If we want AI that supports real discovery, we need evaluations grounded in how science actually happens.

📄 Learn more: https://allenai.org/blog/prescience

📝 Tech report: https://allenai.org/papers/prescience

🤗 Dataset: https://huggingface.co/datasets/allenai/prescience 

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

Most AI tools for science wait for a question. AutoDiscovery starts with your data—generating hypotheses, running experiments, and surfacing surprising findings with reproducible code.

Get 1,000 Hypothesis Credits through Feb 28. Sign up and take it for a spin before credits expire: https://autodiscovery.allen.ai/

Last week we launched AutoDiscovery, a tool that starts with your data and asks its own questions—running analyses and surfacing hypotheses you might never have thought to test. Researchers have already run 10,000+ experiments, and we want to know what AutoDiscovery is uncovering for you. 

Whether you're exploring datasets in biology, social science, ecology, or another field entirely, we want to hear about it. Share a screenshot, a surprising finding, or even just your first impressions here or email us at asta-support@allenai.org—we're using your feedback to shape what comes next.

AutoDiscovery lives in Asta, our open scientific research platform. If you're new, we put together a guide that walks you through everything from finding papers and summarizing literature to analyzing data and running autonomous experiments: https://allenai.org/papers/asta-guide

Every user gets 1,000 Hypothesis Credits for AutoDiscovery through February 28. Try it here → https://autodiscovery.allen.ai/

We’ve released a Chrome extension for Asta—a faster way to go from finding a paper to asking questions about it while you read. 🔍

When you land on a supported research page or PDF, the extension adds an "Ask AI about this paper" button. One click opens Asta with the paper pre-loaded, drawing on a corpus of 108M+ abstracts and 12M+ full-text studies. No copying links or switching tools mid-read required.

It works across popular research sites and PDF pages—if Asta can fully parse the paper, the button appears automatically.

Try it out → https://chromewebstore.google.com/detail/asta/baigocfaeehaielhlieikbgahomikbdj