OpenRaiser/Intern-Atlas huggingface dataset:
SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection
Venue: arXiv cs.CL 2025
Year: 2025

In Semantic Change Detection (SCD), it is a common problem to obtain embeddings that are both interpretable and high-performing. However, improving interpretability often leads to a loss in the SCD performance, and vice versa. To address this problem, we propose SCDTour, a method that orders and merges interpretable axes to alleviate the performance degradation of SCD. SCDTour considers both (a) semantic similarity between axes in the embedding space, as well as (b) the degree to which each axis contributes to semantic change. Experimental results show that SCDTour preserves performance in semantic change detection while maintaining high interpretability. Moreover, agglomerating the sorted axes produces a more refined set of word senses, which achieves comparable or improved performance against the original full-dimensional embeddings in the SCD task. These findings demonstrate that SCDTour effectively balances interpretability and SCD performance, enabling meaningful interpretation of semantic shifts through a small number of refined axes. Source code is available at https://github.com/LivNLP/svp-tour .
Data-Driven Analysis of Text-Conditioned AI-Generated Music: A Case Study with Suno and Udio
Venue: arXiv cs.IR 2025
Year: 2025

Online AI platforms for creating music from text prompts (AI music), such as Suno and Udio, are now being used by hundreds of thousands of users. Some AI music is appearing in advertising, and even charting, in multiple countries. How are these platforms being used? What subjects are inspiring their users? This article answers these questions for Suno and Udio using a large collection of songs generated by users of these platforms from May to October 2024. Using a combination of state-of-the-art text embedding models, dimensionality reduction and clustering methods, we analyze the prompts, tags and lyrics, and automatically annotate and display the processed data in interactive plots. Our results reveal prominent themes in lyrics, language preference, prompting strategies, as well as peculiar attempts at steering models through the use of metatags. To promote the musicological study of the developing cultural practice of AI-generated music we share our code and resources.
A Practical Adversarial Attack against Sequence-based Deep Learning Malware Classifiers
Venue: arXiv cs.CR 2025
Year: 2025

Sequence-based deep learning models (e.g., RNNs), can detect malware by analyzing its behavioral sequences. Meanwhile, these models are susceptible to adversarial attacks. Attackers can create adversarial samples that alter the sequence characteristics of behavior sequences to deceive malware classifiers. The existing methods for generating adversarial samples typically involve deleting or replacing crucial behaviors in the original data sequences, or inserting benign behaviors that may violate the behavior constraints. However, these methods that directly manipulate sequences make adversarial samples difficult to implement or apply in practice. In this paper, we propose an adversarial attack approach based on Deep Q-Network and a heuristic backtracking search strategy, which can generate perturbation sequences that satisfy practical conditions for successful attacks. Subsequently, we utilize a novel transformation approach that maps modifications back to the source code, thereby avoiding the need to directly modify the behavior log sequences. We conduct an evaluation of our approach, and the results confirm its effectiveness in generating adversarial samples from real-world malware behavior sequences, which have a high success rate in evading anomaly detection models. Furthermore, our approach is practical and can generate adversarial samples while maintaining the functionality of the modified software.
Dr.V: A Hierarchical Perception-Temporal-Cognition Framework to Diagnose Video Hallucination by Fine-grained Spatial-Temporal Grounding
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in large video models (LVMs) have significantly enhance video understanding. However, these models continue to suffer from hallucinations, producing content that conflicts with input videos. To address this issue, we propose Dr.V, a hierarchical framework covering perceptive, temporal, and cognitive levels to diagnose video hallucination by fine-grained spatial-temporal grounding. Dr.V comprises of two key components: a benchmark dataset Dr.V-Bench and a satellite video agent Dr.V-Agent. Dr.V-Bench includes 10k instances drawn from 4,974 videos spanning diverse tasks, each enriched with detailed spatial-temporal annotation. Dr.V-Agent detects hallucinations in LVMs by systematically applying fine-grained spatial-temporal grounding at the perceptive and temporal levels, followed by cognitive level reasoning. This step-by-step pipeline mirrors human-like video comprehension and effectively identifies hallucinations. Extensive experiments demonstrate that Dr.V-Agent is effective in diagnosing hallucination while enhancing interpretability and reliability, offering a practical blueprint for robust video understanding in real-world scenarios. All our data and code are available at https://github.com/Eurekaleo/Dr.V.
Multi-animal tracking in Transition: Comparative Insights into Established and Emerging Methods
Venue: arXiv cs.CV 2025
Year: 2025

Precision livestock farming requires advanced monitoring tools to meet the increasing management needs of the industry. Computer vision systems capable of long-term multi-animal tracking (MAT) are essential for continuous behavioral monitoring in livestock production. MAT, a specialized subset of multi-object tracking (MOT), shares many challenges with MOT, but also faces domain-specific issues including frequent animal occlusion, highly similar appearances among animals, erratic motion patterns, and a wide range of behavior types. While some existing MAT tools are user-friendly and widely adopted, they often underperform compared to state-of-the-art MOT methods, which can result in inaccurate downstream tasks such as behavior analysis, health state estimation, and related applications. In this study, we benchmarked both MAT and MOT approaches for long-term tracking of pigs. We compared tools such as DeepLabCut and idTracker with MOT-based methods including ByteTrack, DeepSORT, cross-input consistency, and newer approaches like Track-Anything and PromptTrack. All methods were evaluated on a 10-minute pig tracking dataset. Our results demonstrate that, overall, MOT approaches outperform traditional MAT tools, even for long-term tracking scenarios. These findings highlight the potential of recent MOT techniques to enhance the accuracy and reliability of automated livestock tracking.
Numerical analysis of fluid estimation for source terms in neutral particles simulation
Venue: arXiv cs.CE 2025
Year: 2025

In plasma edge simulations, kinetic Monte Carlo (MC) is often used to simulate neutral particles and estimate source terms. For large-sized reactors, like ITER and DEMO, high particle collision rates lead to a substantial computational cost for such schemes. To address this challenge, an asymptotic-preserving kinetic-diffusion Monte Carlo (KDMC) simulation method and a corresponding fluid estimation technique have been proposed in the literature. In this work, we perform numerical analysis on the convergence of KDMC with the fluid estimation. To do so, we compare the accuracy of the analyzed algorithm with the accuracy of an approximate fluid method using the kinetic MC method as a reference. In a one-dimensional test case, KDMC with the fluid estimation achieves at least one order of magnitude lower errors than the fluid method for both high- and low-collisional regimes. Moreover, KDMC with the fluid estimation outperforms the kinetic MC method with a clear speed-up. Overall, our analysis confirms the effectiveness of the discussed algorithm.
Integrating Prior Observations for Incremental 3D Scene Graph Prediction
Venue: arXiv cs.CV 2025
Year: 2025

3D semantic scene graphs (3DSSG) provide compact structured representations of environments by explicitly modeling objects, attributes, and relationships. While 3DSSGs have shown promise in robotics and embodied AI, many existing methods rely mainly on sensor data, not integrating further information from semantically rich environments. Additionally, most methods assume access to complete scene reconstructions, limiting their applicability in real-world, incremental settings. This paper introduces a novel heterogeneous graph model for incremental 3DSSG prediction that integrates additional, multi-modal information, such as prior observations, directly into the message-passing process. Utilizing multiple layers, the model flexibly incorporates global and local scene representations without requiring specialized modules or full scene reconstructions. We evaluate our approach on the 3DSSG dataset, showing that GNNs enriched with multi-modal information such as semantic embeddings (e.g., CLIP) and prior observations offer a scalable and generalizable solution for complex, real-world environments. The full source code of the presented architecture will be made available at https://github.com/m4renz/incremental-scene-graph-prediction.
Expressive Power of One-Shot Control Operators and Coroutines
Venue: arXiv cs.PL 2025
Year: 2025

Control operators, such as exceptions and effect handlers, provide a means of representing computational effects in programs abstractly and modularly. While most theoretical studies have focused on multi-shot control operators, one-shot control operators -- which restrict the use of captured continuations to at most once -- are gaining attention for their balance between expressiveness and efficiency. This study aims to fill the gap. We present a mathematically rigorous comparison of the expressive power among one-shot control operators, including effect handlers, delimited continuations, and even asymmetric coroutines. Following previous studies on multi-shot control operators, we adopt Felleisen's macro-expressiveness as our measure of expressiveness. We verify the folklore that one-shot effect handlers and one-shot delimited-control operators can be macro-expressed by asymmetric coroutines, but not vice versa. We explain why a previous informal argument fails, and how to revise it to make a valid macro-translation.
VH-Diffuser: Variable Horizon Diffusion Planner for Time-Aware Goal-Conditioned Trajectory Planning
Venue: arXiv cs.RO 2025
Year: 2025

Diffusion-based planners have gained significant recent attention for their robustness and performance in long-horizon tasks. However, most existing planners rely on a fixed, pre-specified horizon during both training and inference. This rigidity often produces length-mismatch (trajectories that are too short or too long) and brittle performance across instances with varying geometric or dynamical difficulty. In this paper, we introduce the Variable Horizon Diffuser (VHD) framework, which treats the horizon as a learned variable rather than a fixed hyperparameter. Given a start-goal pair, we first predict an instance-specific horizon using a learned Length Predictor model, which guides a Diffusion Planner to generate a trajectory of the desired length. Our design maintains compatibility with existing diffusion planners by controlling trajectory length through initial noise shaping and training on randomly cropped sub-trajectories, without requiring architectural changes. Empirically, VHD improves success rates and path efficiency in maze-navigation and robot-arm control benchmarks, showing greater robustness to horizon mismatch and unseen lengths, while keeping training simple and offline-only.
Neuromorphic Intelligence
Venue: arXiv cs.AI 2025
Year: 2025

Neuromorphic computing seeks to replicate the remarkable efficiency, flexibility, and adaptability of the human brain in artificial systems. Unlike conventional digital approaches, which suffer from the Von Neumann bottleneck and depend on massive computational and energy resources, neuromorphic systems exploit brain-inspired principles of computation to achieve orders of magnitude greater energy efficiency. By drawing on insights from a wide range of disciplines -- including artificial intelligence, physics, chemistry, biology, neuroscience, cognitive science and materials science -- neuromorphic computing promises to deliver intelligent systems that are sustainable, transparent, and widely accessible. A central challenge, however, is to identify a unifying theoretical framework capable of bridging these diverse disciplines. We argue that dynamical systems theory provides such a foundation. Rooted in differential calculus, it offers a principled language for modeling inference, learning, and control in both natural and artificial substrates. Within this framework, noise can be harnessed as a resource for learning, while differential genetic programming enables the discovery of dynamical systems that implement adaptive behaviors. Embracing this perspective paves the way toward emergent neuromorphic intelligence, where intelligent behavior arises from the dynamics of physical substrates, advancing both the science and sustainability of AI.
An ETH-Tight FPT Algorithm for Rejection-Proof Set Packing with Applications to Kidney Exchange
Venue: arXiv cs.DS 2025
Year: 2025

We study the parameterized complexity of a recently introduced multi-agent variant of the Kidney Exchange problem. Given a directed graph $G$ and integers $d$ and $k$, the standard problem asks whether $G$ contains a packing of vertex-disjoint cycles, each of length $\leq d$, covering at least $k$ vertices in total. In the multi-agent setting we consider, the vertex set is partitioned over several agents who reject a cycle packing as solution if it can be modified into an alternative packing that covers more of their own vertices. A cycle packing is called rejection-proof if no agent rejects it and the problem asks whether such a packing exists that covers at least $k$ vertices. We exploit the sunflower lemma on a set packing formulation of the problem to give a kernel for this $Σ_2^P$-complete problem that is polynomial in $k$ for all constant values of $d$. We also provide a $2^{\mathcal{O}(k \log k)} + n^{\mathcal{O}(1)}$ algorithm based on it and show that this FPT algorithm is asymptotically optimal under the ETH. Further, we generalize the problem by including an additional positive integer $c$ in the input that naturally captures how much agents can modify a given cycle packing to reject it. For every constant $c$, the resulting problem simplifies from being $Σ_2^P$-complete to NP-complete. The super-exponential lower bound already holds for $c=2$, though. We present an ad-hoc single-exponential algorithm for $c = 1$. These results reveal an interesting discrepancy between the classical and parameterized complexity of the problem and give a good view of what makes it hard.
Examining the Relationship between Scientific Publishing Activity and Hype-Driven Financial Bubbles: A Comparison of the Dot-Com and AI Eras
Venue: arXiv cs.LG 2025
Year: 2025

Financial bubbles often arrive without much warning, but create long-lasting economic effects. For example, during the dot-com bubble, innovative technologies created market disruptions through excitement for a promised bright future. Such technologies originated from research where scientists had developed them for years prior to their entry into the markets. That raises a question on the possibility of analyzing scientific publishing data (e.g. citation networks) leading up to a bubble for signals that may forecast the rise and fall of similar future bubbles. To that end, we utilized temporal SNAs to detect possible relationships between the publication citation networks of scientists and financial market data during two modern eras of rapidly shifting technology: 1) dot-com era from 1994 to 2001 and 2) AI era from 2017 to 2024. Results showed that the patterns from the dot-com era (which did end in a bubble) did not definitively predict the rise and fall of an AI bubble. While yearly citation networks reflected possible changes in publishing behavior of scientists between the two eras, there was a subset of AI era scientists whose publication influence patterns mirrored those during the dot-com era. Upon further analysis using multiple analysis techniques (LSTM, KNN, AR X/GARCH), the data seems to suggest two possibilities for the AI era: unprecedented form of financial bubble unseen or that no bubble exists. In conclusion, our findings imply that the patterns present in the dot-com era do not effectively translate in such a manner to apply them to the AI market.
Query-Focused Extractive Summarization for Sentiment Explanation
Venue: arXiv cs.CL 2025
Year: 2025

Constructive analysis of feedback from clients often requires determining the cause of their sentiment from a substantial amount of text documents. To assist and improve the productivity of such endeavors, we leverage the task of Query-Focused Summarization (QFS). Models of this task are often impeded by the linguistic dissonance between the query and the source documents. We propose and substantiate a multi-bias framework to help bridge this gap at a domain-agnostic, generic level; we then formulate specialized approaches for the problem of sentiment explanation through sentiment-based biases and query expansion. We achieve experimental results outperforming baseline models on a real-world proprietary sentiment-aware QFS dataset.
Learning non-Markovian Dynamical Systems with Signature-based Encoders
Venue: arXiv cs.LG 2025
Year: 2025

Neural ordinary differential equations offer an effective framework for modeling dynamical systems by learning a continuous-time vector field. However, they rely on the Markovian assumption - that future states depend only on the current state - which is often untrue in real-world scenarios where the dynamics may depend on the history of past states. This limitation becomes especially evident in settings involving the continuous control of complex systems with delays and memory effects. To capture historical dependencies, existing approaches often rely on recurrent neural network (RNN)-based encoders, which are inherently discrete and struggle with continuous modeling. In addition, they may exhibit poor training behavior. In this work, we investigate the use of the signature transform as an encoder for learning non-Markovian dynamics in a continuous-time setting. The signature transform offers a continuous-time alternative with strong theoretical foundations and proven efficiency in summarizing multidimensional information in time. We integrate a signature-based encoding scheme into encoder-decoder dynamics models and demonstrate that it outperforms RNN-based alternatives in test performance on synthetic benchmarks.
Exploring Gaze Dynamics in VR Film Education: Gender, Avatar, and the Shift Between Male and Female Perspectives
Venue: arXiv cs.HC 2025
Year: 2025

In virtual reality (VR) education, especially in creative fields like film production, avatar design and narrative style extend beyond appearance and aesthetics. This study explores how the interaction between avatar gender, the dominant narrative actor's gender, and the learner's gender influences film production learning in VR, focusing on gaze dynamics and gender perspectives. Using a 2*2*2 experimental design, 48 participants operated avatars of different genders and interacted with male or female-dominant narratives. The results show that the consistency between the avatar and gender affects presence, and learners' control over the avatar is also influenced by gender matching. Learners using avatars of the opposite gender reported stronger control, suggesting gender incongruity prompted more focus on the avatar. Additionally, female participants with female avatars were more likely to adopt a "female gaze," favoring soft lighting and emotional shots, while male participants with male avatars were more likely to adopt a "male gaze," choosing dynamic shots and high contrast. When male participants used female avatars, they favored "female gaze," while female participants with male avatars focused on "male gaze". These findings advance our understanding of how avatar design and narrative style in VR-based education influence creativity and the cultivation of gender perspectives, and they offer insights for developing more inclusive and diverse VR teaching tools going forward.
A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset
Venue: arXiv cs.CV 2025
Year: 2025

Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. However, traditional manual observation methods are time-consuming, subjective, and limited in scalability. We present a modular pipeline that leverages open-sourced state-of-the-art computer vision techniques to automate animal behavior analysis in a group housing environment. Our approach combines state-of-the-art models for zero-shot object detection, motion-aware tracking and segmentation, and advanced feature extraction using vision transformers for robust behavior recognition. The pipeline addresses challenges including animal occlusions and group housing scenarios as demonstrated in indoor pig monitoring. We validated our system on the Edinburgh Pig Behavior Video Dataset for multiple behavioral tasks. Our temporal model achieved 94.2% overall accuracy, representing a 21.2 percentage point improvement over existing methods. The pipeline demonstrated robust tracking capabilities with 93.3% identity preservation score and 89.3% object detection precision. The modular design suggests potential for adaptation to other contexts, though further validation across species would be required. The open-source implementation provides a scalable solution for behavior monitoring, contributing to precision pig farming and welfare assessment through automated, objective, and continuous analysis.
AvatarSync: Rethinking Talking-Head Animation through Phoneme-Guided Autoregressive Perspective
Venue: arXiv cs.CV 2025
Year: 2025

Talking-head animation focuses on generating realistic facial videos from audio input. Following Generative Adversarial Networks (GANs), diffusion models have become the mainstream, owing to their robust generative capacities. However, inherent limitations of the diffusion process often lead to inter-frame flicker and slow inference, restricting their practical deployment. To address this, we introduce AvatarSync, an autoregressive framework on phoneme representations that generates realistic and controllable talking-head animations from a single reference image, driven directly by text or audio input. To mitigate flicker and ensure continuity, AvatarSync leverages an autoregressive pipeline that enhances temporal modeling. In addition, to ensure controllability, we introduce phonemes, which are the basic units of speech sounds, and construct a many-to-one mapping from text/audio to phonemes, enabling precise phoneme-to-visual alignment. Additionally, to further accelerate inference, we adopt a two-stage generation strategy that decouples semantic modeling from visual dynamics, and incorporate a customized Phoneme-Frame Causal Attention Mask to support multi-step parallel acceleration. Extensive experiments conducted on both Chinese (CMLR) and English (HDTF) datasets demonstrate that AvatarSync outperforms existing talking-head animation methods in visual fidelity, temporal consistency, and computational efficiency, providing a scalable and controllable solution.