If you work on long-context compression for LLMs, you've seen the Gisting approach: add a few "gist tokens" and adjust the attention mask so all context flows into them. Elegant and simple… But we found that it COMPLETELY BREAKS when compressing more than just a few tokens 🤯

📢Excited to share our new paper "Motion-Aware Concept Alignment for Consistent Video Editing". A training-free framework for video semantic mixing: 🔁Blend new concepts into specific objects 🎯Maintain spatial stability & temporal coherence 📊Outperform basselines A thread🧵

📢I am attending #CVPR2025 (Jun 11 - 14). Come to our snap-research.github.io/Omni-ID/ poster to know more about how we achieved the highest ID preservation in personalization and further enables expression following in our follow ups. See you at Fri 4 - 6 pm, ExHall D Poster #326.

Web data, the “fossil fuel of AI”, is being exhausted. What’s next?🤔 We propose Recycling the Web to break the data wall of pretraining via grounded synthetic data. It is more effective than standard data filtering methods, even with multi-epoch repeats! arxiv.org/abs/2506.04689

🚀 Our MatchDiffusion was accepted to ICCV 2025 in Hawaii! 🌺 We generate two synchronized videos from text prompts—designed for match-cuts. Results: matchdiffusion.github.io Paper: arxiv.org/abs/2411.18677 #MatchDiffusion #ICCV2025 #DiffusionModels #TextToVideo #GenerativeAI

New paper out ! Train Long, Think Less. We introduce Curriculum GRPO, start with long reasoning chains, then progressively tighten token budgets to train LLMs that think better with fewer tokens. 📈 +Accuracy, 🔻Token usage, across GSM8K, MATH500 & more. Special thanks to all

AI, decoded in under a minute. Prof. Bernard Ghanem Bernard Ghanem from #KAUST, ranked #1 in the Middle East for producing #AItalent, breaks it into four pillars. The expertise driving Saudi Arabia’s bold #AI future.

We released 44B synthetic tokens from our CoT-guided rewriting, offering higher quality pretraining data than the average human-written web texts📈 🤗Data: huggingface.co/datasets/faceb… 📜Paper: arxiv.org/abs/2506.04689 (accepted at #COLM2025) Excited to see what the community builds!

We just released Hala: open, state-of-the-art Arabic instruction & translation models! ✨ Includes: • 1.2B Translation model (very light-weight) • 4.6M Arabic Instruction Tuning Dataset • 4 models (350M–9B) 📄 Paper: huggingface.co/papers/2509.14… Don't forget to upvote :)!! 🤗

Hala: New Arabic-centric models released on Hugging Face A family of state-of-the-art instruction and translation models, built with a novel translate-and-tune pipeline. Achieves SOTA performance in "nano" (≤2B) and "small" (7-9B) categories on Arabic benchmarks.

🔥 Daily AI Paper (2025-09-18) 📄 Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale 🔗 chatpaper.ai/dashboard/pape… #AI #ML #ChatPaper

1. Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale 🔑 Keywords: Arabic-centric, Hala, translate-and-tune pipeline, lightweight language model, NLP 💡 Category: Natural Language Processing 🌟 Research Objective: - The primary goal is