Berkeley AI Research

🚀 🚀 🚀 Excited to share our new paper: Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration What does it take for an agent to stay curious in a 3D world? The answer is memory. 🌐 Project: recuriosity.github.io 📄 Paper: arxiv.org/abs/2605.22814 💻 Code: github.com/recuriosity/re…

Excited to release @gepa_ai's optimize_anything: a universal API for optimizing any text parameter. It consistently matches or outperforms domain-specific tools optimizing code, prompts, agent harnesses, cloud policies, even visuals! If you can measure it, you can optimize it.

A new Science #Robotics study highlights an open-source, computationally light policy that can adapt to control unfamiliar quadrotors and stabilize against external perturbations like strong winds. @loiannog @jonas_eschmann scim.ag/4wrIQih

#RSS2026 Call for participants 📢 Excited to announce our RSS 2026 Workshop: Post-Training for Robotics Foundation Models, together with the first Real-World Reinforcement Learning Challenge! The workshop is held on July 13 in Sydney. posttraining-for-robotics.github.io。

AI agents are already going wild, but today’s red-teaming tools for them are still like toys 😢 🔥👽 After spending 20 months and $120K API credits, we are excited to finally open-source DecodingTrust-Agent Platform (DTap): the first controllable, realistic simulation platform for advanced AI agent red-teaming !! 🌍 DTap simulates 50+ real-world environments across 14 high-stakes domains, with realistic agent interfaces replicated from their official MCPs and GUIs. The environments are full-stack, interactive, fully parallelizable, and can be easily configured to reproduce arbitrary real-world attack scenarios, making agent red-teaming scalable and highly transferable to deployment settings. 🔥We also release DTap-Bench, a large-scale benchmark with ~7K agent red-teaming tasks and ~4K policy-grounded malicious goals. Each red-teaming task includes a sophisticated attack sequence across environment-, tool-, skill-, prompt-level injections, as well as their compositions, plus a handcrafted verifiable judge that checks the actual consequences in the environment. Using DTap-Bench, we evaluate popular agent frameworks and backbone models across diverse policies, risks, threat models, and attack strategies, revealing systematic vulnerabilities and zero-days in today’s agents! Paper link: arxiv.org/pdf/2605.04808 Platform + benchmark + code: decodingtrust-agent.com Join our Discord: discord.gg/V4fG6NcVc Read more below 👇

🎉Check out our preprint led by @LinkeLi_MGH @_ahmedmalaa with @pnatarajanmd : ) One of the biggest hurdles in proteomics is the non-replication of associations due to limited cross-platform correlation. We tackle this with ML models that bridge the two major ones, SomaScan and Olink, plus a tiered protein reliability system to guide which signals to trust. We validate against gold-standard measurements, AlphaFold 3 predicted structures, and 3 real-world applications, including replication of published dementia🧠and heart failure🫀associations, showing markedly improved cross-platform replicability. biorxiv.org/content/10.648…

What happens when you compare the distributions of real and simulated user behaviors? 🔍 The gap is large. We introduce a method to measure this gap and evaluate 24 LLM-based user simulators across coding and writing tasks. @convai_uiuc @MSFTResearch @berkeley_ai 🧵 1/N

The Summer ’26 issue of Berkeley Engineer magazine is here! Our cover story centers on how Professor @Ken_Goldberg is redefining robot manipulation with creativity and code. Read the issue: engineering.berkeley.edu/magazine