OpenRaiser/Intern-Atlas huggingface dataset:
LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection
Venue: arXiv cs.CV 2025
Year: 2025

Video Moment Retrieval and Highlight Detection aim to find corresponding content in the video based on a text query. Existing models usually first use contrastive learning methods to align video and text features, then fuse and extract multimodal information, and finally use a Transformer Decoder to decode multimodal information. However, existing methods face several issues: (1) Overlapping semantic information between different samples in the dataset hinders the model's multimodal aligning performance; (2) Existing models are not able to efficiently extract local features of the video; (3) The Transformer Decoder used by the existing model cannot adequately decode multimodal features. To address the above issues, we proposed the LD-DETR model for Video Moment Retrieval and Highlight Detection tasks. Specifically, we first distilled the similarity matrix into the identity matrix to mitigate the impact of overlapping semantic information. Then, we designed a method that enables convolutional layers to extract multimodal local features more efficiently. Finally, we fed the output of the Transformer Decoder back into itself to adequately decode multimodal information. We evaluated LD-DETR on four public benchmarks and conducted extensive experiments to demonstrate the superiority and effectiveness of our approach. Our model outperforms the State-Of-The-Art models on QVHighlight, Charades-STA and TACoS datasets. Our code is available at https://github.com/qingchen239/ld-detr.
Dynamic Trend Fusion Module for Traffic Flow Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Accurate traffic flow prediction is essential for applications like transport logistics but remains challenging due to complex spatio-temporal correlations and non-linear traffic patterns. Existing methods often model spatial and temporal dependencies separately, failing to effectively fuse them. To overcome this limitation, the Dynamic Spatial-Temporal Trend Transformer DST2former is proposed to capture spatio-temporal correlations through adaptive embedding and to fuse dynamic and static information for learning multi-view dynamic features of traffic networks. The approach employs the Dynamic Trend Representation Transformer (DTRformer) to generate dynamic trends using encoders for both temporal and spatial dimensions, fused via Cross Spatial-Temporal Attention. Predefined graphs are compressed into a representation graph to extract static attributes and reduce redundancy. Experiments on four real-world traffic datasets demonstrate that our framework achieves state-of-the-art performance.
Jailbreaking Large Language Models in Infinitely Many Ways
Venue: arXiv cs.LG 2025
Year: 2025

We discuss the ``Infinitely Many Paraphrases'' attacks (IMP), a category of jailbreaks that leverages the increasing capabilities of a model to handle paraphrases and encoded communications to bypass their defensive mechanisms. IMPs' viability pairs and grows with a model's capabilities to handle and bind the semantics of simple mappings between tokens and work extremely well in practice, posing a concrete threat to the users of the most powerful LLMs in commerce. We show how one can bypass the safeguards of the most powerful open- and closed-source LLMs and generate content that explicitly violates their safety policies. One can protect against IMPs by improving the guardrails and making them scale with the LLMs' capabilities. For two categories of attacks that are straightforward to implement, i.e., bijection and encoding, we discuss two defensive strategies, one in token and the other in embedding space. We conclude with some research questions we believe should be prioritised to enhance the defensive mechanisms of LLMs and our understanding of their safety.
Information Content and Entropy of Finite Patterns from a Combinatorial Perspective
Venue: arXiv cs.IT 2025
Year: 2025

A unified combinatorial definition of the information content and entropy of different types of patterns, compatible with the traditional concepts of information and entropy, going beyond the limitations of Shannon information interpretable for ergodic Markov processes. We compare the information content of various finite patterns and derive general properties of information quantity from these comparisons. Using these properties, we define normalized information estimation methods based on compression algorithms and Kolmogorov complexity. From a combinatorial point of view, we redefine the concept of entropy in a way that is asymptotically compatible with traditional entropy.
Statistical Design of Thermal Protection System Using Physics-Informed Machine learning
Venue: arXiv cs.CE 2025
Year: 2025

Estimating the material properties of thermal protection films is crucial for their effective design and application, particularly in high-temperature environments. This work presents a novel approach to determine the properties using uncertainty quantification simulations. We quantify uncertainty in the material properties for effective insulation by proposing a Bayesian distribution for them. Sampling from this distribution is performed using Monte Carlo simulations, which require repeatedly solving the predictive thermal model. To address the computational inefficiency of conventional numerical simulations, we develop a parametric Physics-Informed Neural Network (PINN) to solve the heat transfer problem. The proposed PINN significantly reduces computational time while maintaining accuracy, as verified against traditional numerical solutions. Additionally, we used the Sequential Monte Carlo (SMC) method to enable vectorized and parallel computations, further enhancing computational speedup. Our results demonstrate that integrating MCMC with PINN decreases computational time substantially compared to using standard numerical methods. Moreover, combining the SMC method with PINN yields multifold computational speedup, making this approach highly effective for the rapid and accurate estimation of material properties.
BAP v2: An Enhanced Task Framework for Instruction Following in Minecraft Dialogues
Venue: arXiv cs.CL 2025
Year: 2025

Developing interactive agents that can understand language, perceive their surroundings, and act within the physical world is a long-standing goal of AI research. The Minecraft Collaborative Building Task (MCBT) (Narayan-Chen, Jayannavar, and Hockenmaier 2019), a two-player game in which an Architect (A) instructs a Builder (B) to construct a target structure in a simulated 3D Blocks World environment, offers a rich platform to work towards this goal. In this work, we focus on the Builder Action Prediction (BAP) subtask: predicting B's actions in a multimodal game context (Jayannavar, Narayan-Chen, and Hockenmaier 2020) - a challenging testbed for grounded instruction following, with limited training data. We holistically re-examine this task and introduce BAP v2 to address key challenges in evaluation, training data, and modeling. Specifically, we define an enhanced evaluation benchmark, featuring a cleaner test set and fairer, more insightful metrics that also reveal spatial reasoning as the primary performance bottleneck. To address data scarcity and to teach models basic spatial skills, we generate different types of synthetic MCBT data. We observe that current, LLM-based SOTA models trained on the human BAP dialogues fail on these simpler, synthetic BAP ones, but show that training models on this synthetic data improves their performance across the board. We also introduce a new SOTA model, Llama-CRAFTS, which leverages richer input representations, and achieves an F1 score of 53.0 on the BAP v2 task and strong performance on the synthetic data. While this result marks a notable 6 points improvement over previous work, it also underscores the task's remaining difficulty, establishing BAP v2 as a fertile ground for future research, and providing a useful measure of the spatial capabilities of current text-only LLMs in such embodied tasks.
Practical and Ready-to-Use Methodology to Assess the re-identification Risk in Anonymized Datasets
Venue: arXiv cs.CR 2025
Year: 2025

To prove that a dataset is sufficiently anonymized, many privacy policies suggest that a re-identification risk assessment be performed, but do not provide a precise methodology for doing so, leaving the industry alone with the problem. This paper proposes a practical and ready-to-use methodology for re-identification risk assessment, the originality of which is manifold: (1) it is the first to follow well-known risk analysis methods (e.g. EBIOS) that have been used in the cybersecurity field for years, which consider not only the ability to perform an attack, but also the impact such an attack can have on an individual; (2) it is the first to qualify attributes and values of attributes with e.g. degree of exposure, as known real-world attacks mainly target certain types of attributes and not others.
Fake Advertisements Detection Using Automated Multimodal Learning: A Case Study for Vietnamese Real Estate Data
Venue: arXiv cs.LG 2025
Year: 2025

The popularity of e-commerce has given rise to fake advertisements that can expose users to financial and data risks while damaging the reputation of these e-commerce platforms. For these reasons, detecting and removing such fake advertisements are important for the success of e-commerce websites. In this paper, we propose FADAML, a novel end-to-end machine learning system to detect and filter out fake online advertisements. Our system combines techniques in multimodal machine learning and automated machine learning to achieve a high detection rate. As a case study, we apply FADAML to detect fake advertisements on popular Vietnamese real estate websites. Our experiments show that we can achieve 91.5% detection accuracy, which significantly outperforms three different state-of-the-art fake news detection systems.
Spectrum Analysis with the Prime Factor Algorithm on Embedded Systems
Venue: arXiv cs.AR 2025
Year: 2025

This paper details the purpose, difficulties, theory, implementation, and results of developing a Fast Fourier Transform (FFT) using the prime factor algorithm on an embedded system. Many applications analyze the frequency content of signals, which is referred to as spectral analysis. Some of these applications include communication systems, radar systems, control systems, seismology, speech, music, sonar, finance, image processing, and neural networks. For many real-time applications, the speed at which the spectral analysis is performed is crucial. In order to perform spectral analysis, a Fourier transform is employed. For embedded systems, where spectral analysis is done digitally, a discrete Fourier transform (DFT) is employed. The main goal for this project is to develop an FFT for a 36-point DFT on the Nuvoton Nu-LB-NUC140V2. In this case, the prime factor algorithm is utilized to compute a fast DFT.
JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Reliably generating structured outputs has become a critical capability for modern language model (LM) applications. Constrained decoding has emerged as the dominant technology across sectors for enforcing structured outputs during generation. Despite its growing adoption, little has been done with the systematic evaluation of the behaviors and performance of constrained decoding. Constrained decoding frameworks have standardized around JSON Schema as a structured data format, with most uses guaranteeing constraint compliance given a schema. However, there is poor understanding of the effectiveness of the methods in practice. We present an evaluation framework to assess constrained decoding approaches across three critical dimensions: efficiency in generating constraint-compliant outputs, coverage of diverse constraint types, and quality of the generated outputs. To facilitate this evaluation, we introduce JSONSchemaBench, a benchmark for constrained decoding comprising 10K real-world JSON schemas that encompass a wide range of constraints with varying complexity. We pair the benchmark with the existing official JSON Schema Test Suite and evaluate six state-of-the-art constrained decoding frameworks, including Guidance, Outlines, Llamacpp, XGrammar, OpenAI, and Gemini. Through extensive experiments, we gain insights into the capabilities and limitations of constrained decoding on structured generation with real-world JSON schemas. Our work provides actionable insights for improving constrained decoding frameworks and structured generation tasks, setting a new standard for evaluating constrained decoding and structured generation. We release JSONSchemaBench at https://github.com/guidance-ai/jsonschemabench
Diffusion-Based Imitation Learning for Social Pose Generation
Venue: arXiv cs.LG 2025
Year: 2025

Intelligent agents, such as robots and virtual agents, must understand the dynamics of complex social interactions to interact with humans. Effectively representing social dynamics is challenging because we require multi-modal, synchronized observations to understand a scene. We explore how using a single modality, the pose behavior, of multiple individuals in a social interaction can be used to generate nonverbal social cues for the facilitator of that interaction. The facilitator acts to make a social interaction proceed smoothly and is an essential role for intelligent agents to replicate in human-robot interactions. In this paper, we adapt an existing diffusion behavior cloning model to learn and replicate facilitator behaviors. Furthermore, we evaluate two representations of pose observations from a scene, one representation has pre-processing applied and one does not. The purpose of this paper is to introduce a new use for diffusion behavior cloning for pose generation in social interactions. The second is to understand the relationship between performance and computational load for generating social pose behavior using two different techniques for collecting scene observations. As such, we are essentially testing the effectiveness of two different types of conditioning for a diffusion model. We then evaluate the resulting generated behavior from each technique using quantitative measures such as mean per-joint position error (MPJPE), training time, and inference time. Additionally, we plot training and inference time against MPJPE to examine the trade-offs between efficiency and performance. Our results suggest that the further pre-processed data can successfully condition diffusion models to generate realistic social behavior, with reasonable trade-offs in accuracy and processing time.
RIS Deployment Optimization with Iterative Detection and Decoding in Multiuser Multiple-Antenna Systems
Venue: arXiv cs.IT 2025
Year: 2025

This work investigates a Reconfigurable Intelligent Surface (RIS)-assisted uplink system employing iterative detection and decoding (IDD) techniques. We analyze the impact of tuning system parameter tuning for several deployment configurations, including the number of users, access point (AP) antennas, and RIS elements on the IDD performance. Analytical results for both active and passive RIS in a single-input single-output (SISO) scenario demonstrate how deployment choices affect system performance. Numerical simulations confirm the robustness of the RIS-assisted IDD system to variations in these parameters, showing performance gains in certain configurations. Moreover, the findings indicate that the insights derived from SISO analysis extend to multiuser MIMO IDD systems.
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention
Venue: arXiv cs.LG 2025
Year: 2025

Electroencephalograph (EEG) is a crucial tool for studying brain activity. Recently, self-supervised learning methods leveraging large unlabeled datasets have emerged as a potential solution to the scarcity of widely available annotated EEG data. However, current methods suffer from at least one of the following limitations: i) sub-optimal EEG signal modeling, ii) model sizes in the hundreds of millions of trainable parameters, and iii) reliance on private datasets and/or inconsistent public benchmarks, hindering reproducibility. To address these challenges, we introduce a Compact Encoder for Representations of Brain Oscillations using alternating attention (CEReBrO), a new small EEG foundation model. Our tokenization scheme represents EEG signals at a per-channel patch granularity. We propose an alternating attention mechanism that jointly models intra-channel temporal dynamics and inter-channel spatial correlations, achieving 2x speed improvement with 6x less memory required compared to standard self-attention. We present several model sizes ranging from 3.6 million to 85 million parameters. Pre-trained on over 20,000 hours of publicly available scalp EEG recordings with diverse channel configurations, our models set new benchmarks in emotion detection and seizure detection tasks, with competitive performance in anomaly classification and gait prediction. This validates our models' effectiveness and efficiency.
Automated Selfish Mining Analysis for DAG-Based PoW Consensus Protocols
Venue: arXiv cs.CR 2025
Year: 2025

Selfish mining is strategic rule-breaking to maximize rewards in proof-of-work protocols. Markov Decision Processes (MDPs) are the preferred tool for finding optimal strategies in Bitcoin and similar linear chain protocols. Protocols increasingly adopt DAG-based chain structures, for which MDP analysis is more involved. To date, researchers have tailored specific MDPs for each protocol. Protocol design suffers long feedback loops, as each protocol change implies manual work on the MDP. To overcome this, we propose a generic attack model that covers a wide range of protocols, including Ethereum Proof-of-Work, GhostDAG, and Parallel Proof-of-Work. Our approach is modular: we specify each protocol as a concise program, and our tooling then derives and solves the selfish mining MDP automatically.
AutoDeduct: A Tool for Automated Deductive Verification of C Code
Venue: arXiv cs.LO 2025
Year: 2025

Deductive verification has become a mature paradigm for the verification of industrial software. Applying deductive verification, however, requires that every function in the code base is annotated with a function contract specifying its behaviour. This introduces a large overhead of manual work. To address this challenge, we introduce the AutoDeduct toolchain, built on top of the Frama-C framework. It implements a combination of techniques to automatically infer contracts for functions in C programs, in the syntax of ACSL, the specification language of Frama-C. Contract inference in AutoDecuct is implemented as two plugins for Frama-C, each inferring different types of annotations. We assume that programs have an entry-point function already equipped with a contract, which is used in conjunction with the program source code to infer contracts for the helper functions, so that the entry-point contract can be verified. The current release of AutoDeduct is the first public prototype, which we evaluate on an example adapted from industrial software.
A bibliometric analysis of Canadian LIS scholars and practitioners' research contributions
Venue: arXiv cs.DL 2025
Year: 2025

Canada's research productivity in Library and Information Science (LIS) is significant: studies have found that Canada ranks third globally in terms of output. As the LIS field continues to grow, the pace of output accelerates, and the scope of this work expands. The recently launched Canadian Publications in Library and Information Science Database compiles all Canadian scientific publications, including those authored by faculty members and academic librarians. This database offers the advantage of encompassing articles and librarian publications that may not be typically included in traditional bibliometric surveys, such as those conducted using databases like Web of Science, Scopus, and Library and Information Science Abstracts (LISA). Using this data, this study maps the scholarly contributions of Canadian LIS scholars and academic librarians to the field of LIS and examines whether Canadian LIS research is characterized by silos. This paper examines the similarities and differences in research output, impact, topics, and publication venues between academic librarians and scholars in Canada, as well as the extent to which academics and practitioners engage in research collaborations or reference each other's work. We find that while there is some degree of overlap in research topics and publication venues between LIS academics and academic librarians, the two groups appear to act as distinct research communities with distinct topical foci and publishing habits. The two groups also do not appear to engage with each other strongly, either through collaboration or citing each other's work.
A Remote Sensing Image Change Detection Method Integrating Layer Exchange and Channel-Spatial Differences
Venue: arXiv cs.CV 2025
Year: 2025

Change detection in remote sensing imagery is a critical technique for Earth observation, primarily focusing on pixel-level segmentation of change regions between bi-temporal images. The essence of pixel-level change detection lies in determining whether corresponding pixels in bi-temporal images have changed. In deep learning, the spatial and channel dimensions of feature maps represent different information from the original images. In this study, we found that in change detection tasks, difference information can be computed not only from the spatial dimension of bi-temporal features but also from the channel dimension. Therefore, we designed the Channel-Spatial Difference Weighting (CSDW) module as an aggregation-distribution mechanism for bi-temporal features in change detection. This module enhances the sensitivity of the change detection model to difference features. Additionally, bi-temporal images share the same geographic location and exhibit strong inter-image correlations. To construct the correlation between bi-temporal images, we designed a decoding structure based on the Layer-Exchange (LE) method to enhance the interaction of bi-temporal features. Comprehensive experiments on the CLCD, PX-CLCD, LEVIR-CD, and S2Looking datasets demonstrate that the proposed LENet model significantly improves change detection performance. The code and pre-trained models will be available at: https://github.com/dyzy41/lenet.