Qianhui Wu

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-…) 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/Syn…

Big thanks to Qianhui Wu @5000hui and the team behind “Act Where You See” for sharing their amazing work this week at @SimularAI Seminar! 🧠⚡️ Coordinate-free visual grounding for GUI agents is a huge leap toward human-like interaction. 📎 aka.ms/GUI-Actor #AI #SimularSeminar #GUIAgents #SimularToHuman

In this issue of Research Focus, we examine a new conversation segmentation method that delivers more coherent and personalized agent conversation, and we review efforts to improve MLLMs’ understanding of geologic maps. Check out the latest research: msft.it/6019q9k33

Thanks for featuring our work! @arankomatsuzaki. 🔥Today we are thrilled to announce our MSR flagship project Magma! This is a fully open-sourced project. We will roll out all the stuff: code, model and training data through the following days. Check out our full work here: microsoft.github.io/Magma ! To the best of our knowledge, Magma is the first-ever foundation model for multimodal AI agents designed to handle complex interactions for agentic tasks. With a single suite of parameters, Magma achieves state-of-the-art UI navigation and robotics manipulation across both digital and physical environments, as well as excelling on generic image and video understandings!

🕸️ KV cache has its own lifecycle, but previous benchs focus solely on single-req, ignore full lifecycle. 🪻SCBench fills the gap with a KV-cache-centric angle, covering 4 kv cache stage across 12 tasks, 4 capability, and 2 shared-context mode aka.ms/SCBench