Neuro-symbolic learning has emerged as a promising paradigm for interpretable visual reasoning, where mapping natural language questions to executable programs plays a central role. However, most existing methods focus exclusively on the forward program generation from questions while overlooking the reverse process of reconstructing questions from programs. In this paper, we propose BiPaR (Bidirectional Parsing and Reconstruction), a Transformer-based framework that jointly models both program parsing and question reconstruction within a unified architecture. Unlike previous approaches that only perform forward parsing, BiPaR introduces reverse program-to-question reconstruction as a powerful auxiliary signal, which improves program generation quality and accelerates convergence, particularly under limited supervision. We further provide a theoretical analysis showing how reverse reconstruction facilitates faster optimization during training. The bidirectional modeling makes BiPaR well-suited for both supervised and semi-supervised learning scenarios. We present two architectural variants: BiPaR-Full, which employs encoder-decoder Transformers for both modules; and BiPaR-DOnly, a lightweight variant that employs a decoder-only structure for question reconstruction, reducing model complexity. Experiments on CLEVR and a GQA subset demonstrate that BiPaR significantly outperforms standard Transformer baselines. Furthermore, in the semi-supervised learning setting, BiPaR achieves notable improvements by leveraging additional questions without program annotations.

The complexity and uncertainty in power systems leads to a great challenge for controlling the power grid using traditional manual adjustment methods. Reinforcement learning is a promising data-driven paradigm to address control issues in power grids. This article presents a novel human–machine collaborative (HMC) framework for line flow control. We formulate the collaboration between humans and machines as an extended Markov decision process (MDP) and introduce a human–machine collaborative reinforcement learning (HMC-RL) approach, which comprises a routing module, a machine dispatching module and an HMC dispatching module. The routing module determines whether the power system should be operated by the machine or through human–machine collaboration. The machine dispatching module predicts a machine dispatching action for regulating line flow, while the HMC module predicts an HMC dispatching action with human assistance. Experimental results conducted on the IEEE 39-bus and IEEE 118-bus systems demonstrate that our HMC-RL approach can significantly improve the performance of regulation compared to the machine dispatching policy. Specifically, HMC-RL achieves a 40.03% performance improvement on the IEEE 118-bus system, with 25.8% of human participation.