by jsendak | Nov 5, 2025 | Computer Science
arXiv:2511.02234v1 Announce Type: new
Abstract: Standard training for Multi-modal Large Language Models (MLLMs) involves concatenating non-textual information, like vision or audio, with a text prompt. This approach may not encourage deep integration of modalities, limiting the model’s ability to leverage the core language model’s reasoning capabilities. This work examined the impact of interleaved instruction tuning in an audio MLLM, where audio tokens are interleaved within the prompt. Using the Listen, Think, and Understand (LTU) model as a testbed, we conduct an experiment using the Synonym and Hypernym Audio Reasoning Dataset (SHARD), our newly created reasoning benchmark for audio-based semantic reasoning focusing on synonym and hypernym recognition. Our findings show that while even zero-shot interleaved prompting improves performance on our reasoning tasks, a small amount of fine-tuning using interleaved training prompts improves the results further, however, at the expense of the MLLM’s audio labeling ability.
Expert Commentary
The integration of multiple modalities in large language models is a multifaceted and complex process that requires careful consideration and experimentation. This study highlights the importance of exploring new approaches, such as interleaved instruction tuning, to enhance the performance of Multi-modal Large Language Models (MLLMs). By interleaving audio tokens within the text prompt, the researchers were able to improve the model’s reasoning capabilities in audio-based semantic reasoning tasks.
One of the key findings of this work is the trade-off between improving reasoning performance and preserving audio labeling ability in MLLMs. This suggests that there is a delicate balance that needs to be maintained when incorporating multiple modalities in language models. It also underscores the importance of fine-tuning training prompts to optimize performance on specific tasks, such as synonym and hypernym recognition in audio-based reasoning.
Multi-disciplinary Nature
This study bridges the gap between language models, audio processing, and semantic reasoning, demonstrating the multi-disciplinary nature of research in multimedia information systems. By examining how different modalities can be integrated and optimized in large language models, researchers are paving the way for more sophisticated applications in artificial reality, augmented reality, and virtual realities.
Overall, this work contributes valuable insights into the challenges and opportunities associated with training Multi-modal Large Language Models, highlighting the need for innovative approaches to maximize the model’s capabilities across various domains.
Read the original article
by jsendak | Nov 5, 2025 | Computer Science
Analysis of Workload Scheduler Solutions
In this review, the authors have delved into the realm of workload schedulers, examining various solutions that are currently being deployed and actively used in the industry. One of the key contributions of this analysis is the development of a taxonomy that categorizes these systems based on their architecture and design.
Key Design Factors
The authors focus on key design factors that have a significant impact on the throughput and scalability of workload scheduler solutions. These factors play a crucial role in determining the overall efficiency and performance of the system. By identifying and analyzing these design factors, the authors provide valuable insights into the mechanisms that drive the effectiveness of workload schedulers.
Incremental Improvements
Furthermore, the review highlights the incremental improvements that have been made in the architecture of workload scheduler systems. These refinements have led to advancements in performance, reliability, and overall system efficiency. By examining these incremental improvements, the authors shed light on the evolutionary process that is shaping the landscape of workload schedulers.
Google’s Borg
Special attention is given to Google’s Borg, one of the most advanced and extensively published systems in this domain. Google’s Borg has set a high standard for workload schedulers, showcasing innovative design principles and demonstrating remarkable scalability and performance. By closely examining Google’s Borg, the authors provide valuable insights into the cutting-edge technologies that are shaping the future of workload scheduling.
Expert Insights
As an expert commentator, it is clear that the authors have conducted a comprehensive and insightful analysis of workload scheduler solutions. By focusing on key design factors, incremental improvements, and advanced systems like Google’s Borg, this review offers valuable insights for researchers and practitioners in the field of distributed systems and cloud computing. Moving forward, it will be crucial for developers and designers to continue building upon these insights and pushing the boundaries of workload scheduler technology to meet the evolving demands of modern computing environments.
Read the original article
by jsendak | Nov 4, 2025 | Computer Science
arXiv:2511.00279v1 Announce Type: new
Abstract: We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal model with 560 billion parameters, excelling at real-time audio-visual interaction. By adopting a curriculum-inspired progressive training strategy that transitions from simpler to increasingly complex modality sequence modeling tasks, LongCat-Flash-Omni attains comprehensive multimodal capabilities while maintaining strong unimodal capability. Building upon LongCat-Flash, which adopts a high-performance Shortcut-connected Mixture-of-Experts (MoE) architecture with zero-computation experts, LongCat-Flash-Omni integrates efficient multimodal perception and speech reconstruction modules. Despite its immense size of 560B parameters (with 27B activated), LongCat-Flash-Omni achieves low-latency real-time audio-visual interaction. For training infrastructure, we developed a modality-decoupled parallelism scheme specifically designed to manage the data and model heterogeneity inherent in large-scale multimodal training. This innovative approach demonstrates exceptional efficiency by sustaining over 90% of the throughput achieved by text-only training. Extensive evaluations show that LongCat-Flash-Omni achieves state-of-the-art performance on omni-modal benchmarks among open-source models. Furthermore, it delivers highly competitive results across a wide range of modality-specific tasks, including text, image, and video understanding, as well as audio understanding and generation. We provide a comprehensive overview of the model architecture design, training procedures, and data strategies, and open-source the model to foster future research and development in the community.
Expert Commentary:
The LongCat-Flash-Omni model represents a significant advancement in the field of multimodal AI systems, combining state-of-the-art performance with a massive scale of 560 billion parameters. This model demonstrates a novel approach to training by incorporating a curriculum-inspired progressive strategy that transitions between simpler and more complex modality sequence modeling tasks. By maintaining strong unimodal capability while excelling in multimodal interaction, LongCat-Flash-Omni showcases the power of integrating diverse sensory inputs for more robust AI systems.
One of the key strengths of LongCat-Flash-Omni lies in its efficient multimodal perception and speech reconstruction modules, allowing for low-latency real-time audio-visual interaction. The model’s high-performance Shortcut-connected Mixture-of-Experts architecture with zero-computation experts enables it to handle the complexity of multimodal data while maintaining efficiency. This highlights the importance of leveraging both multimodal capabilities and computational efficiency in developing AI systems that can process diverse types of information simultaneously.
From a multidisciplinary perspective, the LongCat-Flash-Omni model touches upon various aspects of multimedia information systems, including text, image, video, and audio processing. Its ability to perform well across different modality-specific tasks demonstrates the versatility and adaptability of multimodal AI models in handling diverse types of data. As AI technologies continue to evolve, integrating concepts from artificial reality, augmented reality, and virtual realities into multimodal systems like LongCat-Flash-Omni could open up new possibilities for immersive and interactive applications.
The development of LongCat-Flash-Omni’s modality-decoupled parallelism scheme showcases the importance of addressing data and model heterogeneity in large-scale multimodal training. By efficiently managing the flow of information across different modalities, this innovative approach enables the model to sustain high throughput levels comparable to text-only training. This signifies a step forward in optimizing the training process for multimodal AI systems, paving the way for advancements in real-time interaction and performance across a wide range of tasks.
In conclusion, the LongCat-Flash-Omni model represents a significant contribution to the field of multimodal AI, with implications for the wider domains of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By pushing the boundaries of what is possible with multimodal models, this open-source initiative paves the way for further research and development in the community, driving innovation in AI technology and applications.
Read the original article
by jsendak | Nov 4, 2025 | Computer Science
Expert Commentary: Advancing Automated Testing in Virtual Reality with VRScout
Virtual Reality (VR) technology has made significant strides in recent years, bringing immersive gaming experiences to millions of users worldwide. However, ensuring the quality, safety, and appropriateness of VR content remains a critical challenge for developers and industry stakeholders. Traditional methods of human-based quality assurance are not only labor-intensive but also struggle to keep up with the rapid pace of VR development.
The introduction of automated testing tools, such as VRScout, represents a groundbreaking advancement in the field of VR game testing. By leveraging deep learning algorithms and human demonstrations, VRScout is able to autonomously navigate virtual environments and interact with objects in a realistic and human-like manner. This innovative approach not only streamlines the testing process but also enhances the overall efficiency and accuracy of QA procedures in VR development.
One of the key strengths of VRScout lies in its ability to predict multi-step action sequences through an enhanced Action Chunking Transformer. This enables the agent to learn higher-level strategies and adapt to diverse VR environments, ultimately achieving expert-level performance with limited training data. Furthermore, the dynamically adjustable sliding horizon helps to balance responsiveness and precision, ensuring real-time inference at 60 FPS on consumer-grade hardware.
By demonstrating the effectiveness of VRScout on commercial VR titles, the research team has laid a solid foundation for the widespread adoption of automated VR game testing. Not only does this technology offer a practical and scalable solution for quality assurance processes, but it also holds promising applications in safety auditing and compliance testing within the VR industry.
In conclusion, VRScout represents a significant step forward in the quest for efficient and reliable VR testing methodologies. As the VR landscape continues to evolve, tools like VRScout will play a critical role in ensuring the integrity and quality of virtual experiences for users worldwide.
Read the original article
by jsendak | Nov 3, 2025 | Computer Science
arXiv:2510.26818v1 Announce Type: cross
Abstract: Dance-to-music (D2M) generation aims to automatically compose music that is rhythmically and temporally aligned with dance movements. Existing methods typically rely on coarse rhythm embeddings, such as global motion features or binarized joint-based rhythm values, which discard fine-grained motion cues and result in weak rhythmic alignment. Moreover, temporal mismatches introduced by feature downsampling further hinder precise synchronization between dance and music. To address these problems, we propose textbf{GACA-DiT}, a diffusion transformer-based framework with two novel modules for rhythmically consistent and temporally aligned music generation. First, a textbf{genre-adaptive rhythm extraction} module combines multi-scale temporal wavelet analysis and spatial phase histograms with adaptive joint weighting to capture fine-grained, genre-specific rhythm patterns. Second, a textbf{context-aware temporal alignment} module resolves temporal mismatches using learnable context queries to align music latents with relevant dance rhythm features. Extensive experiments on the AIST++ and TikTok datasets demonstrate that GACA-DiT outperforms state-of-the-art methods in both objective metrics and human evaluation. Project page: https://beria-moon.github.io/GACA-DiT/.
Expert Commentary: GACA-DiT Framework for Dance-to-Music Generation
The concept of Dance-to-Music (D2M) generation is a fascinating and complex intersection of multiple disciplines such as music composition, dance choreography, and artificial intelligence. In this innovative study, the researchers propose the GACA-DiT framework, which leverages diffusion transformer-based models to enhance the synchronization and alignment between dance movements and music compositions.
One of the key challenges in D2M generation is achieving precise rhythmic alignment between the dance movements and the generated music. Traditional methods often use coarse rhythm embeddings that overlook fine-grained motion cues, leading to weak synchronization. The GACA-DiT framework addresses this issue by introducing a genre-adaptive rhythm extraction module that captures genre-specific rhythm patterns with multi-scale temporal wavelet analysis and spatial phase histograms. This allows for a more nuanced understanding of rhythm, leading to more authentic and engaging music compositions.
Furthermore, the context-aware temporal alignment module in GACA-DiT is designed to resolve temporal mismatches between dance and music features. By using learnable context queries, the model can dynamically adjust the alignment of music latents to synchronize with relevant dance rhythm features. This results in a more coherent and synchronized output, enhancing the overall D2M generation process.
From a multimedia information systems perspective, the GACA-DiT framework illustrates the potential of combining advanced AI techniques with music and dance analysis to create immersive and interactive experiences. With applications in animations, augmented reality, and virtual realities, the GACA-DiT framework could open up new possibilities for creative expression and interactive storytelling. By pushing the boundaries of interdisciplinary research in multimedia content generation, this study highlights the exciting potential of AI-driven technologies in the realm of creative arts.
In conclusion, the GACA-DiT framework represents a significant step forward in the field of D2M generation by addressing key challenges in rhythm alignment and temporal synchronization. By incorporating advanced AI techniques and multi-disciplinary concepts, this research opens up new avenues for innovation in multimedia content creation and interactive experiences.
Read the original article
by jsendak | Nov 3, 2025 | Computer Science
Expert Commentary: NanyinHGNN – A Breakthrough in Computational Ethnomusicology
In the realm of computational ethnomusicology, where cultural preservation meets cutting-edge technology, the development of NanyinHGNN represents a crucial advancement in the field. Nanyin, a UNESCO-recognized intangible cultural heritage, poses unique challenges due to its heterophonic tradition centered around the pipa. This tradition, characterized by orally transmitted ornamentations layered over notated core melodies, has long presented difficulties for preservation and innovation.
The NanyinHGNN model tackles these challenges head-on by leveraging a Pipa-Centric MIDI dataset and a specialized tokenization method, NanyinTok, to capture the nuances of Nanyin music. Through the conversion of symbolic sequences into graph structures, the model ensures the preservation of key musical features and the authenticity of the generated heterophonic ensembles.
One of the standout features of NanyinHGNN is its innovative approach to ornamentation generation. By reframing ornamentations as nodes within a heterogeneous graph, the model seamlessly integrates melodic outlines optimized for ornamentations with a rule-guided system informed by Nanyin performance practices. This unique methodology not only produces authentic ornamentations but also does so without the need for explicit ornamentation annotations during training.
The experimental results showcasing the successful generation of heterophonic ensembles featuring traditional instruments validate the efficacy of NanyinHGNN in addressing data scarcity challenges in computational ethnomusicology. By incorporating domain-specific knowledge into the model architecture, the researchers have demonstrated that a deep understanding of cultural context can enhance the effectiveness of AI models in preserving and innovating upon intangible cultural heritage.
Read the original article
