Vanilla spiking neurons are simplified from complex biological neurons with dendrites, soma, and synapses, into single somatic compartments. Due to limitations in performance and training efficiency, vanilla spiking neurons face significant challenges in modeling long sequences. In terms of performance, the oversimplified dynamics of spiking neurons omit long-term temporal dependencies. Additionally, the long-tail membrane potential distribution and binary activation discretization errors further limit their capacity to model long sequences. In terms of efficiency, the serial mechanism of spiking neurons leads to excessively long training times for long sequences. Though parallel spiking neurons are an efficient solution, their number of parameters is often tied to the hidden dimension or sequence length, which makes current parallel neurons unsuitable for large architectures. To address these issues, we propose **MMDEND**: a Multi-Branch Multi-Compartment Parallel Spiking Dendritic Neuron. Its proportion-adjustable multi-branch, multi-compartment structure enables long-term temporal dependencies. Additionally, we introduce a Scaling-Shifting Integer Firing (SSF) mechanism that fits the long-tail membrane potential distribution, retains efficiency, and mitigates discretization errors. Compared with parallel neurons, MMDEND achieves better long-sequence modeling capability with fewer parameters and lower energy consumption. Visualization also confirms that the SSF mechanism effectively fits long-tail distributions.

This paper provides an overview of the Shared Task RIRAG-2025, which focused on advancing the field of Regulatory Information Retrieval and Answer Generation (RIRAG). The task was designed to evaluate methods for answering regulatory questions using the ObliQA dataset. This paper summarizes the shared task, participants’ methods, and the results achieved by various teams.

Brain-inspired Spiking Neural Network (SNN) has demonstrated its effectiveness and efficiency in vision, natural language, and speech understanding tasks, indicating their capacity to “see”, “listen”, and “read”. In this paper, we design SpikeVoice, which performs high-quality Text-To-Speech (TTS) via SNN, to explore the potential of SNN to “speak”. A major obstacle to using SNN for such generative tasks lies in the demand for models to grasp long-term dependencies. The serial nature of spiking neurons, however, leads to the invisibility of information at future spiking time steps, limiting SNN models to capture sequence dependencies solely within the same time step. We term this phenomenon “partial-time dependency”. To address this issue, we introduce Spiking Temporal-Sequential Attention (STSA) in the SpikeVoice. To the best of our knowledge, SpikeVoice is the first TTS work in the SNN field. We perform experiments using four well-established datasets that cover both Chinese and English languages, encompassing scenarios with both single-speaker and multi-speaker configurations. The results demonstrate that SpikeVoice can achieve results comparable to Artificial Neural Networks (ANN) with only 10.5% energy consumption of ANN. Both our demo and code are available as supplementary material.

Recently, end-to-end speech translation (ST) has gained significant attention in research, but its progress is hindered by the limited availability of labeled data. To overcome this challenge, leveraging pre-trained models for knowledge transfer in ST has emerged as a promising direction. In this paper, we propose PETL-ST, which investigates parameter-efficient transfer learning for end-to-end speech translation. Our method utilizes two lightweight adaptation techniques, namely prefix and adapter, to modulate Attention and the Feed-Forward Network, respectively, while preserving the capabilities of pre-trained models. We conduct experiments on MuST-C En-De, Es, Fr, Ru datasets to evaluate the performance of our approach. The results demonstrate that PETL-ST outperforms strong baselines, achieving superior translation quality with high parameter efficiency. Moreover, our method exhibits remarkable data efficiency and significantly improves performance in low-resource settings.