OpenRaiser/Intern-Atlas huggingface dataset:
Prompt-Guided Turn-Taking Prediction
Venue: arXiv cs.CL 2025
Year: 2025

Turn-taking prediction models are essential components in spoken dialogue systems and conversational robots. Recent approaches leverage transformer-based architectures to predict speech activity continuously and in real-time. In this study, we propose a novel model that enables turn-taking prediction to be dynamically controlled via textual prompts. This approach allows intuitive and explicit control through instructions such as "faster" or "calmer" adapting dynamically to conversational partners and contexts. The proposed model builds upon a transformer-based voice activity projection (VAP) model, incorporating textual prompt embeddings into both channel-wise transformers and a cross-channel transformer. We evaluated the feasibility of our approach using over 950 hours of human-human spoken dialogue data. Since textual prompt data for the proposed approach was not available in existing datasets, we utilized a large language model (LLM) to generate synthetic prompt sentences. Experimental results demonstrated that the proposed model improved prediction accuracy and effectively varied turn-taking timing behaviors according to the textual prompts.
Follow the user meaningfully and product growth will follow: A mixed methods case study tying UX Point of View & Growth leading to measurable impact
Venue: arXiv cs.HC 2025
Year: 2025

Have you wondered how cross-functional teams balance between maximizing value that users derive and business growth leading to win-win situations? This case study shows how User Experience Research (UXR) and Data Science teams used mixed methods research to strategically influence Product Led Growth (PLG) for a Password Manager used by million+ users, thus allowing our users, internal teams, and business to win. The audience will take away practical lessons/techniques related to leveraging mixed methods to: a. Maximize user value while meeting business growth goals b. Influence cross-functional teams c. Measure user and business impact This case study can be easily tied to the UXR Point of view pyramid (POV) [2] that represents a methodological approach to construct a POV and further dives into actioning POV to create measurable user and business impact.
MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification
Venue: arXiv cs.CV 2025
Year: 2025

Current medical image analysis systems are typically task-specific, requiring separate models for classification and segmentation, and lack the flexibility to support user-defined workflows. To address these challenges, we introduce MedPrompt, a unified framework that combines a few-shot prompted Large Language Model (Llama-4-17B) for high-level task planning with a modular Convolutional Neural Network (DeepFusionLab) for low-level image processing. The LLM interprets user instructions and generates structured output to dynamically route task-specific pretrained weights. This weight routing approach avoids retraining the entire framework when adding new tasks-only task-specific weights are required, enhancing scalability and deployment. We evaluated MedPrompt across 19 public datasets, covering 12 tasks spanning 5 imaging modalities. The system achieves a 97% end-to-end correctness in interpreting and executing prompt-driven instructions, with an average inference latency of 2.5 seconds, making it suitable for near real-time applications. DeepFusionLab achieves competitive segmentation accuracy (e.g., Dice 0.9856 on lungs) and strong classification performance (F1 0.9744 on tuberculosis). Overall, MedPrompt enables scalable, prompt-driven medical imaging by combining the interpretability of LLMs with the efficiency of modular CNNs.
Subtitled Media Adaptations for People with Aphasia: Ongoing Accessibility Barriers and Emerging Design Practices
Venue: arXiv cs.HC 2025
Year: 2025

The consumption of subtitles via TVs, laptops and smartphones has the potential to marginalize people based on their complex accessibility needs. The current one-size-fits-all approach to this accessibility aid is no longer fit for purpose and work is required to look at how it can be adapted to be personalised for individual users based on individual context, content, and consumption habits. People with Aphasia, for example, encounter significant challenges in understanding subtitle texts. We see our work as a call to action for more inclusive practices, focusing on how the thoughts and opinions of people with aphasia can be included in media research. Our work investigates how to develop future media solutions for people with aphasia to create a more inclusive media viewing environment. We believe the key to this is appropriate prototyping tools and methods to allow equitable inclusion in the system design process.
Condensed Representation of RDF and its Application on Graph Versioning
Venue: arXiv cs.DB 2025
Year: 2025

Evolving phenomena, often complex, can be represented using knowledge graphs, which have the capability to model heterogeneous data from multiple sources. Nowadays, a considerable amount of sources delivering periodic updates to knowledge graphs in various domains is openly available. The evolution of data is of interest to knowledge graph management systems, and therefore it is crucial to organize these constantly evolving data to make them easily accessible and exploitable for analysis. In this article, we will present and formalize the condensed representation of these evolving graphs and propose a new solution called QuaQue that allows querying across multiple versions of graphs and we also present the results of our benchmark comparing our solution against existing approaches.
Edge Clique Partition and Cover Beyond Independence
Venue: arXiv cs.DS 2025
Year: 2025

Covering and partitioning the edges of a graph into cliques are classical problems at the intersection of combinatorial optimization and graph theory, having been studied through a range of algorithmic and complexity-theoretic lenses. Despite the well-known fixed-parameter tractability of these problems when parameterized by the total number of cliques, such a parameterization often fails to be meaningful for sparse graphs. In many real-world instances, on the other hand, the minimum number of cliques in an edge cover or partition can be very close to the size of a maximum independent set α(G). Motivated by this observation, we investigate above αparameterizations of the edge clique cover and partition problems. Concretely, we introduce and study Edge Clique Cover Above Independent Set (ECC/α) and Edge Clique Partition Above Independent Set (ECP/α), where the goal is to cover or partition all edges of a graph using at most α(G) + k cliques, and k is the parameter. Our main results reveal a distinct complexity landscape for the two variants. We show that ECP/αis fixed-parameter tractable, whereas ECC/αis NP-complete for all k \geq 2, yet can be solved in polynomial time for k \in {0,1}. These findings highlight intriguing differences between the two problems when viewed through the lens of parameterization above a natural lower bound. Finally, we demonstrate that ECC/αbecomes fixed-parameter tractable when parameterized by k + ω(G), where ω(G) is the size of a maximum clique of the graph G. This result is particularly relevant for sparse graphs, in which ωis typically small. For H-minor free graphs, we design a subexponential algorithm of running time f(H)^{\sqrt{k}}n^{O(1)}.
Video Virtual Try-on with Conditional Diffusion Transformer Inpainter
Venue: arXiv cs.CV 2025
Year: 2025

Video virtual try-on aims to naturally fit a garment to a target person in consecutive video frames. It is a challenging task, on the one hand, the output video should be in good spatial-temporal consistency, on the other hand, the details of the given garment need to be preserved well in all the frames. Naively using image-based try-on methods frame by frame can get poor results due to severe inconsistency. Recent diffusion-based video try-on methods, though very few, happen to coincide with a similar solution: inserting temporal attention into image-based try-on model to adapt it for video try-on task, which have shown improvements but there still exist inconsistency problems. In this paper, we propose ViTI (Video Try-on Inpainter), formulate and implement video virtual try-on as a conditional video inpainting task, which is different from previous methods. In this way, we start with a video generation problem instead of an image-based try-on problem, which from the beginning has a better spatial-temporal consistency. Specifically, at first we build a video inpainting framework based on Diffusion Transformer with full 3D spatial-temporal attention, and then we progressively adapt it for video garment inpainting, with a collection of masking strategies and multi-stage training. After these steps, the model can inpaint the masked garment area with appropriate garment pixels according to the prompt with good spatial-temporal consistency. Finally, as other try-on methods, garment condition is added to the model to make sure the inpainted garment appearance and details are as expected. Both quantitative and qualitative experimental results show that ViTI is superior to previous works.
HieraSurg: Hierarchy-Aware Diffusion Model for Surgical Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

Surgical Video Synthesis has emerged as a promising research direction following the success of diffusion models in general-domain video generation. Although existing approaches achieve high-quality video generation, most are unconditional and fail to maintain consistency with surgical actions and phases, lacking the surgical understanding and fine-grained guidance necessary for factual simulation. We address these challenges by proposing HieraSurg, a hierarchy-aware surgical video generation framework consisting of two specialized diffusion models. Given a surgical phase and an initial frame, HieraSurg first predicts future coarse-grained semantic changes through a segmentation prediction model. The final video is then generated by a second-stage model that augments these temporal segmentation maps with fine-grained visual features, leading to effective texture rendering and integration of semantic information in the video space. Our approach leverages surgical information at multiple levels of abstraction, including surgical phase, action triplets, and panoptic segmentation maps. The experimental results on Cholecystectomy Surgical Video Generation demonstrate that the model significantly outperforms prior work both quantitatively and qualitatively, showing strong generalization capabilities and the ability to generate higher frame-rate videos. The model exhibits particularly fine-grained adherence when provided with existing segmentation maps, suggesting its potential for practical surgical applications.
Exploring Adapter Design Tradeoffs for Low Resource Music Generation
Venue: arXiv cs.SD 2025
Year: 2025

Fine-tuning large-scale music generation models, such as MusicGen and Mustango, is a computationally expensive process, often requiring updates to billions of parameters and, therefore, significant hardware resources. Parameter-Efficient Fine-Tuning (PEFT) techniques, particularly adapter-based methods, have emerged as a promising alternative, enabling adaptation with minimal trainable parameters while preserving model performance. However, the design choices for adapters, including their architecture, placement, and size, are numerous, and it is unclear which of these combinations would produce optimal adapters and why, for a given case of low-resource music genre. In this paper, we attempt to answer this question by studying various adapter configurations for two AI music models, MusicGen and Mustango, on two genres: Hindustani Classical and Turkish Makam music. Our findings reveal distinct trade-offs: convolution-based adapters excel in capturing fine-grained local musical details such as ornamentations and short melodic phrases, while transformer-based adapters better preserve long-range dependencies crucial for structured improvisation. Additionally, we analyze computational resource requirements across different adapter scales, demonstrating how mid-sized adapters (40M parameters) achieve an optimal balance between expressivity and quality. Furthermore, we find that Mustango, a diffusion-based model, generates more diverse outputs with better adherence to the description in the input prompt while lacking in providing stability in notes, rhythm alignment, and aesthetics. Also, it is computationally intensive and requires significantly more time to train. In contrast, autoregressive models like MusicGen offer faster training and are more efficient, and can produce better quality output in comparison, but have slightly higher redundancy in their generations.
Automatic Reviewers Assignment to a Research Paper Based on Allied References and Publications Weight
Venue: arXiv cs.DL 2025
Year: 2025

Everyday, a vast stream of research documents is submitted to conferences, anthologies, journals, newsletters, annual reports, daily papers, and various periodicals. Many such publications use independent external specialists to review submissions. This process is called peer review, and the reviewers are called referees. However, it is not always possible to pick the best referee for reviewing. Moreover, new research fields are emerging in every sector, and the number of research papers is increasing dramatically. To review all these papers, every journal assigns a small team of referees who may not be experts in all areas. For example, a research paper in communication technology should be reviewed by an expert from the same field. Thus, efficiently selecting the best reviewer or referee for a research paper is a big challenge. In this research, we propose and implement program that uses a new strategy to automatically select the best reviewers for a research paper. Every research paper contains references at the end, usually from the same area. First, we collect the references and count authors who have at least one paper in the references. Then, we automatically browse the web to extract research topic keywords. Next, we search for top researchers in the specific topic and count their h-index, i10-index, and citations for the first n authors. Afterward, we rank the top n authors based on a score and automatically browse their homepages to retrieve email addresses. We also check their co-authors and colleagues online and discard them from the list. The remaining top n authors, generally professors, are likely the best referees for reviewing the research paper.
CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations
Venue: arXiv cs.CV 2025
Year: 2025

2D scene graphs provide a structural and explainable framework for scene understanding. However, current work still struggles with the lack of accurate scene graph data. To overcome this data bottleneck, we present CoPa-SG, a synthetic scene graph dataset with highly precise ground truth and exhaustive relation annotations between all objects. Moreover, we introduce parametric and proto-relations, two new fundamental concepts for scene graphs. The former provides a much more fine-grained representation than its traditional counterpart by enriching relations with additional parameters such as angles or distances. The latter encodes hypothetical relations in a scene graph and describes how relations would form if new objects are placed in the scene. Using CoPa-SG, we compare the performance of various scene graph generation models. We demonstrate how our new relation types can be integrated in downstream applications to enhance planning and reasoning capabilities.
ToosiCubix: Monocular 3D Cuboid Labeling via Vehicle Part Annotations
Venue: arXiv cs.CV 2025
Year: 2025

Many existing methods for 3D cuboid annotation of vehicles rely on expensive and carefully calibrated camera-LiDAR or stereo setups, limiting their accessibility for large-scale data collection. We introduce ToosiCubix, a simple yet powerful approach for annotating ground-truth cuboids using only monocular images and intrinsic camera parameters. Our method requires only about 10 user clicks per vehicle, making it highly practical for adding 3D annotations to existing datasets originally collected without specialized equipment. By annotating specific features (e.g., wheels, car badge, symmetries) across different vehicle parts, we accurately estimate each vehicle's position, orientation, and dimensions up to a scale ambiguity (8 DoF). The geometric constraints are formulated as an optimization problem, which we solve using a coordinate descent strategy, alternating between Perspective-n-Points (PnP) and least-squares subproblems. To handle common ambiguities such as scale and unobserved dimensions, we incorporate probabilistic size priors, enabling 9 DoF cuboid placements. We validate our annotations against the KITTI and Cityscapes3D datasets, demonstrating that our method offers a cost-effective and scalable solution for high-quality 3D cuboid annotation.
Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) excel in understanding and generating text but struggle with providing professional literary criticism for works with profound thoughts and complex narratives. This paper proposes GLASS (Greimas Literary Analysis via Semiotic Square), a structured analytical framework based on Greimas Semiotic Square (GSS), to enhance LLMs' ability to conduct in-depth literary analysis. GLASS facilitates the rapid dissection of narrative structures and deep meanings in narrative works. We propose the first dataset for GSS-based literary criticism, featuring detailed analyses of 48 works. Then we propose quantitative metrics for GSS-based literary criticism using the LLM-as-a-judge paradigm. Our framework's results, compared with expert criticism across multiple works and LLMs, show high performance. Finally, we applied GLASS to 39 classic works, producing original and high-quality analyses that address existing research gaps. This research provides an AI-based tool for literary research and education, offering insights into the cognitive mechanisms underlying literary engagement.
rQdia: Regularizing Q-Value Distributions With Image Augmentation
Venue: arXiv cs.LG 2025
Year: 2025

rQdia regularizes Q-value distributions with augmented images in pixel-based deep reinforcement learning. With a simple auxiliary loss, that equalizes these distributions via MSE, rQdia boosts DrQ and SAC on 9/12 and 10/12 tasks respectively in the MuJoCo Continuous Control Suite from pixels, and Data-Efficient Rainbow on 18/26 Atari Arcade environments. Gains are measured in both sample efficiency and longer-term training. Moreover, the addition of rQdia finally propels model-free continuous control from pixels over the state encoding baseline.
GenFlow: Interactive Modular System for Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

Generative art unlocks boundless creative possibilities, yet its full potential remains untapped due to the technical expertise required for advanced architectural concepts and computational workflows. To bridge this gap, we present GenFlow, a novel modular framework that empowers users of all skill levels to generate images with precision and ease. Featuring a node-based editor for seamless customization and an intelligent assistant powered by natural language processing, GenFlow transforms the complexity of workflow creation into an intuitive and accessible experience. By automating deployment processes and minimizing technical barriers, our framework makes cutting-edge generative art tools available to everyone. A user study demonstrated GenFlow's ability to optimize workflows, reduce task completion times, and enhance user understanding through its intuitive interface and adaptive features. These results position GenFlow as a groundbreaking solution that redefines accessibility and efficiency in the realm of generative art.
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection
Venue: arXiv cs.LG 2025
Year: 2025

Cryptocurrency transaction fraud detection faces the dual challenges of increasingly complex transaction patterns and severe class imbalance. Traditional methods rely on manual feature engineering and struggle to capture temporal and structural dependencies in transaction networks. This paper proposes an Augmented Temporal-aware Graph Attention Network (ATGAT) that enhances detection performance through three modules: (1) designing an advanced temporal embedding module that fuses multi-scale time difference features with periodic position encoding; (2) constructing a temporal-aware triple attention mechanism that jointly optimizes structural, temporal, and global context attention; (3) employing weighted BCE loss to address class imbalance. Experiments on the Elliptic++ cryptocurrency dataset demonstrate that ATGAT achieves an AUC of 0.9130, representing a 9.2% improvement over the best traditional method XGBoost, 12.0% over GCN, and 10.0% over standard GAT. This method not only validates the enhancement effect of temporal awareness and triple attention mechanisms on graph neural networks, but also provides financial institutions with more reliable fraud detection tools, with its design principles generalizable to other temporal graph anomaly detection tasks.
Flowcut Switching: High-Performance Adaptive Routing with In-Order Delivery Guarantees
Venue: arXiv cs.NI 2025
Year: 2025

Network latency severely impacts the performance of applications running on supercomputers. Adaptive routing algorithms route packets over different available paths to reduce latency and improve network utilization. However, if a switch routes packets belonging to the same network flow on different paths, they might arrive at the destination out-of-order due to differences in the latency of these paths. For some transport protocols like TCP, QUIC, and RoCE, out-of-order (OOO) packets might cause large performance drops or significantly increase CPU utilization. In this work, we propose flowcut switching, a new adaptive routing algorithm that provides high-performance in-order packet delivery. Differently from existing solutions like flowlet switching, which are based on the assumption of bursty traffic and that might still reorder packets, flowcut switching guarantees in-order delivery under any network conditions, and is effective also for non-bursty traffic, as it is often the case for RDMA.
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Venue: arXiv cs.LG 2025
Year: 2025

Despite their widespread use, large language models (LLMs) are known to hallucinate incorrect information and be poorly calibrated. This makes the uncertainty quantification of these models of critical importance, especially in high-stakes domains, such as autonomy and healthcare. Prior work has made Bayesian deep learning-based approaches to this problem more tractable by performing inference over the low-rank adaptation (LoRA) parameters of a fine-tuned model. While effective, these approaches struggle to scale to larger LLMs due to requiring further additional parameters compared to LoRA. In this work we present $\textbf{Scala}$ble $\textbf{B}$ayesian $\textbf{L}$ow-Rank Adaptation via Stochastic Variational Subspace Inference (ScalaBL). We perform Bayesian inference in an $r$-dimensional subspace, for LoRA rank $r$. By repurposing the LoRA parameters as projection matrices, we are able to map samples from this subspace into the full weight space of the LLM. This allows us to learn all the parameters of our approach using stochastic variational inference. Despite the low dimensionality of our subspace, we are able to achieve competitive performance with state-of-the-art approaches while only requiring ${\sim}1000$ additional parameters. Furthermore, it allows us to scale up to the largest Bayesian LLM to date, with four times as a many base parameters as prior work.