NEXT: Reasoning-Driven Video Recommendation via a Vision-Language Model

• URL: http://arxiv.org/abs/2607.24789v1
• Date: Sat, 27 Jun 2026 06:51:58 GMT
• Title: NEXT: Reasoning-Driven Video Recommendation via a Vision-Language Model
• Abstract summary: Next-interest EXploration Transformer is a reasoning-driven video recommendation framework.<n>Next-8B is a purpose-trained 8B vision-language model with a three-stage recipe.<n>Next shows statistically significant production gains, including +0.53% watch time and +0.51% distinct video exposure.
• Score: 8.612187834047418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present NEXT (Next-interest EXploration Transformer), a reasoning-driven video recommendation framework that reasons over the video a user has just watched, infers the viewer's next intent, and retrieves concrete follow-up videos. Explicit continuations such as episodes are linked directly; implicit cases are handled by generating intent queries and searching for matching candidates. This Item-to-Intent-to-Item formulation produces directed recommendations beyond co-engagement correlation or semantic similarity. To make this framework reliable at scale, we train NEXT-8B, a purpose-trained 8B vision-language model with a three-stage recipe: Perception-Enhanced Reinforcement Learning for query-agnostic evidence extraction, Distribution-Aligned Supervised Fine-Tuning over real and synthetic visual QA mixtures, and Group Relative Policy Optimization for last-mile alignment. NEXT-8B achieves the best single-model DocVQA performance, ranking second overall only behind a multi-agent system while surpassing a substantially larger 200B+ scale model, and improves next-intent logic-wise quality by 3.3% over the base model in a task-specific LLM-as-a-judge evaluation. We deploy NEXT as an additional retrieval path in a large-scale social media recommendation system and observe statistically significant production gains, including +0.53% watch time and +0.51% distinct video exposure. Overall, NEXT shows that a carefully trained compact vision-language model can serve as a practical reasoning engine for next-interest exploration at production scale.

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