Ever wondered how training dynamics differ between LLMs 🖋️... and Vision 👁️ models? We explore this and close the gap between VMs and LLMs in our #NeurIPS2024 paper "TrAct: Making First-layer Pre-Activations Trainable". Paper📜 Video🎥show more

This is how large language models turn objects to... vector representations. In this video, we explore how large language models (LLMs) convert objects into internal representations, especially when translating between languages like English and Hindi. Using real-world examples, we highlight the challenges of gender inference, grammatical structure, and why direct word-to-word translations often fail. If you're curious about how LLMs deal with multilingual contexts and what it takes to improve translation quality across languages, this video is for you. #LLMs #Vectors #LCMshow more

NeurIPS 2025 Paper: LLMs are Reinforcement Learners 🤯! Surprisingly,... we show that LLMs can solve RL tasks without any external component! We introduce Prompted Policy Search (ProPS), an RL method based only LLMs and in-context learning. [Paper]show more

🌍Are LLMs aware of cultural and legal safety in... today’s geo-diverse world? 🚀Introducing SafeWorld, our #NeurIPS2024 paper and benchmark assessing LLMs’ understanding of geo-diverse safety, based on cultural norms and policies across 50 countries and 493 regions/races. ⚖️We also propose a multi-dimensional framework for evaluating contextual appropriateness, accuracy, and comprehensiveness, revealing major gaps in current LLMs. 🧨To address this, we train SafeWorldLM using DPO, achieving SOTA performance and a 20% higher global human evaluator rating in helpfulness and harmfulness over competing models, including GPT-4o. 🔗Paper: 💻 GitHub: 🫶🏻This is a joint leading effort with Da Yin. Also many thanks to the amazing team Kung-Hsiang Steeve Huang Kai-Wei Chang, and Violet Peng for their hard work. Check out more details and results we conclude from our paper in the thread below. 🧵show more

How can we use small LLMs to shift more... AI workloads onto our laptops and phones? In our paper and open-source code, we pair on-device LLMs (ollama) with frontier LLMs in the cloud (OpenAI, Together), to solve token-intensive workloads on your 💻 at 17.5% of the cloud cost while maintaining 97.9% of the accuracy. See Gru and the Minions in action below, 🔉on please (h/t )!show more

Excited to share our work, Know3D, which connects LLMs'... reasoning ability and knowledge to 3D generative models. This increases the controllability and plausibility of unseen parts in the generated 3D shapes. Paper: Project page:show more

Photoshop for text. In our #CHI2025 paper “Textoshop”, we... explore how interactions inspired by drawing software can help edit text. We consider words as pixels, sentences as regions, and tones as colours. #HCI #NLProc #LLMs #AI Thread 🧵 (1/10)show more

How do LLMs actually work? 🧠 Watch our video... below and understand LLMs in 1 minute 👇show more

Why do we get a memory wipe in between... reincarnations? And how does the human incarnation cycle mimic Ai and LLMs…? The answer might surprise youshow more

A little video that - explains the relationship between... conventional LLMs and reasoning models (and agents), - philosophizes a about "from scratch" approaches, - and explains how to install Python & PyTorch requirements with uv.show more

#CVPR2026 Can frontier LLMs write PhD-level 3D vision code?... We introduce GeoCodeBench, a benchmark that asks models to read real 3D geometric vision papers and implement core functions. Best result so far: GPT-5 reaches only 36.6%. This suggests that scientific coding in 3D vision remains far from solved. Paper: Project:show more

Ever wondered how market-making works? 🤔 In today's video,... we explore who market makers are, what they do, and how everything works. Watch the full video:show more

🧵1/n LLMs significantly improve Evolutionary Algorithms for molecular discovery!... For 18 different molecular optimization tasks, we demonstrate how to achieve SOTA performance by incorporating different LLMs! Learn more in our new paper! Website: Code)show more

A conversation 2,000 years in the making. Using cutting-edge... AI, we digitally “resurrected” Confucius and Socrates. By training LLMs on their lifetime of wisdom, we made the impossible possible: a face-to-face dialogue between history’s greatest minds.show more

Speech-native models like Moshi sound great and answer fast,... but aren’t as smart as text LLMs. In our new paper, MoshiRAG, we show how Moshi can ask for advice from a text LLM or a knowledge base. The tricky part is how to do this in real time without adding latency. 🧵show more

Introducing GRID: the General Robot Intelligence Development platform, designed... for prototyping smart and safe robots rapidly using foundation models, LLMs, and simulation. Paper: Try now: GitHub: 🧵👇(1/N)show more

Robot AI brains, aka Vision-Language-Action models, cannot adapt to... new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:show more

What happens when art meets AI? Magic! Learn how... Huawei's computing power and data engineering helps help train LLMs, bridging the gap between traditional craft and cutting-edge tech, with AI creating a "magic brush"!show more