👀Humans compare images by looking back and forth. Many open-weight VLMs encode each image independently, and defer comparison to the LM. We introduce SVE: Stateful Visual Encoders for Vision-Language Models, where the visual encoder itself becomes change-aware. 🌐Project: statefulvisualencoders.githu… 📰Paper: arxiv.org/abs/2606.04433 💻Code: github.com/StatefulVisualEnc… 1/n

Static benchmarks are dying — they tend to get saturated quickly. Evaluation and training data should co-evolve with frontier models. We released BenchEvolver — a framework that automatically evolves saturated problems into harder, verified tasks for evaluating frontier models, which can also serve as useful self-improvement signals for RL. New work from UC Berkeley @berkeley_ai @BerkeleyRDI @BerkeleySky Project Page: benchevolver.github.io Paper: arxiv.org/abs/2606.01286

Agents are finding more vulnerabilities than ever. But it turns out there are gaps in existing vulnerability discovery. Over the past 90 days vs. a year ago, web vulnerabilities (XSS/SQLi/CSRF) are down 66% and memory safety exploitability is down 3.5x. We built the Agentic Vulnerability Coverage Map to track it all, updated daily. Introducing the Berkeley Vulnerability Initiative: vuln.cs.berkeley.edu. ⤵️

We release Recon — a new approach to reasoning synthesis for user modeling. The key insight: post-hoc rationalization ≠ reasoning. We propose using action reconstruction as a scoring criterion for synthesized reasoning traces, yielding more causally faithful reasoning and improved downstream action prediction across user modeling tasks. Paper and project page in 🧵

Excited to share that MAP has been selected for ✨ICML Oral✨ We look forward to sharing the insights in the paper with the community And much much appreciations to everyone who participated in our study ❤️ MAP won’t be possible without your contribution to open science

Open-ended coding training data may no longer be the bottleneck: AI can scale open-ended tasks—and even outperform human-expert curation. FrontierCS team is releasing FrontierSmith: a system for synthesizing open-ended coding problems at scale. Starting from closed-ended coding tasks, FrontierSmith mutates, filters, and builds runnable optimization environments for long-horizon coding agents. In our experiments, FrontierSmith data trains stronger models than human-curated open-ended data on FrontierCS and ALE-bench. Blog: frontier-cs.org/blog/frontie… Paper: arxiv.org/abs/2605.14445 Code: github.com/FrontierCS/Fronti… Model: huggingface.co/runyuanhe/qwe…

🚀 Excited to release mKernel: a set of fast multi-node, multi-GPU fused kernels. 💻 Code: github.com/uccl-project/mKer… 📝 Blog: uccl-project.github.io/posts… mKernel fuses compute communication into one persistent GPU kernel, covering both intra/inter-node with GPU-initiated communication. Amazing team: @yangzhouy, Chon Lam Lao, Costin Raiciu, Scott Shenker, @istoica05

Learning from rich textual feedback (errors, traces, partial reasoning) beats scalar reward alone for LLM optimization. GEPA demonstrated this for context-space optimization (prompts and agent harnesses), delivering frontier results at a fraction of the cost of RL. But context-only optimization is bounded by the base model's capability ceiling; weight updates can reach further. Very excited about this new line of work on Fast-Slow Training (FST), which interleaves context and model weight optimization! The idea is a clean division of labor between two interleaved loops: 🔹 Fast loop (context): GEPA reads rich rollout feedback updating the context layer. The context becomes a fast-updating scratchpad of what the model needs to know about this task, right now. 🔹 Slow loop (model parameters): RL updates the model's parameters conditioned on the evolving context. Because the prompt already carries task-specific nuances, the model parameters are freed from absorbing them and focus on what actually generalizes across tasks and pushes the frontier. ⦁ 3× more sample-efficient than RL on math, code, and physics reasoning ⦁ ~70% lower KL divergence from base at matched accuracy ⦁ Plasticity preserved: FST checkpoints respond better to additional RL on new tasks than RL-only ones ⦁ Continual learning across changing tasks (HoVer → CodeIO → Physics) where RL stalls the moment the task switches FST is a direction towards: ⦁ Addressing RL's pain points: entropy collapse, sparse rewards, long-horizon exploration ⦁ Providing a clean channel for rich feedback into weight updates ⦁ Demonstrating model-harness co-evolution ⦁ Discovery: Using fast context updates for broad exploration, while leveraging a continually improving model. Check out the full thread below:

1/ Thrilled to introduce T³: a corpus for RAG over reasoning tasks, built from thinking traces. We show that surprisingly RAG can improve reasoning— with the right corpus. Rag with Transformed Thinking Traces T³ gain by up to 43.9% on AIME 2025-2026. 🔗 arxiv.org/abs/2605.03344 🧵

Today, we’re releasing Continual Learning Bench 1.0: the first, realistic benchmark for measuring how AI systems can improve in online settings. Benchmarks today assume models are stateless. Each example is independent, and once a system finishes a task, it moves on as if nothing happened. But deployed AI systems should learn from experience. We tested 10 frontier systems against novel, expert-validated tasks and find there’s still plenty of headroom for learning. (1/n)

Agent harness is as important as the model for cybersecurity. $300 in compute, 9 OSS-Fuzz projects, 14 security issues and 5 CVEs. The key lesson: you don’t need a secret model to find real security issues. You need an effective, affordable, reliable harness. 5 takeaways 🧵

Excited to announce that FrontierCS has been accepted to ICML 2026! 🚀 We are scaling our open-ended task set to 250 tasks (100 new tasks in 2026 Q1🔥), featuring long-horizon agent settings in Harbor and integration into real-world human contests. More exciting updates to come! Huge thanks to all our collaborators. #ICML2026 #AI #MachineLearning

Excited to share: MAP has been accepted as 🌟 ICML Spotlight 🌟 We hope MAP can provide data-driven insights that help the communities to work on various under-explored research directions around agent systems! Huge thanks & congrats to my amazing co-authors. See you all at Seoul! 🫡

What if one person could run a unicorn company? Today we're open-sourcing OMAR — a TUI that lets a single engineer orchestrate hundreds of AI coding agents in deep, recursive hierarchies. Built at Berkeley. Powered by tmux. github.com/lsk567/omar 🧵

Would you trust an AI agent to negotiate on your country's behalf at the G20? Real coordination is long-horizon, asymmetric, and non-binding; current multi-agent evaluations miss this. We build Cooperate to Compete (C2C): a testbed for LM agents coordinating with rivals. 🤝🔪🎭

Congratulations to Matei Zaharia on being awarded the ACM Prize in Computing! His development of open-source systems helped enable large-scale machine learning, analytics and AI at a global scale. @matei_zaharia @UCBerkeley 🔗 Read more: bit.ly/4vbNujK

🎯 One Year of AI-Driven Research at Berkeley [ADRS Blog #20] For the past year at Berkeley, we have been working on automating discovery with AI. In our blog post this week, we provide an overview of these efforts: the key problems we’re tackling, the frameworks and solutions we’ve built so far, and how these efforts fit into a broader vision for AI-driven scientific discovery. ✍️ Read the blog: ucbskyadrs.github.io/blog/be… 📖 ADRS Blog Series: ucbskyadrs.github.io/

Introducing M²RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling We bring back non-linear recurrence to language modeling and show it's been held back by small state sizes, not by non-linearity itself. 📄 Paper: arxiv.org/abs/2603.14360 💻 Code: github.com/open-lm-engine/lm… 🤗 Models: huggingface.co/collections/o…

Researchers spend hours and hours hand-crafting the strategies behind LLM-driven optimization systems like AlphaEvolve: deciding which ideas to reuse, when to explore vs exploit, and what mutations to try. 🤖But what if AI could evolve its own evolution process? We introduce EvoX, a meta-evolution pipeline that lets AI evolve the strategy guiding the optimization. It achieves high-quality solutions for <$5, while existing open systems and even Claude Code often cost 3-5× more on some tasks. Across ~200 optimization problems, EvoX delivers the strongest overall results: often outperforming AlphaEvolve, OpenEvolve, GEPA, and ShinkaEvolve on math and systems tasks, exceeding human SOTA, and improving median performance by up to 61% on 172 competitive programming problems. 👇

Very nice results and great project! Sharing some of our experience with similar agentic frameworks at UC Berkeley: ADRS blog series: ucbskyadrs.github.io/blog/ GEPA: github.com/gepa-ai/gepa KISS: github.com/ksenxx/kiss_ai