New work on automated prompt engineering for personalized text-to-image generation: PRISM: Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation Paper + Code: kellyyutonghe.github.io/prism/

www.llama.com Llama4 models are out! Open sourced! Check them out: “Native multimodality, mixture-of-experts models, super long context windows, step changes in performance, and unparalleled efficiency. All in easy-to-deploy sizes custom fit for how you want to use it”

New work #ICLR2025 on “Dissecting Adversarial Robustness of Multimodal LM Agents” that shows that one can successfully break latest agents that use black-box frontier LLMs, including agents that perform reflection and tree search. Paper + Code + Data: chenwu.io/attack-agent/

Excited to be at the GenAI Summit at UCSD! I'll be sharing our latest work on VisualWebArena, inference-time tree search, and Internet-scale training of LLM Agents. genaisummit2025.ucsd.edu

1/4 New Work on InSTA: A pipeline for Internet-scale training of web agents across 150k diverse websites without human annotations. Paper + Code: data-for-agents.github.io Environment: github.com/data-for-age...

1/3 New work on Self-Regulation and Requesting Interventions: Enabling agents with a limited intervention budget to decide when to seek help: Paper: soyeonm.github.io/self_reg/ We develop an offline framework that trains a helper policy to request interventions by combining LLM-based PRMs with RL

🌲 Ruslan Salakhutdinov (@rsalakhu.bsky.social) from CMU (@scsatcmu.bsky.social) opened the workshop with a talk on Tree Search for Language Model Agents. Timestamp 36:20 in neurips.cc/virtual/2024... 📎 arxiv.org/abs/2407.01476 #NeurIPS2024 #AdaptiveFoundationModels

🎉 Had fun at #NeurIPS2024 Workshop on #AdaptiveFoundationModels! 🚀 Speakers: @rsalakhu.bsky.social @sedielem.bsky.social Kate Saenko, Matthias Bethge / @vishaalurao.bsky.social Minjoon Seo, Bing Liu, Tianqi Chen 🌐Posters: adaptive-foundation-models.org/papers 🎬 neurips.cc/virtual/2024... 🧵Recap!

With my amazing students and collaborators at @neuripsconf.bsky.social in Vancouver!

Carnegie Mellon University at NeurIPS 2024 – Machine Learning Blog | ML@CMU | Carnegie Mellon University Carnegie Mellon University is proud to present 194 papers at the 38th conference on Neural Information Processing Systems (NeurIPS 2024) blog.ml.cmu.edu/2024/12/02/c...

1/2 New work on Evaluating Deep Unlearning in Large Language Models. Paper: arxiv.org/abs/2410.15153 Unlearning specific facts in LLMs is challenging because the facts in LLMs can be deduced from each other. This work proposes a framework for deep unlearning of facts that are interrelated.

What is happening?! Who is this? 😆

Hello BlueSky