ChainRec: An Agentic Recommender Learning to Route Tool Chains for Diverse and Evolving Interests

Large language models (LLMs) are increasingly integrated into recommender systems, motivating recent interest in agentic and reasoning-based recommendation. However, most existing approaches still rely on fixed workflows, applying the same reasoning procedure across diverse recommendation scenarios. In practice, user contexts vary substantially-for example, in cold-start settings or during interest shifts, so an agent should adaptively decide what evidence to gather next rather than following a scripted process. To address this, we propose ChainRec, an agentic recommender that uses a planner to dynamically select reasoning tools. ChainRec builds a standardized Tool Agent Library from expert trajectories. It then trains a planner using supervised fine-tuning and preference optimization to dynamically select tools, decide their order, and determine when to stop. Experiments on AgentRecBench across Amazon, Yelp, and Goodreads show that ChainRec consistently improves Avg HR@{1,3,5} over strong baselines, with especially notable gains in cold-start and evolving-interest scenarios. Ablation studies further validate the importance of tool standardization and preference-optimized planning.

Spatiotemporal Predictive Pre-training for Robotic Motor Control

Robotic motor control necessitates the ability to predict the dynamics of environments and interaction objects. However, advanced self-supervised pre-trained visual representations (PVRs) in robotic motor control, leveraging large-scale egocentric videos, often focus solely on learning the static content features of sampled image frames. This neglects the crucial temporal motion clues in human video data, which implicitly contain key knowledge about sequential interacting and manipulating with the environments and objects. In this paper, we present a simple yet effective robotic motor control visual pre-training framework that jointly performs spatiotemporal predictive learning utilizing large-scale video data, termed as STP. Our STP samples paired frames from video clips. It adheres to two key designs in a multi-task learning manner. First, we perform spatial prediction on the masked current frame for learning content features. Second, we utilize the future frame with an extremely high masking ratio as a condition, based on the masked current frame, to conduct temporal prediction of future frame for capturing motion features. These efficient designs ensure that our representation focusing on motion information while capturing spatial details. We carry out the largest-scale evaluation of PVRs for robotic motor control to date, which encompasses 21 tasks within a real-world Franka robot arm and 5 simulated environments. Extensive experiments demonstrate the effectiveness of STP as well as unleash its generality and data efficiency by further post-pre-training and hybrid pre-training.