Your coding agent’s code can pass every test and still be exploitable, but users rarely know👾 🤔SFT, RL, or OPD: which teaches security best? 🔥SecureVibe helps vibe coding plan, code, and test for security, +6.9 pts BaxBench, +11.5 unseen CWEs SusVibes, +4.1 SWE-bench Verified

🤩 Introducing Self-HPO: let the coding agent improve its own harness, then train a stronger policy model with the optimized harness, and repeat the process for recursive improvement! 💪 Self-evolving: no external model required 🎯 Harness search targets the policy's evolving failure modes 📈 Avg over SWE-Bench Verified/Multilingual/Pro: 57.8% → 64.8% 🧵[1/n]

Recursive self-improvement needs agents that can build their own training environments. But researchers still part of the loop, making it hard to scale. We introduce AutoEnvScaling, which turns Data Flywheel into a terminal task: a proposer agent builds complete Harbor tasks and sandbox. It builds just like human researchers. This enables self-play RL for terminal agents: one model improves as both builder and solver, without human-in-the-loop.

Excited to introduce OpenWebRL 🚀 A fully open framework for training visual web agents with online multi-turn RL on live websites. Today’s strongest web agents often rely on proprietary infrastructure, large-scale closed data, and costly API-based evaluation. OpenWebRL aims to make this training stack open, reproducible, and cost-friendly. With only 0.4K SFT trajectories and 2.2K online RL tasks, OpenWebRL-4B sets a new open-source SOTA on live-web benchmarks including Online-Mind2Web, DeepShop, and WebVoyager, outperforming much larger open-source agents and even matching proprietary systems. We release the full stack: data, models, training framework, and judge models. Paper: arxiv.org/abs/2606.02031 Project: openwebrl.github.io/ Code: github.com/OpenWebRL/OpenWeb… Models & Data: huggingface.co/OpenWebRL

Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure. msft.it/6019a8fqP

🌳 Introducing Orchard — an open-source agentic modeling framework! 🎉 One thin & cheap sandbox infra powers training recipes across SWE / GUI / personal-assistant agents: ⚙️ Orchard Env: 0.28s exec latency; 100% success @ 1,000 parallel sandboxes 💪 🛠️ Orchard-SWE: 67.5% on SWE-bench Verified (30B-A3B, ~3B active) 🖥️ Orchard-GUI: 68.4% avg on WebVoyager / Online-Mind2Web / DeepShop (4B!) 📬 Orchard-Claw: 73.9% pass@3 on Claw-Eval 🔗 arxiv.org/abs/2605.15040 📦 Code and data are coming soon! Let's accelerate open agentic AI! 🚀

Introducing WebHarbor ⚓ — an open community effort to dock real websites into local, deterministic, and evolving environments for web agent research. 🌐 Come help us build it. 🤝 Contribute new web environments or fix existing ones — will be included in the author list! ✍️ 🎉 First release: 15 multimodal, high-fidelity environments covering all 643 WebVoyager tasks — full frontend, backend, database, and auth, all in one lightweight Docker image. Why? Web agent eval today is broken😦: reCAPTCHA, geo-blocks, content drift, flaky networks, and login-gated deep features (e.g., account and checkout) that benchmarks can't touch. Live sites can't be reset either — making online agent RL impractical. Again, the bottleneck isn't the agent. It's the environment. WebHarbor: dock real websites into stable, reproducible local mirrors with sub-second reset. But here's the key 🌱 — you can't clone the entire web upfront, and you don't need to. WebHarbor evolves with the agent: as harder tasks arrive, environments grow to support them. Coding agents (e.g., Claude Code/CodeX) build mirrors fast; human reviewers catch what coding agent hacks (shortcuts, leaks, fake completions). We need you. 🙌 Help us scale to 100+ and beyond: 🔨 Contribute a new web environment 🐛 Fix or improve existing mirrors 🔍 Audit task fidelity & interaction realism See more details and join the effort: - 🏠 Project Page: aiming-lab.github.io/webharb… - 💻 GitHub Repo: github.com/aiming-lab/WebHar… - 📝 Contribution Form: forms.gle/ngcD1rzAfUEphNmRA Let's build the open-source environment infrastructure for GUI web agents! ⚓ Initiating institutions: UNC-Chapel Hill ✖️Microsoft #AIAgents #WebAgents #LLM #OpenSource #AgenticAI

Cool new paper from Microsoft on Skill learning for autonomous web agents.

Collecting high-quality GUI trajectories for agent training is expensive. But are we fully leveraging the open-source data we already have? 🤔 ✨Introducing GUI-Libra (gui-libra.github.io): 81K high-quality, action-aligned reasoning dataset curated from open-source corpora, plus a tailored training recipe that combines action-aware SFT with step-wise RLVR-style training (⚠️partially verifiable rather than fully verifiable!). Result: stronger native GUI agents on both offline step-wise evaluation and online environments across mobile and web domains. Take away: With careful data curation + tailored post-training recipe, a small subset of open-source trajectories can still go a long way for training native GUI agents. Check out our paper (arxiv.org/abs/2602.22190) and code/dataset/model (github.com/GUI-Libra/GUI-Lib…) for more details. #GUI #agent #VLM

We’re thrilled to share that our team’s work LightMem has been accepted to ICLR 2026 🎉 Paper: arxiv.org/abs/2510.18866 Code: github.com/zjunlp/LightMem LightMem is a lightweight, modular memory system for LLM agents that enables scalable long-context reasoning and structured memory management across tasks and environments. Recent updates: 1️⃣ Introduced a comprehensive baseline evaluation framework for benchmarking memory layers (Mem0, A-MEM, LangMem) across datasets like LoCoMo and LongMemEval 2️⃣ Released a demo video showcasing long-context handling, along with tutorial notebooks covering multiple usage scenarios 3️⃣ Enabled multi-tool invocation via MCP Server integration 4️⃣ Added full LoCoMo dataset support and integrated GLM-4.6, achieving strong performance and efficiency with reproducible scripts 5️⃣ Supported local deployment through Ollama, vLLM, and Transformers with automatic model loading #ICLR2026 #LLM #Agents #MemorySystems #LightMem

🚀 New work: SynthAgent – a fully synthetic supervision pipeline for web agents 🤖 We generate high quality and environment-specific tasks + trajectories to adapt agents to new websites without human efforts 🧠🧼 arxiv：arxiv.org/pdf/2511.06101 code：github.com/aiming-lab/SynthA…