Vector researchers advance AI frontiers with 80 papers at NeurIPS 2025
Researchers from Vector’s vibrant community are presenting groundbreaking work across the full spectrum of artificial intelligence at this year’s Conference on Neural Information Processing Systems (NeurIPS), taking place December 2-7 […] The post Vector researchers advance AI frontiers with 80 papers at NeurIPS 2025 appeared first on Vector Institute for Artificial Intelligence .
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State of Evaluation Study: Vector Institute Unlocks New Transparency in Benchmarking Global AI Models
Five takeaways for AI model developers, researchers and users Vector Institute’s first State of Evaluation study, developed by Vector’s AI Engineering team, shines new light on the evaluation and benchmarking [ ] The post State of Evaluation Study: Vector Institute Unlocks New Transparency in Benchmarking Global AI Models appeared first on Vector Institute for Artificial Intelligence .
Transforming Youth Mental Health Support: FAIIR s AI-Powered Crisis Response Model
Vector Institute and Kids Help Phone (KHP) researchers have co-created the Frontline Assistant: Issue Identification and Recommendation (FAIIR) model. This model automatically identifies and categorizes key issues discussed during crisis [ ] The post Transforming Youth Mental Health Support: FAIIR s AI-Powered Crisis Response Model appeared first on Vector Institute for Artificial Intelligence .
Ontario’s AI ecosystem: fueling real economic growth with record number of jobs and private investments
The Vector Institute released its fourth annual Ontario AI Snapshot, produced in partnership with Deloitte Canada. The research indicates that the province’s AI sector has grown significantly, cementing Ontario’s position [ ] The post Ontario’s AI ecosystem: fueling real economic growth with record number of jobs and private investments appeared first on Vector Institute for Artificial Intelligence .
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Vector researchers tackle real-world AI challenges at ICML 2025
Leading researchers from Vector are presenting cutting-edge work at this year s International Conference on Machine Learning (ICML), taking place July 13-19, 2025 in Vancouver, Canada and through virtual platforms. With [ ] The post Vector researchers tackle real-world AI challenges at ICML 2025 appeared first on Vector Institute for Artificial Intelligence .
Beyond Metadata: Multimodal, Policy-Aware Detection of YouTube Scam Videos
arXiv:2509.23418v2 Announce Type: replace Abstract: YouTube is a major platform for information and entertainment, but its wide accessibility also makes it attractive for scammers to upload deceptive or malicious content. Prior detection approaches rely largely on textual or statistical metadata, such as titles, descriptions, view counts, or likes, which are effective in many cases but can be evaded through benign-looking text, manipulated statistics, or other obfuscation strategies (e.g., 'Leetspeak'), while ignoring visual cues. In this study, we systematically investigate multimodal approaches for detecting YouTube scams. Our dataset consolidates established scam categories and augments them with full-length videos and policy-grounded reasoning annotations. Experiments show that a text-
Online Flow Time Minimization: Tight Bounds for Non-Preemptive Algorithms
arXiv:2511.03485v3 Announce Type: replace Abstract: This paper studies the online scheduling problem of minimizing total flow time for $n$ jobs on $m$ identical machines. A classical $\Omega(n)$ lower bound shows that no deterministic single-machine algorithm can beat the trivial greedy, even when $n$ is known in advance. However, this barrier is specific to deterministic algorithms on a single machine, leaving open what randomization, multiple machines, or the kill-and-restart capability can achieve. We give a nearly complete answer. For randomized non-preemptive algorithms, we establish a tight $\Theta(\sqrt{n/m})$ competitive ratio, which also improves the best offline approximation to $O(\sqrt{n/m})$. For deterministic non-preemptive algorithms on multiple machines, we prove an $O(n/m^
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