While audio large language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning capabilities. This performance disparity primarily stems from the scarcity of high-quality audio reasoning data. To bridge this modality gap, we propose a novel cross-modal on-policy distillation framework. Our approach leverages a powerful text model as a teacher to provide dynamic correction and guidance to the audio foundational model during on-policy exploration. Furthermore, we construct a three-tier symmetric dataset encompassing logical reasoning, complex audio reasoning, and speech dialogue. By dynamically aligning the student model’s reasoning trajectories within the high-noise auditory space with the teacher model’s prior knowledge in the clean text space, our method effectively distills robust reasoning capabilities into the audio domain. Extensive experiments demonstrate that our model achieves substantial improvements on the audio and speech understanding benchmarks, paving a new path for comprehensive audio-language understanding.

The latest advancements in large language models (LLMs) have sparked interest in their potential for software vulnerability detection. However, there is currently a lack of research specifically focused on vulnerabilities in the PHP language, and challenges in data sampling and processing persist, hindering the model’s ability to effectively capture the characteristics of specific vulnerabilities. In this paper, we present RealVul, the first LLM-based framework designed for PHP vulnerability detection, addressing these issues. By improving code sampling methods and employing normalization techniques, we can isolate potential vulnerability triggers while streamlining the code and eliminating unnecessary semantic information, enabling the model to better understand and learn from the generated vulnerability samples. We also address the issue of insufficient PHP vulnerability samples by improving data synthesis methods. To evaluate RealVul’s performance, we conduct an extensive analysis using five distinct code LLMs on vulnerability data from 180 PHP projects. The results demonstrate a significant improvement in both effectiveness and generalization compared to existing methods, effectively boosting the vulnerability detection capabilities of these models.

Long short-term memory (LSTM) language model (LM) has been widely investigated for automatic speech recognition (ASR) and natural language processing (NLP). Although excellent performance is obtained for large vocabulary tasks, tremendous memory consumption prohibits the use of LSTM LM in low-resource devices. The memory consumption mainly comes from the word embedding layer. In this paper, a novel binarized LSTM LM is proposed to address the problem. Words are encoded into binary vectors and other LSTM parameters are further binarized to achieve high memory compression. This is the first effort to investigate binary LSTM for large vocabulary LM. Experiments on both English and Chinese LM and ASR tasks showed that can achieve a compression ratio of 11.3 without any loss of LM and ASR performances and a compression ratio of 31.6 with acceptable minor performance degradation.