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Awesome work!

The next frontier for AI shouldn’t just be generally helpful. It should be helpful for you! Our new paper shows how to personalize LLMs — efficiently, scalably, and without retraining. Meet PReF (arxiv.org/abs/2503.06358) 1\n

Introducing Deep Research for arXiv Ask questions like 'What are the latest breakthroughs in RL fine-tuning?' and get comprehensive lit reviews with trending papers automatically included Turn hours of literature searches into seconds with AI-powered research context ⚡

2024: Let’s benchmark LLMs using 15 problems (AIME). 2025: Let’s benchmark LLMs using 6 problems (USAMO). I have a worrying scaling law for LLM benchmarks.

Ever wonder how LLM developers choose their pretraining data? It’s not guesswork— all AI labs create small-scale models as experiments, but the models and their data are rarely shared. DataDecide opens up the process: 1,050 models, 30k checkpoints, 25 datasets & 10 benchmarks 🧵

It can be helpful to pinpoint the in-context information that a language model uses when generating content (is it using provided documents? or its own intermediate thoughts?). We present Attribution with Attention (AT2), a method for doing so efficiently and reliably! (1/8)

Interested in Data-centric AI?? Check out our ICML Conference workshop 🥳🥳 #icml25

david rein See work from Alex Madry's lab led by Eddie Vendrow x.com/aleks_madry/st… Aleksander Madry

Eddie and I first discovered this behavior suddenly appear in the middle of a math problem—using the error viewer we created for Platinum Benchmarks: platinum-bench.csail.mit.edu/inspect?model=… We then realized we could reproduce this behavior directly across models!

🚨Are LLMs truly ready for autonomous data science? Real-world data is messy—missing values, outliers, inconsistencies—and if not handled properly, can lead to wrong conclusions. 🌟We introduce RADAR, a benchmark evaluating whether LLMs can handle imperfect tabular data. 🧵

We are thrilled to announce that CV for Ecology Workshop is returning for its second year at #ICCV2025 in Honolulu, Hawaii! If your work combines computer vision and ecology, submit a paper and join us! Deadlines: July 4 (Proceedings) / July 22 (Non-Archival)

Calling for papers to our ICCV Workshop on Computer Vision for Ecology in Hawaii! The deadline is July 4 for archival proceedings: cv4e.netlify.app