OpenRaiser/Intern-Atlas huggingface dataset:
Music Similarity Representation Learning Focusing on Individual Instruments with Source Separation and Human Preference
Venue: arXiv cs.SD 2025
Year: 2025

This paper proposes music similarity representation learning (MSRL) based on individual instrument sounds (InMSRL) utilizing music source separation (MSS) and human preference without requiring clean instrument sounds during inference. We propose three methods that effectively improve performance. First, we introduce end-to-end fine-tuning (E2E-FT) for the Cascade approach that sequentially performs MSS and music similarity feature extraction. E2E-FT allows the model to minimize the adverse effects of a separation error on the feature extraction. Second, we propose multi-task learning for the Direct approach that directly extracts disentangled music similarity features using a single music similarity feature extractor. Multi-task learning, which is based on the disentangled music similarity feature extraction and MSS based on reconstruction with disentangled music similarity features, further enhances instrument feature disentanglement. Third, we employ perception-aware fine-tuning (PAFT). PAFT utilizes human preference, allowing the model to perform InMSRL aligned with human perceptual similarity. We conduct experimental evaluations and demonstrate that 1) E2E-FT for Cascade significantly improves InMSRL performance, 2) the multi-task learning for Direct is also helpful to improve disentanglement performance in the feature extraction, 3) PAFT significantly enhances the perceptual InMSRL performance, and 4) Cascade with E2E-FT and PAFT outperforms Direct with the multi-task learning and PAFT.
Verbal Process Supervision Elicits Better Coding Agents
Venue: arXiv cs.AI 2025
Year: 2025

The emergence of large language models and their applications as AI agents have significantly advanced state-of-the-art code generation benchmarks, transforming modern software engineering tasks. However, even with test-time computed reasoning models, these systems still struggle with complex software engineering challenges. This work introduces CURA, a code understanding and reasoning agent system enhanced with verbal process supervision (VPS), achieving a 3.65\% improvement over baseline models on challenging benchmarks like BigCodeBench. Furthermore, CURA, when paired with the o3-mini model and VPS techniques, attains state-of-the-art performance. This work represents a step forward in integrating reasoning-driven architectures with LLM-based code generation, enabling agentic reasoning for language models to solve complex software engineering tasks.
k-NN as a Simple and Effective Estimator of Transferability
Venue: arXiv cs.LG 2025
Year: 2025

How well can one expect transfer learning to work in a new setting where the domain is shifted, the task is different, and the architecture changes? Many transfer learning metrics have been proposed to answer this question. But how accurate are their predictions in a realistic new setting? We conducted an extensive evaluation involving over 42,000 experiments comparing 23 transferability metrics across 16 different datasets to assess their ability to predict transfer performance. Our findings reveal that none of the existing metrics perform well across the board. However, we find that a simple k-nearest neighbor evaluation -- as is commonly used to evaluate feature quality for self-supervision -- not only surpasses existing metrics, but also offers better computational efficiency and ease of implementation.
MMCR: Advancing Visual Language Model in Multimodal Multi-Turn Contextual Reasoning
Venue: arXiv cs.AI 2025
Year: 2025

Compared to single-turn dialogue, multi-turn dialogue involving multiple images better aligns with the needs of real-world human-AI interactions. Additionally, as training data, it provides richer contextual reasoning information, thereby guiding the model to achieve better performance. However, existing vision-language models (VLMs) primarily rely on single-turn dialogue training and evaluation benchmarks. In this paper, following the characteristics of human dialogue, such as focused topics and concise, clear content, we present MMCR (Multimodal Multi-turn Contextual Reasoning), a novel dataset comprising: (1) MMCR-310k -- the largest multi-image multi-turn instruction tuning dataset with 310K contextual dialogues, each covering 1-4 images and 4 or 8 dialogue turns; and (2) MMCR-Bench -- a diagnostic benchmark featuring dialogues, spanning 8 domains (Humanities, Natural, Science, Education, etc.) and 40 sub-topics. Extensive evaluations demonstrate that models fine-tuned with MMCR-310k achieve 5.2\% higher contextual accuracy on MMCR-Bench, while showing consistent improvements on existing benchmarks (+1.1\% on AI2D, +1.2\% on MMMU and MMVet). MMCR and prompt engineering will be released publicly.
EvAnimate: Event-conditioned Image-to-Video Generation for Human Animation
Venue: arXiv cs.CV 2025
Year: 2025

Conditional human animation traditionally animates static reference images using pose-based motion cues extracted from video data. However, these video-derived cues often suffer from low temporal resolution, motion blur, and unreliable performance under challenging lighting conditions. In contrast, event cameras inherently provide robust and high temporal-resolution motion information, offering resilience to motion blur, low-light environments, and exposure variations. In this paper, we propose EvAnimate, the first method leveraging event streams as robust and precise motion cues for conditional human image animation. Our approach is fully compatible with diffusion-based generative models, enabled by encoding asynchronous event data into a specialized three-channel representation with adaptive slicing rates and densities. High-quality and temporally coherent animations are achieved through a dual-branch architecture explicitly designed to exploit event-driven dynamics, significantly enhancing performance under challenging real-world conditions. Enhanced cross-subject generalization is further achieved using specialized augmentation strategies. To facilitate future research, we establish a new benchmarking, including simulated event data for training and validation, and a real-world event dataset capturing human actions under normal and challenging scenarios. The experiment results demonstrate that EvAnimate achieves high temporal fidelity and robust performance in scenarios where traditional video-derived cues fall short.
LeanStereo: A Leaner Backbone based Stereo Network
Venue: arXiv cs.CV 2025
Year: 2025

Recently, end-to-end deep networks based stereo matching methods, mainly because of their performance, have gained popularity. However, this improvement in performance comes at the cost of increased computational and memory bandwidth requirements, thus necessitating specialized hardware (GPUs); even then, these methods have large inference times compared to classical methods. This limits their applicability in real-world applications. Although we desire high accuracy stereo methods albeit with reasonable inference time. To this end, we propose a fast end-to-end stereo matching method. Majority of this speedup comes from integrating a leaner backbone. To recover the performance lost because of a leaner backbone, we propose to use learned attention weights based cost volume combined with LogL1 loss for stereo matching. Using LogL1 loss not only improves the overall performance of the proposed network but also leads to faster convergence. We do a detailed empirical evaluation of different design choices and show that our method requires 4x less operations and is also about 9 to 14x faster compared to the state of the art methods like ACVNet [1], LEAStereo [2] and CFNet [3] while giving comparable performance.
AMD-Hummingbird: Towards an Efficient Text-to-Video Model
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-Video (T2V) generation has attracted significant attention for its ability to synthesize realistic videos from textual descriptions. However, existing models struggle to balance computational efficiency and high visual quality, particularly on resource-limited devices, e.g.,iGPUs and mobile phones. Most prior work prioritizes visual fidelity while overlooking the need for smaller, more efficient models suitable for real-world deployment. To address this challenge, we propose a lightweight T2V framework, termed Hummingbird, which prunes existing models and enhances visual quality through visual feedback learning. Our approach reduces the size of the U-Net from 1.4 billion to 0.7 billion parameters, significantly improving efficiency while preserving high-quality video generation. Additionally, we introduce a novel data processing pipeline that leverages Large Language Models (LLMs) and Video Quality Assessment (VQA) models to enhance the quality of both text prompts and video data. To support user-driven training and style customization, we publicly release the full training code, including data processing and model training. Extensive experiments show that our method achieves a 31X speedup compared to state-of-the-art models such as VideoCrafter2, while also attaining the highest overall score on VBench. Moreover, our method supports the generation of videos with up to 26 frames, addressing the limitations of existing U-Net-based methods in long video generation. Notably, the entire training process requires only four GPUs, yet delivers performance competitive with existing leading methods. Hummingbird presents a practical and efficient solution for T2V generation, combining high performance, scalability, and flexibility for real-world applications.
Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning in non-stationary environments is challenging due to abrupt and unpredictable changes in dynamics, often causing traditional algorithms to fail to converge. However, in many real-world cases, non-stationarity has some structure that can be exploited to develop algorithms and facilitate theoretical analysis. We introduce one such structure, Switching Non-Stationary Markov Decision Processes (SNS-MDP), where environments switch over time based on an underlying Markov chain. Under a fixed policy, the value function of an SNS-MDP admits a closed-form solution determined by the Markov chain's statistical properties, and despite the inherent non-stationarity, Temporal Difference (TD) learning methods still converge to the correct value function. Furthermore, policy improvement can be performed, and it is shown that policy iteration converges to the optimal policy. Moreover, since Q-learning converges to the optimal Q-function, it likewise yields the corresponding optimal policy. To illustrate the practical advantages of SNS-MDPs, we present an example in communication networks where channel noise follows a Markovian pattern, demonstrating how this framework can effectively guide decision-making in complex, time-varying contexts.
Reading Decisions from Gaze Direction during Graphics Turing Test of Gait Animation
Venue: arXiv cs.HC 2025
Year: 2025

We investigated gaze direction during movement observation. The eye movement data were collected during an experiment, in which different models of movement production (based on movement primitives, MPs) were compared in a two alternatives forced choice task (2AFC). Participants observed side-by-side presentation of two naturalistic 3D-rendered human movement videos, where one video was based on motion captured gait sequence, the other one was generated by recombining the machine-learned MPs to approximate the same movement. The task was to discriminate between these movements while their eye movements were recorded. We are complementing previous binary decision data analyses with eye tracking data. Here, we are investigating the role of gaze direction during task execution. We computed the shared information between gaze features and decisions of the participants, and between gaze features and correct answers. We found that eye movements reflect the decision of participants during the 2AFC task, but not the correct answer. This result is important for future experiments, which should take advantage of eye tracking to complement binary decision data.
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology
Venue: arXiv cs.CV 2025
Year: 2025

Vision foundation models (FMs) are accelerating the development of digital pathology algorithms and transforming biomedical research. These models learn, in a self-supervised manner, to represent histological features in highly heterogeneous tiles extracted from whole-slide images (WSIs) of real-world patient samples. The performance of these FMs is significantly influenced by the size, diversity, and balance of the pre-training data. However, data selection has been primarily guided by expert knowledge at the WSI level, focusing on factors such as disease classification and tissue types, while largely overlooking the granular details available at the tile level. In this paper, we investigate the potential of unsupervised automatic data curation at the tile-level, taking into account 350 million tiles. Specifically, we apply hierarchical clustering trees to pre-extracted tile embeddings, allowing us to sample balanced datasets uniformly across the embedding space of the pretrained FM. We further identify these datasets are subject to a trade-off between size and balance, potentially compromising the quality of representations learned by FMs, and propose tailored batch sampling strategies to mitigate this effect. We demonstrate the effectiveness of our method through improved performance on a diverse range of clinically relevant downstream tasks.
Intuitionistic modal logics: new and simpler decidability proofs for FIK and LIK
Venue: arXiv cs.LO 2025
Year: 2025

In this note, by integrating ideas concerning terminating tableaux-based procedures in modal logics and finite frame property of intuitionistic modal logic IK, we provide new and simpler decidability proofs for FIK and LIK.
SFDLA: Source-Free Document Layout Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Document Layout Analysis (DLA) is a fundamental task in document understanding. However, existing DLA and adaptation methods often require access to large-scale source data and target labels. This requirements severely limiting their real-world applicability, particularly in privacy-sensitive and resource-constrained domains, such as financial statements, medical records, and proprietary business documents. According to our observation, directly transferring source-domain fine-tuned models on target domains often results in a significant performance drop (Avg. -32.64%). In this work, we introduce Source-Free Document Layout Analysis (SFDLA), aiming for adapting a pre-trained source DLA models to an unlabeled target domain, without access to any source data. To address this challenge, we establish the first SFDLA benchmark, covering three major DLA datasets for geometric- and content-aware adaptation. Furthermore, we propose Document Layout Analysis Adapter (DLAdapter), a novel framework that is designed to improve source-free adaptation across document domains. Our method achieves a +4.21% improvement over the source-only baseline and a +2.26% gain over existing source-free methods from PubLayNet to DocLayNet. We believe this work will inspire the DLA community to further investigate source-free document understanding. To support future research of the community, the benchmark, models, and code will be publicly available at https://github.com/s3setewe/sfdla-DLAdapter.
Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages
Venue: arXiv cs.CL 2025
Year: 2025

A key consideration when training an LLM is whether the target language is more or less resourced, whether this is English compared to Welsh, or Python compared to Excel. Typical training data for programming languages consist of real program demonstrations coupled with human-written comments. Here we present novel approaches to the creation of such data for low resource programming languages. We generate fully-synthetic, textbook-quality demonstrations of common library functions in an example domain of Excel formulas, using a teacher model. We then finetune an underperforming student model, and show improvement on 2 question-answering datasets recast into the Excel domain. We show advantages of finetuning over standard, off-the-shelf RAG approaches, which can offer only modest improvement due to the unfamiliar target domain.
Transformer-based Ranking Approaches for Keyword Queries over Relational Databases
Venue: arXiv cs.DB 2025
Year: 2025

Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without requiring schema knowledge or query-language proficiency. Although numerous R-KwS methods have been proposed, most still focus on queries referring only to attribute values or primarily address performance enhancements, providing limited support for queries referencing schema elements. We previously introduced Lathe, a system that accommodates schema-based keyword queries and employs an eager CJN evaluation strategy to filter out spurious Candidate Joining Networks (CJNs). However, Lathe still faces challenges in accurately ranking CJNs when queries are ambiguous. In this work, we propose a new transformer-based ranking approach that provides a more context-aware evaluation of Query Matches (QMs) and CJNs. Our solution introduces a linearization process to convert relational structures into textual sequences suitable for transformer models. It also includes a data augmentation strategy aimed at handling diverse and ambiguous queries more effectively. Experimental results, comparing our transformer-based ranking to Lathe's original Bayesian-based method, show significant improvements in recall and R@k, demonstrating the effectiveness of our neural approach in delivering the most relevant query results.
AlphaSpace: Enabling Robotic Actions through Semantic Tokenization and Symbolic Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents AlphaSpace, a novel methodology designed to enhance the spatial reasoning capabilities of language models for robotic manipulation in 3D Cartesian space. AlphaSpace employs a hierarchical semantics-based tokenization strategy that encodes spatial information at both coarse and fine-grained levels. Our approach represents objects with their attributes, positions, and height information through structured tokens, enabling precise spatial reasoning without relying on traditional vision-based embeddings. This approach enables LLMs to accurately manipulate objects by positioning them at specific (x, y, z) coordinates. Experimental results suggest that AlphaSpace demonstrates promising potential for improving manipulation tasks, achieving a total accuracy of 66.67%, compared to 37.5% for GPT-4o and 29.17% for Claude 3.5 Sonnet. These results demonstrate the potential of structured spatial encoding for manipulation tasks and warrant further exploration.
Virtual Reality in Manufacturing Education: A Scoping Review Indicating State-of-the-Art, Benefits, and Challenges Across Domains, Levels, and Entities
Venue: arXiv cs.HC 2025
Year: 2025

To address the shortage of a skilled workforce in the U.S. manufacturing industry, immersive Virtual Reality (VR)-based training solutions hold promising potential. To effectively utilize VR to meet workforce demands, it is important to understand the role of VR in manufacturing education. Therefore, we conduct a scoping review in the field. As a first step, we used a 5W1H (What, Where, Who, When, Why, How) formula as a problem-solving approach to define a comprehensive taxonomy that can consider the role of VR from all relevant possibilities. Our taxonomy categorizes VR applications across three key aspects: (1) Domains, (2) Levels, and (3) Entities. Using a systematic literature search and analysis, we reviewed 108 research articles to find the current state, benefits, challenges, and future opportunities of VR in the field. It was found that VR has been explored in a variety of areas and provides numerous benefits to learners. Despite these benefits, its adoption in manufacturing education is limited. This review discusses the identified barriers and provides actionable insights to address them. These insights can enable the widespread usage of immersive technology to nurture and develop a workforce equipped with the skills required to excel in the evolving landscape of manufacturing.
Exploring the Integration of Key-Value Attention Into Pure and Hybrid Transformers for Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

While CNNs were long considered state of the art for image processing, the introduction of Transformer architectures has challenged this position. While achieving excellent results in image classification and segmentation, Transformers remain inherently reliant on large training datasets and remain computationally expensive. A newly introduced Transformer derivative named KV Transformer shows promising results in synthetic, NLP, and image classification tasks, while reducing complexity and memory usage. This is especially conducive to use cases where local inference is required, such as medical screening applications. We endeavoured to further evaluate the merit of KV Transformers on semantic segmentation tasks, specifically in the domain of medical imaging. By directly comparing traditional and KV variants of the same base architectures, we provide further insight into the practical tradeoffs of reduced model complexity. We observe a notable reduction in parameter count and multiply accumulate operations, while achieving similar performance from most of the KV variant models when directly compared to their QKV implementation.
Seeing Speech and Sound: Distinguishing and Locating Audios in Visual Scenes
Venue: arXiv cs.CV 2025
Year: 2025

We present a unified model capable of simultaneously grounding both spoken language and non-speech sounds within a visual scene, addressing key limitations in current audio-visual grounding models. Existing approaches are typically limited to handling either speech or non-speech sounds independently, or at best, together but sequentially without mixing. This limitation prevents them from capturing the complexity of real-world audio sources that are often mixed. Our approach introduces a 'mix-and-separate' framework with audio-visual alignment objectives that jointly learn correspondence and disentanglement using mixed audio. Through these objectives, our model learns to produce distinct embeddings for each audio type, enabling effective disentanglement and grounding across mixed audio sources. Additionally, we created a new dataset to evaluate simultaneous grounding of mixed audio sources, demonstrating that our model outperforms prior methods. Our approach also achieves comparable or better performance in standard segmentation and cross-modal retrieval tasks, highlighting the benefits of our mix-and-separate approach.