Better agents may begin with better teachers. A capable PhD student can lose days to scattered experiment logs and broken workflows. A good advisor identifies these bottlenecks and builds the support that lets the student focus on scientific judgment. Our new work brings this idea to AI4AI: a Builder learns from a Target’s execution feedback to design better harnesses. We call these reusable lessons meta-skills. Both models’ weights stay frozen; what improves is how one AI supports another. Check out our newest paper: Learning Meta-Skills for Agent Harness Design in Test-Time AI4AI 📄 arxiv.org/abs/2609.38143 💻 github.com/qiancheng-apodex/…

🚀 CFP: 3rd Edition of the Lifelong Agent Workshop (LLA)! After ICLR 2026 and COLM 2026, we’re excited to bring LLA to AIAS+ 2026, back in San Francisco on Nov 7! We welcome work on all aspects of lifelong agents, including lifelong learning and adaptation, alignment and safety, agent evolution and self-improvement, evaluation and benchmarking, multi-agent systems, AI for Science, and applications across diverse domains. 📅 Submission deadline: Oct 10 (AoE) We’d love to see your latest ideas, insights, and ongoing work. Submit your work and join us in San Francisco! Website: lifelongagent.github.io/aias… OpenReview: openreview.net/group?id=aias… Registration: aiasplus.org/event-registrat… Hope to see you at LLA @ AIAS+ 2026! #LLMAgents #AIAgents #LifelongLearning #AIAS2026

Over the past year, we have worked on three projects around User-Centric Agents, and this line of work has finally come together at EMNLP this year. Behind all three projects, we have really been asking the same question: If the ultimate purpose of an Agent is to serve people, should we move beyond modeling tasks -- and start modeling users? Many Agent systems implicitly assume that users provide goals that are clear, complete, and stable. Real human interaction is almost the opposite: our needs may be underspecified, our preferences often emerge gradually through conversation, and our behavior is shaped by latent factors such as our beliefs, intentions, and perspectives. A truly user-centric Agent therefore needs to solve three increasingly deeper problems: 1. Can it understand and satisfy user needs through complex interaction? 2. Can it learn better interaction strategies by continuously engaging with users? 3. And ultimately, can it understand the unobservable mental states behind user behavior? 🍃🍃🍃 🧭 UserBench: From Completing Tasks to Understanding Users We first put the user back into the Agent's environment. In UserBench, user requests can be underspecified, incremental, and indirect. The Agent must actively communicate with the user, uncover preferences, and then use tools to accomplish the task. What we found is striking: even models that are already very strong at tool use often manage to "complete the task" without actually satisfying the user. This reveals an important distinction: --> Task success is not the same as user success. 🏋️ UserRL: From Evaluation to Learning The next natural question was: can these capabilities be learned systematically? UserRL builds 8 user-centric gyms, where Agents continuously interact with simulated users through multi-turn rollouts and learn via reinforcement learning to improve capabilities such as clarification, adaptation, persuasion, and intent understanding. Here, the user is no longer merely the provider of a prompt: --> The user becomes a dynamic, interactive training environment. 🧠 UserHarness: From Behavior to Mind But users are more than collections of preferences. They also have their own beliefs, intentions, and perspectives. UserHarness therefore takes one step further and reframes Theory of Mind as user-mind reconstruction: explicitly tracking what a user has observed, what they believe, what they intend to do, and how these internal states give rise to their behavior. --> The goal is no longer merely to predict what the user will do, but to understand why the user behaves that way. 🍃🍃🍃 Whether future Agents evolve toward AI4AI, RSI, or increasingly autonomous intelligence, one question will remain unavoidable: "Who is the Agent ultimately acting for?" Today's AI can already automate increasingly complex tasks. But the more autonomous these systems become, the more important it is that they genuinely understand human needs, preferences, intentions, and boundaries. The capabilities of future Agents may continue to expand, but their objectives, behavioral boundaries, and ultimate locus of control should remain firmly anchored to the user. Finally, hope to see many of you in Budapest this year! 📄 UserBench: arxiv.org/pdf/2507.22034 📄 UserRL: arxiv.org/pdf/2509.19736 📄 UserHarness: arxiv.org/pdf/2605.27721

✨ Creativity is not just recognizing what an object is — it is imagining what it could become. 🔧 A key edge can cut tape. 🛡️ A rubber pad can protect a wall. 🪮 A comb guard can clear a sink slot. But can multimodal AI agents discover these hidden physical affordances from images? 🚀We introduce MM-CreativityBench, a benchmark designed to test whether LMMs can creatively repurpose everyday objects by interactively inspecting scenes, entities, and object parts. 🔍 Our findings show that today’s LMMs often identify the right object, but fail to ground their reasoning in the right part. They hallucinate properties, overlook physical constraints, or propose solutions that are not mechanically valid. 🧠 To move beyond plausible guesses, we propose affordance-grounded alignment: training models to explore visual evidence, reject hallucinated affordances, and reason from geometry, material, and mechanics. 📄 Paper: arxiv.org/pdf/2605.26396 🌐 Project: creativitybench.github.io/mm… 💻 Code: github.com/CreativityBench/M… 🤗 Hugging Face: huggingface.co/papers/2605.2…