📍 Can LLMs discover, abstract, and reuse higher-level tool skills across tasks? Existing tool-use benchmarks test solving tasks with fixed tools. But real workflows contain recurring structures where efficiency comes from reusable tool compositions, not isolated calls. We introduce SkillCraft: 126 tasks across 6 domains designed to test whether LLM agents can acquire compositional skills, not just call atomic tools. We also propose Skill Mode, a lightweight protocol with four MCP primitives that let agents compose, verify, cache, and reuse tool chains at test time. Our Key findings across evaluating 8 SOTA models: ⚡Skill Mode enables agents to self-discover and reuse skills, leading to higher success and efficiency than agents without it. The gains are larger for stronger models. 🧠 Stronger models (e.g., Claude) discover more generalizable skills, which transfer across tasks and even across models. 🔍 Deeper composition ≠ better — shallow, well-tested skills generalize best. 🔗 Paper: arxiv.org/abs/2603.00718 💻 Code: github.com/shiqichen17/Skill… 🏠 Page: skillcraft-website.github.io… (1/7)

In Agent RL, models suffer from Template Collapse. They generate vast, diverse outputs (High Entropy) that lose all meaningful connection to the input prompt (Low Mutual Information). In other words, agent learn different ways to say nothing. 🚀 Introducing RAGEN-v2 -- Here's how we define and fix such silent failure modes in Agent RL. 🧵

VAGEN poster 𝐭𝐨𝐦𝐨𝐫𝐫𝐨𝐰 at #NeurIPS! 🎮🧠 - 🕚 11am–2pm Wed - 📍 Exhibit Hall C,D,E #5502 We had much fun exploring: • How 𝐰𝐨𝐫𝐥𝐝 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 helps VLM RL agents learn better policies • 𝐌𝐮𝐥𝐭𝐢-𝐭𝐮𝐫𝐧 𝐏𝐏𝐎 credit assignment via 𝐭𝐰𝐨-𝐥𝐞𝐯𝐞𝐥 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐨𝐫 (Bi-Level GAE) for turn-level and token-level critic learning Come chat about agents, RL, and world models 👀

We are looking for PhDs and Postdocs! So proud of my students on achieving so many amazing things during their "very first year". I have been asked many times how I like being faculty, especially with funding cuts. My answer is always "it is the prefect job for me"! Still deep in the honeymoon phase. The only reason is the students are so amazing, making my transition so much easier. One year in, they already collected paper awards, orals, spotlights, etc What makes me proudest is they are vividly alive: curious, playful, confident in their own weird way, light up when talking about ideas, and never afraid to explore "the thing might fail". Everyone is just… themselves. And somehow, that version of themselves keeps shipping amazing work. In today's anxious academic world, this kind of aliveness is what I will try best to protect. Maybe the best part of being an advisor is that every student is so different and unique lol Interestingly, coming to second year, they've got their own passions, I can't just plug my ideas into their heads. So when I get excited about sth new, my first thought is: "Okay, time to find some fresh first-years who will be thrilled about this!" MLL lab is 1 year old, we started right in Oct 2024. We are growing and looking for more phds to join us! 1. Why our lab? (1/2) 2. Why @northwesterncs? (2/2) In 2025 alone: NU has 7 faculty as Sloan Fellows, plus a Nobel winner! Check more below

Can VLMs build Spatial Mental Models like humans? Reasoning from limited views? Reasoning from partial observations? Reasoning about unseen objects behind furniture / beyond current view? Check out MindCube! 🌐mll-lab-nu.github.io/mind-cu… 📰arxiv.org/pdf/2506.21458 🤗huggingface.co/datasets/MLL-… 👩‍💻github.com/mll-lab-nu/MindCu…