Sometimes the biggest gains in AI come from old ideas, seen fresh. Dillon Chen returned as a Vector Research Intern for Winter 2025 and Winter 2026, working with Vector Faculty Member and Canada CIFAR AI Chair Sheila McIlraith on long-horizon planning and sequential decision making – key building blocks for autonomous systems that need to be efficient, safe, and reliable. A standout insight from his work: revisiting foundational, decades-old concepts can outperform newer, heavily engineered approaches, and unlock major speed-ups in compute. But it was the day-to-day experience, with strong access to research resources and compute, and the chance to learn directly from bright, motivating researchers and colleagues, that helped him move faster, learn more, and stay ambitious about what was possible. Apply for Winter 2027 Research Internships by June 22: na3.hubs.ly/y0vH7g0

AI already supports 150,000 jobs in Canada, and more than 800,000 people work across ourbroader digital economy. Our new strategy, AI for All, invests $350 million in Canadian AI institutions like @VectorInst, so that AI is developed here — safely, responsibly, and to benefit of all Canadians.

How do we improve VLM post-training? Don't just force longer-horizon multimodal reasoning. Reshape the curriculum to match how VLMs learn. 👀➡️🧠 Excited to share our new #ICML2026 paper in collaboration with @ucsc @amazon @UWaterloo @VectorInst: "From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models" 🌐 Project: ucsc-vlaa.github.io/VLM-CapC… 📄 Paper: arxiv.org/abs/2605.20177 💻 Code: github.com/UCSC-VLAA/VLM-Cap… 🤗 HF Collections: UCSC-VLAA/VLM-CapCurriculum Recent VLM post-training has largely focused on scaling up reasoning through RL and long chain-of-thought traces. But after auditing model failures across visual math, geometry, and diagram reasoning tasks, we found a different bottleneck: 👉 86.9% of Qwen3-VL-8B's errors originate from perception, not reasoning. Once a model misreads the image, additional reasoning often just reinforces the wrong interpretation. Our key insight is simple: before a model can reason better, it must first see better. To achieve this, we introduce a new simple capability curriculum for VLM post-training: 🟦 Visual Perception → 🟩 Textual Reasoning → 🟨 Visual Reasoning Instead of mixing all capabilities into a single RLVR stage, we train them sequentially, allowing perception to develop as a dedicated foundational capability. The results are remarkably consistent: ✅ Staged post-training outperforms standard merged training across 4 VLM backbones (#Qwen2.5-VL, #Qwen3-VL, #InternVL3, #InternVL3.5) ✅ Better perception literally lets the model think less！The staged post-training on #Qwen3-VL-8B uses only 79% of the reasoning tokens of the merged model (20.8% shorter traces) achieves 1.46% average accuracy. ✅We also show that capability curriculum and traditional difficulty curriculum are complementary. On #Qwen3-VL-8B, combining both boosts average performance from 58.6 → 63.0, outperforming either curriculum alone. The broader message is that curriculum learning for VLMs should not only consider how difficult an example is, but also which capability it develops. 📦 We are releasing everything: 1️⃣ A new capability curriculum paradigm, orthogonal to difficulty-based curricula 2️⃣ High-quality perception training data built with a scalable DOCCI-based synthesis and filtering pipeline 3️⃣ 4 staged-trained VLMs: #Qwen2.5-VL-7B, #Qwen3-VL-8B, #InternVL3-8B, and #InternVL3.5-8B 4️⃣ Full training, evaluation, and perception-error analysis code Led by my PhD student @JJwu41867797, as well as an outstanding team of collaborators @HardyChen266091, @HaoqinT, Xianfeng Tang, @fredahshi, Hui Liu, Hanqing Lu, @cihangxie

“Safety of AI is a necessity for our country because of the data that we have. Through this, we can unlock all kinds of work and research that we can export globally.” — Glenda Crisp, CEO @VectorInst at the AI Task Force event today @Empire_Club

Great discussions today at the launch of our AI Task Force Report at the @Empire_Club focusing on what real change in the age of #AI looks like. ontariosuniversities.ca/repo…

From founding sponsor to five more years: @BMO is renewing their partnership with Vector through 2031, deepening nearly a decade of work in responsible AI. 🎉 Read the full announcement: newswire.ca/news-releases/bm… #ResponsibleAI #AIinCanada

Applied Machine Learning Intern Ahmed Radwan started with one goal: build, not just study. And his Vector internship delivered. During his Fall 2025 term with Vector's AI Engineering team on AIXpert, Ahmed developed: → SONIC-O1: a benchmark to identify and reduce bias in AI used in job interviews and healthcare settings → UnBias-Plus: a tool that helps journalists detect and rewrite biased language in under a minute Here he learned to think beyond implementation – to ask not just how to build a solution, but why it was strategically necessary. What will you learn as a Vector intern? Applications for Fall 2026 Applied Internships are now open with three paid roles across ML, AI program delivery, and public sector AI adoption. Applications close May 28. Apply here 👇 na3.hubs.ly/y0tL8S0

Meet Sunehra Sarwar. As a Vector intern, Sunehra led an AI partnership between Vector and a crisis helpline, working to improve service delivery through AI. She was there for every stage of the project lifecycle: use case exploration, planning, development and testing. The work required collaboration across AI engineering, industry innovation, and data operations. She came away with a real, end-to-end view of what it takes to execute an AI implementation from the ground up. The IMPACT Mentorship Program gave her direct access to professionals navigating the careers she was working toward – with candid conversations that helped her identify which skills to build and why. She's now an Associate Project Manager on Vector's Health AI Implementation Team. Where will Vector’s Applied AI Internships take you? Apply before May 28 at 1:00 PM ET to find out 👇 na3.hubs.ly/y0tkq40

The age of #AI is here – and Ontario’s universities are ready to lead. Join us on May 29 @Empire_Club for the launch of our AI Task Force Report, featuring a panel of key voices shaping Ontario's role in the global AI economy & remarks from the Hon. @nolanmquinn & @kbardeesy: empireclubofcanada.com/event…… #ONpse #ONpoli

⏰ Final days to apply for Vector's Fall 2026 Research Internships! Applications close Tuesday, May 19 at 1:00 p.m. ET Work on research that matters: 🔬 Collaborate with Vector Faculty on cutting-edge projects 🌟 Opportunity to participate in IMPACT Mentorship Program 🤝 Build your professional network 📚 Access career development opportunities Don't wait – complete your application this weekend: na3.hubs.ly/y0sZNG0 Winter 2027 applications remain open until June 22

🚀 Can #AI agents actually do science? Current agents can optimize for predictive fit — but fail at recovering the underlying physical laws. We introduce Stargazer🪐: a scalable benchmark environment for astronomical discovery🔭 Agents must: • propose hypotheses • design/update models • fit noisy astronomical observations • iteratively refine theories • recover the true latent physical mechanisms 🪐 Main result: Good curve fits ≠ good science Even strong agents achieve: • >70% statistical fit pass rate (Hard split) • <6% recovery of the correct physical law • On real observational data: 0/8 models succeed The benchmark exposes a key gap between: 📈 predictive performance vs. 🧠 mechanistic scientific understanding 📄 Paper: arxiv.org/abs/2604.15664 💻 Demo: aips-uoft.github.io/Stargaze… Authors: @XingeLiu1213 @TerryJCZhang @bschoelkopf @ZhijingJin @KristenMenou Supported by: @UofT @VectorInst @UofTCompSci @JinesisLab @MPI_IS @ELLISInst_Tue @EuroSafeAI