OpenRaiser/Intern-Atlas huggingface dataset:
Detecting and Pruning Prominent but Detrimental Neurons in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) often develop learned mechanisms specialized to specific datasets, such as reliance on domain-specific correlations, which yield high-confidence predictions without generalizable reasoning. While beneficial in one setting, these dataset-specific mechanisms typically degrade performance when models encounter novel tasks or distributions. In this work, we introduce a fine-tuning approach designed to enhance generalization by identifying and pruning neurons associated with dataset-specific mechanisms in transformer-based LLMs. Our method employs Integrated Gradients to quantify each neuron's influence on high-confidence predictions, pinpointing those that disproportionately contribute to dataset-specific performance without supporting robust, transferable reasoning. Selectively pruning these neurons compels the model to depend on generalizable representations. Evaluated across multiple-choice benchmarks, our pruning-based fine-tuning significantly enhances performance, surpassing prior (non-pruning) adaptation methods.
User-to-PC Authentication Through Confirmation on Mobile Devices: On Usability and Performance
Venue: arXiv cs.HC 2025
Year: 2025

Protecting personal computers (PCs) from unauthorized access typically relies on password authentication, which is know to suffer from cognitive burden and weak credentials. As many users nowadays carry mobile devices with advanced security features throughout their day, there is an opportunity to leverage these devices to improve authentication to PCs. In this paper we utilize a token-based passwordless approach where users authenticate to their PC by confirming the authentication request on their smartphones or smartwatches. Upon a request to login to the PC, or to evaluate privileges, the PC issues an authentication request that users receive on their mobile devices, where users can confirm or deny the request. We evaluate button tap and biometric fingerprint verification as confirmation variants, and compare their authentication duration, success rate, and usability to traditional password-based authentication in a user study with 30 participants and a total of 1,200 authentication attempts. Smartwatch-based authentication outperformed password-based authentication and smartphone-based variants in authentication duration, while showing comparable success rates. Participants rated smartwatch-based authentication highest in usability, followed by password-based authentication and smartphone-based authentication.
THYME: Temporal Hierarchical-Cyclic Interactivity Modeling for Video Scene Graphs in Aerial Footage
Venue: arXiv cs.CV 2025
Year: 2025

The rapid proliferation of video in applications such as autonomous driving, surveillance, and sports analytics necessitates robust methods for dynamic scene understanding. Despite advances in static scene graph generation and early attempts at video scene graph generation, previous methods often suffer from fragmented representations, failing to capture fine-grained spatial details and long-range temporal dependencies simultaneously. To address these limitations, we introduce the Temporal Hierarchical Cyclic Scene Graph (THYME) approach, which synergistically integrates hierarchical feature aggregation with cyclic temporal refinement to address these limitations. In particular, THYME effectively models multi-scale spatial context and enforces temporal consistency across frames, yielding more accurate and coherent scene graphs. In addition, we present AeroEye-v1.0, a novel aerial video dataset enriched with five types of interactivity that overcome the constraints of existing datasets and provide a comprehensive benchmark for dynamic scene graph generation. Empirically, extensive experiments on ASPIRe and AeroEye-v1.0 demonstrate that the proposed THYME approach outperforms state-of-the-art methods, offering improved scene understanding in ground-view and aerial scenarios.
Stereo-based 3D Anomaly Object Detection for Autonomous Driving: A New Dataset and Baseline
Venue: arXiv cs.CV 2025
Year: 2025

3D detection technology is widely used in the field of autonomous driving, with its application scenarios gradually expanding from enclosed highways to open conventional roads. For rare anomaly categories that appear on the road, 3D detection models trained on closed sets often misdetect or fail to detect anomaly objects. To address this risk, it is necessary to enhance the generalization ability of 3D detection models for targets of arbitrary shapes and to possess the capability to filter out anomalies. The generalization of 3D detection is limited by two factors: the coupled training of 2D and 3D, and the insufficient diversity in the scale distribution of training samples. This paper proposes a Stereo-based 3D Anomaly object Detection (S3AD) algorithm, which decouples the training strategy of 3D and 2D to release the generalization ability for arbitrary 3D foreground detection, and proposes an anomaly scoring algorithm based on foreground confidence prediction, achieving target-level anomaly scoring. In order to further verify and enhance the generalization of anomaly detection, we use a 3D rendering method to synthesize two augmented reality binocular stereo 3D detection datasets which named KITTI-AR. KITTI-AR extends upon KITTI by adding 97 new categories, totaling 6k pairs of stereo images. The KITTI-AR-ExD subset includes 39 common categories as extra training data to address the sparse sample distribution issue. Additionally, 58 rare categories form the KITTI-AR-OoD subset, which are not used in training to simulate zero-shot scenarios in real-world settings, solely for evaluating 3D anomaly detection. Finally, the performance of the algorithm and the dataset is verified in the experiments. (Code and dataset can be obtained at https://github.com/shiyi-mu/S3AD-Code).
Online Long-term Point Tracking in the Foundation Model Era
Venue: arXiv cs.CV 2025
Year: 2025

Point tracking aims to identify the same physical point across video frames and serves as a geometry-aware representation of motion. This representation supports a wide range of applications, from robotics to augmented reality, by enabling accurate modeling of dynamic environments. Most existing long-term tracking approaches operate in an offline setting, where future frames are available to refine predictions and recover from occlusions. However, real-world scenarios often demand online predictions: the model must operate causally, using only current and past frames. This constraint is critical in streaming video and embodied AI, where decisions must be made immediately based on past observations. Under such constraints, viewpoint invariance becomes essential. Visual foundation models, trained on diverse large-scale datasets, offer the potential for robust geometric representations. While they lack temporal reasoning on their own, they can be integrated into tracking pipelines to enrich spatial features. In this thesis, we address the problem of long-term point tracking in an online setting, where frames are processed sequentially without access to future information or sliding windows. We begin by evaluating the suitability of visual foundation models for this task and find that they can serve as useful initializations and be integrated into tracking pipelines. However, to enable long-term tracking in an online setting, a dedicated design is still required. In particular, maintaining coherence over time in this causal regime requires memory to propagate appearance and context across frames. To address this, we introduce Track-On, a transformer-based model that treats each tracked point as a query and processes video frames one at a time. Track-On sets a new state of the art across seven public benchmarks, demonstrating the feasibility of long-term tracking without future access.
Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring
Venue: arXiv cs.CY 2025
Year: 2025

AI-driven recruitment systems, while promising efficiency and objectivity, often perpetuate systemic inequalities by encoding cultural and social capital disparities into algorithmic decision making. This article develops and defends a novel theory of secondary bounded rationality, arguing that AI systems, despite their computational power, inherit and amplify human cognitive and structural biases through technical and sociopolitical constraints. Analyzing multimodal recruitment frameworks, we demonstrate how algorithmic processes transform historical inequalities, such as elite credential privileging and network homophily, into ostensibly meritocratic outcomes. Using Bourdieusian capital theory and Simon's bounded rationality, we reveal a recursive cycle where AI entrenches exclusion by optimizing for legible yet biased proxies of competence. We propose mitigation strategies, including counterfactual fairness testing, capital-aware auditing, and regulatory interventions, to disrupt this self-reinforcing inequality.
On Lattice Isomorphism Problems for Lattices from LCD Codes over Finite Rings
Venue: arXiv cs.IT 2025
Year: 2025

These days, post-quantum cryptography based on the lattice isomorphism problem has been proposed. Ducas-Gibbons introduced the hull attack, which solves the lattice isomorphism problem for lattices obtained by Construction A from an LCD code over a finite field. Using this attack, they showed that the lattice isomorphism problem for such lattices can be reduced to the lattice isomorphism problem with the trivial lattice $\mathbb{Z}^n$ and the graph isomorphism problem. While the previous work by Ducas-Gibbons only considered lattices constructed by a code over a \textit{finite field}, this paper considers lattices constructed by a code over a \textit{finite ring} $\mathbb{Z}/k\mathbb{Z}$, which is a more general case. In particular, when $k$ is odd, an odd prime power, or not divisible by $4$, we show that the lattice isomorphism problem can be reduced to the lattice isomorphism problem for $\mathbb{Z}^n$ and the graph isomorphism problem.
Psychology-Driven Enhancement of Humour Translation
Venue: arXiv cs.CL 2025
Year: 2025

Humour translation plays a vital role as a bridge between different cultures, fostering understanding and communication. Although most existing Large Language Models (LLMs) are capable of general translation tasks, these models still struggle with humour translation, which is especially reflected through linguistic interference and lacking humour in translated text. In this paper, we propose a psychology-inspired Humour Decomposition Mechanism (HDM) that utilises Chain-of-Thought (CoT) to imitate the ability of the human thought process, stimulating LLMs to optimise the readability of translated humorous texts. Moreover, we integrate humour theory in HDM to further enhance the humorous elements in the translated text. Our automatic evaluation experiments on open-source humour datasets demonstrate that our method significantly improves the quality of humour translation, yielding average gains of 7.75\% in humour, 2.81\% in fluency, and 6.13\% in coherence of the generated text.
Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal large language models (MLLMs) hold considerable promise for applications in healthcare. However, their deployment in safety-critical settings is hindered by two key limitations: (i) sensitivity to prompt design, and (ii) a tendency to generate incorrect responses with high confidence. As clinicians may rely on a model's stated confidence to gauge the reliability of its predictions, it is especially important that when a model expresses high confidence, it is also highly accurate. We introduce Prompt4Trust, the first reinforcement learning (RL) framework for prompt augmentation targeting confidence calibration in MLLMs. A lightweight LLM is trained to produce context-aware auxiliary prompts that guide a downstream task MLLM to generate responses in which the expressed confidence more accurately reflects predictive accuracy. Unlike conventional calibration techniques, Prompt4Trust specifically prioritizes aspects of calibration most critical for safe and trustworthy clinical decision-making. Beyond improvements driven by this clinically motivated calibration objective, our proposed method also improves task accuracy, achieving state-of-the-art medical visual question answering (VQA) performance on the PMC-VQA benchmark, which is composed of multiple-choice questions spanning diverse medical imaging modalities. Moreover, our framework trained with a small downstream task MLLM showed promising zero-shot generalization to larger MLLMs in our experiments, suggesting the potential for scalable calibration without the associated computational costs. This work demonstrates the potential of automated yet human-aligned prompt engineering for improving the the trustworthiness of MLLMs in safety critical settings. Our codebase can be found at https://github.com/xingbpshen/prompt4trust.
Generative Latent Kernel Modeling for Blind Motion Deblurring
Venue: arXiv cs.CV 2025
Year: 2025

Deep prior-based approaches have demonstrated remarkable success in blind motion deblurring (BMD) recently. These methods, however, are often limited by the high non-convexity of the underlying optimization process in BMD, which leads to extreme sensitivity to the initial blur kernel. To address this issue, we propose a novel framework for BMD that leverages a deep generative model to encode the kernel prior and induce a better initialization for the blur kernel. Specifically, we pre-train a kernel generator based on a generative adversarial network (GAN) to aptly characterize the kernel's prior distribution, as well as a kernel initializer to provide a well-informed and high-quality starting point for kernel estimation. By combining these two components, we constrain the BMD solution within a compact latent kernel manifold, thus alleviating the aforementioned sensitivity for kernel initialization. Notably, the kernel generator and initializer are designed to be easily integrated with existing BMD methods in a plug-and-play manner, enhancing their overall performance. Furthermore, we extend our approach to tackle blind non-uniform motion deblurring without the need for additional priors, achieving state-of-the-art performance on challenging benchmark datasets. The source code is available at https://github.com/dch0319/GLKM-Deblur.
Fast3D: Accelerating 3D Multi-modal Large Language Models for Efficient 3D Scene Understanding
Venue: arXiv cs.CV 2025
Year: 2025

While 3D Multi-modal Large Language Models (MLLMs) demonstrate remarkable scene understanding capabilities, their practical deployment faces critical challenges due to computational inefficiency. The key bottleneck stems from processing excessive object-centric visual tokens required for comprehensive 3D scene representation. Although visual token pruning has shown promise in accelerating 2D MLLMs, its applicability to 3D domains remains largely unexplored due to fundamental disparities in token structures. In this paper, we reveal two critical insights: (1) Significant redundancy exists in object-level 3D token representations, analogous to patch-level redundancy in 2D systems; (2) Global attention patterns exhibit strong predictive power for identifying non-essential tokens in 3D contexts. Building on these observations, we propose Fast3D, a plug-and-play visual token pruning framework for 3D MLLMs featuring two technical innovations: (1) Global Attention Prediction (GAP), where a lightweight neural network learns to predict the global attention distributions of the target model, enabling efficient token importance estimation for precise pruning guidance; (2) Sample-Adaptive visual token Pruning (SAP), which introduces dynamic token budgets through attention-based complexity assessment, automatically adjusting layer-wise pruning ratios based on input characteristics. Both of these two techniques operate without modifying the parameters of the target model. Extensive evaluations across five benchmarks validate the effectiveness of Fast3D, particularly under high visual token pruning ratios. Code is available at https://github.com/wencan25/Fast3D
Fair CCA for Fair Representation Learning: An ADNI Study
Venue: arXiv cs.LG 2025
Year: 2025

Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes crucial in machine learning, fair CCA has gained attention. However, previous approaches often overlook the impact on downstream classification tasks, limiting applicability. We propose a novel fair CCA method for fair representation learning, ensuring the projected features are independent of sensitive attributes, thus enhancing fairness without compromising accuracy. We validate our method on synthetic data and real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), demonstrating its ability to maintain high correlation analysis performance while improving fairness in classification tasks. Our work enables fair machine learning in neuroimaging studies where unbiased analysis is essential. Code is available in https://github.com/ZhanliangAaronWang/FR-CCA-ADNI.
Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding
Venue: arXiv cs.NE 2025
Year: 2025

Fraud detection is one of the most important challenges that financial systems must address. Detecting fraudulent transactions is critical for payment gateway companies like Flow Payment, which process millions of transactions monthly and require robust security measures to mitigate financial risks. Increasing transaction authorization rates while reducing fraud is essential for providing a good user experience and building a sustainable business. For this reason, discovering novel and improved methods to detect fraud requires continuous research and investment for any company that wants to succeed in this industry. In this work, we introduced a novel method for detecting transactional fraud by incorporating the Relative Distance Rotating Encoding (ReDRE) in the RoFormer model. The incorporation of angle rotation using ReDRE enhances the characterization of time series data within a Transformer, leading to improved fraud detection by better capturing temporal dependencies and event relationships.
Adversarial Activation Patching: A Framework for Detecting and Mitigating Emergent Deception in Safety-Aligned Transformers
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) aligned for safety through techniques like reinforcement learning from human feedback (RLHF) often exhibit emergent deceptive behaviors, where outputs appear compliant but subtly mislead or omit critical information. This paper introduces adversarial activation patching, a novel mechanistic interpretability framework that leverages activation patching as an adversarial tool to induce, detect, and mitigate such deception in transformer-based models. By sourcing activations from "deceptive" prompts and patching them into safe forward passes at specific layers, we simulate vulnerabilities and quantify deception rates. Through toy neural network simulations across multiple scenarios (e.g., 1000 trials per setup), we demonstrate that adversarial patching increases deceptive outputs to 23.9% from a 0% baseline, with layer-specific variations supporting our hypotheses. We propose six hypotheses, including transferability across models, exacerbation in multimodal settings, and scaling effects. An expanded literature review synthesizes over 20 key works in interpretability, deception, and adversarial attacks. Mitigation strategies, such as activation anomaly detection and robust fine-tuning, are detailed, alongside ethical considerations and future research directions. This work advances AI safety by highlighting patching's dual-use potential and provides a roadmap for empirical studies on large-scale models.
DATE-LM: Benchmarking Data Attribution Evaluation for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Data attribution methods quantify the influence of training data on model outputs and are becoming increasingly relevant for a wide range of LLM research and applications, including dataset curation, model interpretability, data valuation. However, there remain critical gaps in systematic LLM-centric evaluation of data attribution methods. To this end, we introduce DATE-LM (Data Attribution Evaluation in Language Models), a unified benchmark for evaluating data attribution methods through real-world LLM applications. DATE-LM measures attribution quality through three key tasks -- training data selection, toxicity/bias filtering, and factual attribution. Our benchmark is designed for ease of use, enabling researchers to configure and run large-scale evaluations across diverse tasks and LLM architectures. Furthermore, we use DATE-LM to conduct a large-scale evaluation of existing data attribution methods. Our findings show that no single method dominates across all tasks, data attribution methods have trade-offs with simpler baselines, and method performance is sensitive to task-specific evaluation design. Finally, we release a public leaderboard for quick comparison of methods and to facilitate community engagement, with the motivation that DATE-LM can serve as a foundation for future data attribution research in LLMs.
Justification Logic for Intuitionistic Modal Logic (Extended Technical Report)
Venue: arXiv cs.LO 2025
Year: 2025

Justification logics are an explication of modal logic; boxes are replaced with proof terms formally through realisation theorems. This can be achieved syntactically using a cut-free proof system e.g. using sequent, hypersequent or nested sequent calculi. In constructive modal logic, boxes and diamonds are decoupled and not De Morgan dual. Kuznets, Marin and Straßburger provide a justification counterpart to constructive modal logic CK and some extensions by making diamonds explicit by introducing new terms called satisfiers. We continue the line of work to provide a justification counterpart to Fischer Servi's intuitionistic modal logic IK and its extensions with the t and 4 axioms. We: extend the syntax of proof terms to accommodate the additional axioms of intuitionistic modal logic; provide an axiomatisation of these justification logics; provide a syntactic realisation procedure using a cut-free nested sequent system for intuitionistic modal logic introduced by Straßburger.