Title: Towards Learning Implicit Symbolic Representation for Visual Reasoning. Abstract: Visual reasoning tasks are designed to test a learning algorithm's capability to infer causal relationships, discover object interactions, and understand temporal dynamics, all from visual cues. It is commonly believed that to achieve compositional generalization on visual reasoning, an explicit abstraction of the visual scene must be constructed; for example, object detection can be applied to the visual input to produce representations that are then processed by a neural network or a neuro-symbolic framework. We demonstrate that a simple and general self-supervised approach is able to learn implicit symbolic representations with general-purpose neural networks, enabling the end-to-end learning of visual reasoning directly from raw visual inputs. Our proposed approach ``compresses'' each frame of a video into a small set of tokens with a transformer network. The self-supervised learning objective is to reconstruct each image based on the compressed temporal context. To minimize the reconstruction loss, the network must learn a compact representation for each image, as well as capture temporal dynamics and object permanence from temporal context. We evaluate the proposed approach on two visual reasoning benchmarks, CATER and ACRE. We observe that self-supervised pretraining is essential to achieve compositional generalization for our end-to-end trained neural network, and our proposed method achieves on par or better performance compared to recent neuro-symbolic approaches that often require additional object-level supervision.