July 29, 2026

AI VK Research presented a new approach that lets classical, non-neural recommendation algorithms factor in how a current recommendation will influence a user's future preferences, not just present interests. The method was accepted at the Customer Journey workshop of KDD 2026 and showed improved accuracy on open datasets including VK-LSVD.

Microsoft researchers introduced Mage-VL, a 4B-parameter vision-language model with a visual encoder (Mage-ViT) trained from scratch on 560M images and 100M video frames. It uses codec-guided sparse token selection, borrowing I-frame/P-frame structure from video codecs, to cut visual tokens by roughly 75% versus dense frame sampling, giving up to 3.5x wall-clock inference speedup. A dual-system design pairs a lightweight streaming gate with a full reasoning decoder; it matches Qwen3-VL-4B on static VQA while beating it on video understanding and spatial reasoning.

The Qwen team at Alibaba proposed Skill-SP, a reinforcement-learning framework with three co-evolving components: a proposer that generates tasks conditioned on dynamically sampled skills, a solver that explores solutions, and a skill controller that updates a skill library from execution feedback. It aims to bridge reliable-but-narrow environment-bound training with broad-but-unreliable open-ended task generation, showing gains on tool-use and reasoning benchmarks, including large improvements for initially weak models.