Early childhood is a critical stage for cognitive development, involving core skills such as visual perception and reasoning. While multimodal large language models (MLLMs) have made rapid progress in various general-purpose tasks, their ability to support early education remains largely underexplored. Existing research on child-related AI largely centers on modeling language, emotion, or behavior, with limited focus on evaluating cognitive tasks relevant to early learning. To address this gap, we propose ChildBench, a multimodal benchmark designed to assess models on tasks inspired by early childhood cognitive development. It covers five key domains through ten tasks, including spatial reasoning, visual reasoning, visual discrimination, counting skills, and visual tracking. The benchmark includes 4,890 carefully constructed images and 5,346 manually annotated samples, ensuring both diversity and age-appropriate content. We evaluate a range of state-of-the-art (SoTA) open-source and closed-source MLLMs—including GPT-4o, Gemini, and Qwen2.5-VL—on ChildBench. Despite strong performance on other benchmarks, the best 7B-parameter model with LoRA tuning achieves only 52.01% accuracy, far below the 96% achieved by 5-year-old children. These results reveal critical limitations in fine-grained perception and reasoning. We further analyze failure cases and discuss directions for future model development.

Efficient execution is crucial to the successful deployment of deep learning models to real-world applications. Considerable recent effort have been devoted to computer systems for automatic discovery of efficient schedules for executing tensor programs on given hardware platforms. Built on TVM [1], Ansor [2] is the most recent and state-of-the-art framework for auto-scheduling deep neural net computation pipelines. In this paper, we present Bansor, an improved version of Ansor for automatic optimization of tensor programs, using bandit-based reinforcement learning (RL). We reexamine the algorithmic procedures of Ansor, and identify two selection sub-problems where RL techniques are readily usable. We then introduce bandit-based algorithms to balance exploration and exploitation during the processes of sketch selection and task scheduling. We evaluate the resulting algorithm, Bansor, on a wide range of tensor programs and two hardware platforms. Experiment results show that Bansor yields significant improvement over Ansor. On hard network test cases, Bansor uses an order of magnitude less number of measurement trails to attain Ansor’s best schedules, eventually converging to significantly better results given an equal number of measurement trials, despite the fact that Ansor’s performance has been superb.

In this paper, we propose a hybrid model that combines neural network and hidden Markov model for time-aware recommender systems. We use higher-order hidden Markov model to capture the temporal information of users and items in collaborative filtering systems. Because the computation of the transition matrix of higher-order hidden Markov model is hard, we compute the transition matrix by deep neural networks. We implement the algorithms of the hybrid model for offline batch-learning and online updating respectively. Experiments on real datasets demonstrate that the hybrid model has improvement performances over the existing recommender systems.