OpenRaiser/Intern-Atlas huggingface dataset:
LLM-ABM for Transportation: Assessing the Potential of LLM Agents in System Analysis
Venue: arXiv cs.MA 2025
Year: 2025

Agent-based modeling approaches represent the state-of-art in modeling travel demand and transportation system dynamics and are valuable tools for transportation planning. However, established agent-based approaches in transportation rely on multi-hierarchical mathematical models to simulate travel behavior, which faces theoretical and practical limitations. The advent of large language models (LLM) provides a new opportunity to refine agent-based modeling in transportation. LLM agents, which have impressive reasoning and planning abilities, can serve as a proxy of human travelers and be integrated into the modeling framework. However, despite evidence of their behavioral soundness, no existing studies have assessed the impact and validity of LLM-agent-based simulations from a system perspective in transportation. This paper aims to address this issue by designing and integrating LLM agents with human-traveler-like characteristics into a simulation of a transportation system and assessing its performance based on existing benchmarks. Using the classical transportation setting of the morning commute, we find that not only do the agents exhibit fine behavioral soundness, but also produce system dynamics that align well with standard benchmarks. Our analysis first verifies the effectiveness and potential of LLM-agent-based modeling for transportation planning on the system level.
Training in translation tools and technologies: Findings of the EMT survey 2023
Venue: arXiv cs.CY 2025
Year: 2025

This article reports on the third iteration of a survey of computerized tools and technologies taught as part of postgraduate translation training programmes. While the survey was carried out under the aegis of the EMT Network, more than half of responses are from outside that network. The results show the responsiveness of programmes to innovations in translation technology, with increased compulsory inclusion of machine translation, post-editing, and quality evaluation, and a rapid response to the release of generative tools. The flexibility required during the Covid-19 pandemic has also led to some lasting changes to programmes. While the range of tools being taught has continued to expand, programmes seem to be consolidating their core offering around cloud-based software with cost-free academic access. There has also been an increase in the embedding of professional contexts and workflows associated with translation technology. Generic file management and data security skills have increased in perceived importance, and legal and ethical issues related to translation data have also become more prominent. In terms of course delivery the shift away from conventional labs identified in EMT2017 has accelerated markedly, no doubt partly driven by the pandemic, accompanied by a dramatic expansion in the use of students' personal devices.
Co-design of magnetic soft robots with large deformation and contacts via material point method and topology optimization
Venue: arXiv cs.RO 2025
Year: 2025

Magnetic soft robots embedded with hard magnetic particles enable untethered actuation via external magnetic fields, offering remote, rapid, and precise control, which is highly promising for biomedical applications. However, designing such systems is challenging due to the complex interplay of magneto-elastic dynamics, large deformation, solid contacts, time-varying stimuli, and posture-dependent loading. As a result, most existing research relies on heuristics and trial-and-error methods or focuses on the independent design of stimuli or structures under static conditions. We propose a topology optimization framework for magnetic soft robots that simultaneously designs structures, location-specific material magnetization and time-varying magnetic stimuli, accounting for large deformations, dynamic motion, and solid contacts. This is achieved by integrating generalized topology optimization with the magneto-elastic material point method, which supports GPU-accelerated parallel simulations and auto-differentiation for sensitivity analysis. We applied this framework to design magnetic robots for various tasks, including multi-task shape morphing and locomotion, in both 2D and 3D. The method autonomously generates optimized robotic systems to achieve target behaviors without requiring human intervention. Despite the nonlinear physics and large design space, it demonstrates high computational efficiency, completing all cases within minutes. The framework provides a computational foundation for the autonomous co-design of active soft materials in applications such as metasurfaces, drug delivery, and minimally invasive procedures.
SIGHT: Synthesizing Image-Text Conditioned and Geometry-Guided 3D Hand-Object Trajectories
Venue: arXiv cs.CV 2025
Year: 2025

When humans grasp an object, they naturally form trajectories in their minds to manipulate it for specific tasks. Modeling hand-object interaction priors holds significant potential to advance robotic and embodied AI systems in learning to operate effectively within the physical world. We introduce SIGHT, a novel task focused on generating realistic and physically plausible 3D hand-object interaction trajectories from a single image and a brief language-based task description. Prior work on hand-object trajectory generation typically relies on textual input that lacks explicit grounding to the target object, or assumes access to 3D object meshes, which are often considerably more difficult to obtain than 2D images. We propose SIGHT-Fusion, a novel diffusion-based image-text conditioned generative model that tackles this task by retrieving the most similar 3D object mesh from a database and enforcing geometric hand-object interaction constraints via a novel inference-time diffusion guidance. We benchmark our model on the HOI4D and H2O datasets, adapting relevant baselines for this novel task. Experiments demonstrate our superior performance in the diversity and quality of generated trajectories, as well as in hand-object interaction geometry metrics.
Pairwise Matching of Intermediate Representations for Fine-grained Explainability
Venue: arXiv cs.CV 2025
Year: 2025

The differences between images belonging to fine-grained categories are often subtle and highly localized, and existing explainability techniques for deep learning models are often too diffuse to provide useful and interpretable explanations. We propose a new explainability method (PAIR-X) that leverages both intermediate model activations and backpropagated relevance scores to generate fine-grained, highly-localized pairwise visual explanations. We use animal and building re-identification (re-ID) as a primary case study of our method, and we demonstrate qualitatively improved results over a diverse set of explainability baselines on 35 public re-ID datasets. In interviews, animal re-ID experts found PAIR-X to be a meaningful improvement over existing baselines for deep model explainability, and suggested that its visualizations would be directly applicable to their work. We also propose a novel quantitative evaluation metric for our method, and demonstrate that PAIR-X visualizations appear more plausible for correct image matches than incorrect ones even when the model similarity score for the pairs is the same. By improving interpretability, PAIR-X enables humans to better distinguish correct and incorrect matches. Our code is available at: https://github.com/pairx-explains/pairx
Task Tokens: A Flexible Approach to Adapting Behavior Foundation Models
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in imitation learning have led to transformer-based behavior foundation models (BFMs) that enable multi-modal, human-like control for humanoid agents. While excelling at zero-shot generation of robust behaviors, BFMs often require meticulous prompt engineering for specific tasks, potentially yielding suboptimal results. We introduce "Task Tokens", a method to effectively tailor BFMs to specific tasks while preserving their flexibility. Our approach leverages the transformer architecture of BFMs to learn a new task-specific encoder through reinforcement learning, keeping the original BFM frozen. This allows incorporation of user-defined priors, balancing reward design and prompt engineering. By training a task encoder to map observations to tokens, used as additional BFM inputs, we guide performance improvement while maintaining the model's diverse control characteristics. We demonstrate Task Tokens' efficacy across various tasks, including out-of-distribution scenarios, and show their compatibility with other prompting modalities. Our results suggest that Task Tokens offer a promising approach for adapting BFMs to specific control tasks while retaining their generalization capabilities.
From Motivating to Manipulative: The Use of Deceptive Design in a Game's Free-to-Play Transition
Venue: arXiv cs.HC 2025
Year: 2025

Over the last decade, the free-to-play (F2P) game business model has gained popularity in the games industry. We examine the role of deceptive design during a game's transition to F2P and its impacts on players. Our analysis focuses on game mechanics and a Reddit analysis of the Overwatch (OW) series after it transitioned to an F2P model. Our study identifies nine game mechanics that use deceptive design patterns. We also identify factors contributing to a negative gameplay experience. Business model transitions in games present possibilities for problematic practices. Our findings identify the need for game developers and publishers to balance player investments and fairness of rewards. A game's successful transition depends on maintaining fundamental components of player motivation and ensuring transparent communication. Compared to existing taxonomies in other media, games need a comprehensive classification of deceptive design. We emphasize the importance of understanding player perceptions and the impact of deceptive practices in future research.
Resona: Improving Context Copying in Linear Recurrence Models with Retrieval
Venue: arXiv cs.CL 2025
Year: 2025

Recent shifts in the space of large language model (LLM) research have shown an increasing focus on novel architectures to compete with prototypical Transformer-based models that have long dominated this space. Linear recurrent models have proven to be a viable competitor due to their computational efficiency. However, such models still demonstrate a sizable gap compared to Transformers in terms of in-context learning among other tasks that require recalling information from a context. In this work, we introduce Resona, a simple and scalable framework for augmenting linear recurrent models with retrieval. Resona augments models with the ability to integrate retrieved information from the provided input context, enabling tailored behavior to diverse task requirements. Experiments on a variety of linear recurrent models demonstrate that Resona-augmented models observe significant performance gains on a variety of synthetic as well as real-world natural language tasks, highlighting its ability to act as a general purpose method to improve the in-context learning and language modeling abilities of linear recurrent LLMs.
Factored Agents: Decoupling In-Context Learning and Memorization for Robust Tool Use
Venue: arXiv cs.AI 2025
Year: 2025

In this paper, we propose a novel factored agent architecture designed to overcome the limitations of traditional single-agent systems in agentic AI. Our approach decomposes the agent into two specialized components: (1) a large language model (LLM) that serves as a high level planner and in-context learner, which may use dynamically available information in user prompts, (2) a smaller language model which acts as a memorizer of tool format and output. This decoupling addresses prevalent issues in monolithic designs, including malformed, missing, and hallucinated API fields, as well as suboptimal planning in dynamic environments. Empirical evaluations demonstrate that our factored architecture significantly improves planning accuracy and error resilience, while elucidating the inherent trade-off between in-context learning and static memorization. These findings suggest that a factored approach is a promising pathway for developing more robust and adaptable agentic AI systems.
SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have transformed natural language processing by learning from massive datasets, yet this rapid progress has also drawn legal scrutiny, as the ability to unintentionally generate copyrighted content has already prompted several prominent lawsuits. In this work, we introduce SUV (Selective Unlearning for Verbatim data), a selective unlearning framework designed to prevent LLM from memorizing copyrighted content while preserving its overall utility. In detail, the proposed method constructs a dataset that captures instances of copyrighted infringement cases by the targeted LLM. With the dataset, we unlearn the content from the LLM by means of Direct Preference Optimization (DPO), which replaces the verbatim copyrighted content with plausible and coherent alternatives. Since DPO may hinder the LLM's performance in other unrelated tasks, we integrate gradient projection and Fisher information regularization to mitigate the degradation. We validate our approach using a large-scale dataset of 500 famous books (predominantly copyrighted works) and demonstrate that SUV significantly reduces verbatim memorization with negligible impact on the performance on unrelated tasks. Extensive experiments on both our dataset and public benchmarks confirm the scalability and efficacy of our approach, offering a promising solution for mitigating copyright risks in real-world LLM applications.
Student-Powered Digital Scholarship CoLab Project in the HKUST Library: Develop a Chinese Named-Entity Recognition (NER) Tool within One Semester from the Ground Up
Venue: arXiv cs.DL 2025
Year: 2025

Starting in February 2024, the HKUST Library further extended the scope of AI literacy to AI utilization, which focuses on fostering student involvement in utilizing state-of-the-art technologies in the projects that initiated by the Library, named "Digital Scholarship (DS) CoLab". A key focus of the DS CoLab scheme has been on cultivating talents and enabling students to utilize advanced technologies in practical context. It aims to reinforce the library's role as a catalyst and hub for fostering multidisciplinary collaboration and cultivate the "can do spirit" among university members. The Library offers 1-2 projects per year for students to engage with advanced technologies in practical contexts while supporting the Library in tackling challenges and streamlining operational tasks. The tool that introduced in this paper was mainly developed by two of the authors, Sherry Yip Sau Lai and Berry Han Liuruo, as part-time student helpers under one of our DS CoLab scheme in the 2024 Spring Semester (February to May 2024). This paper details the complete journey from ideation to implementation of developing a Chinese Named-Entity Recognition (NER) Tool from the group up within one semester, from the initial research and planning stages to execution and come up a viable product. The collaborative spirit fostered by this project, with students playing a central role, exemplifies the power and potential of innovative educational models that prioritize hands-on learning with student involvement.
Prompt Engineering for Large Language Model-assisted Inductive Thematic Analysis
Venue: arXiv cs.HC 2025
Year: 2025

The potential of large language models (LLMs) to mitigate the time- and cost- related challenges associated with inductive thematic analysis (ITA) has been extensively explored in the literature. However, the use of LLMs to support ITA has often been opportunistic, relying on ad hoc prompt engineering (PE) approaches, thereby undermining the reliability, transparency, and replicability of the analysis. The goal of this study is to develop a structured approach to PE in LLM-assisted ITA. To this end, a comprehensive review of the existing literature is conducted to examine how ITA researchers integrate LLMs into their workflows and, in particular, how PE is utilized to support the analytical process. Built on the insights generated from this review, four key steps for effective PE in LLM-assisted ITA are identified and extensively outlined. Furthermore, the study explores state-of-the-art PE techniques that can enhance the execution of these steps, providing ITA researchers with practical strategies to improve their analyses. In conclusion, the main contributions of this paper include: (i) it maps the existing research on LLM-assisted ITA to enable a better understanding of the rapidly developing field, (ii) it outlines a structured four-step PE process to enhance methodological rigor, (iii) it discusses the application of advanced PE techniques to support the execution of these steps, and (iv) it highlights key directions for future research.
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
Venue: arXiv cs.LG 2025
Year: 2025

Differentially Private Stochastic Gradient Descent (DP-SGD) is a widely adopted technique for privacy-preserving deep learning. A critical challenge in DP-SGD is selecting the optimal clipping threshold C, which involves balancing the trade-off between clipping bias and noise magnitude, incurring substantial privacy and computing overhead during hyperparameter tuning. In this paper, we propose Dynamic Clipping DP-SGD (DC-SGD), a framework that leverages differentially private histograms to estimate gradient norm distributions and dynamically adjust the clipping threshold C. Our framework includes two novel mechanisms: DC-SGD-P and DC-SGD-E. DC-SGD-P adjusts the clipping threshold based on a percentile of gradient norms, while DC-SGD-E minimizes the expected squared error of gradients to optimize C. These dynamic adjustments significantly reduce the burden of hyperparameter tuning C. The extensive experiments on various deep learning tasks, including image classification and natural language processing, show that our proposed dynamic algorithms achieve up to 9 times acceleration on hyperparameter tuning than DP-SGD. And DC-SGD-E can achieve an accuracy improvement of 10.62% on CIFAR10 than DP-SGD under the same privacy budget of hyperparameter tuning. We conduct rigorous theoretical privacy and convergence analyses, showing that our methods seamlessly integrate with the Adam optimizer. Our results highlight the robust performance and efficiency of DC-SGD, offering a practical solution for differentially private deep learning with reduced computational overhead and enhanced privacy guarantees.
Simplification of Trajectory Streams
Venue: arXiv cs.CG 2025
Year: 2025

While there are software systems that simplify trajectory streams on the fly, few curve simplification algorithms with quality guarantees fit the streaming requirements. We present streaming algorithms for two such problems under the Fréchet distance $d_F$ in $\mathbb{R}^d$ for some constant $d \geq 2$. Consider a polygonal curve $τ$ in $\mathbb{R}^d$ in a stream. We present a streaming algorithm that, for any $\varepsilon\in (0,1)$ and $δ> 0$, produces a curve $σ$ such that $d_F(σ,τ[v_1,v_i])\le (1+\varepsilon)δ$ and $|σ|\le 2\,\mathrm{opt}-2$, where $τ[v_1,v_i]$ is the prefix in the stream so far, and $\mathrm{opt} = \min\{|σ'|: d_F(σ',τ[v_1,v_i])\le δ\}$. Let $α= 2(d-1){\lfloor d/2 \rfloor}^2 + d$. The working storage is $O(\varepsilon^{-α})$. Each vertex is processed in $O(\varepsilon^{-α}\log\frac{1}{\varepsilon})$ time for $d \in \{2,3\}$ and $O(\varepsilon^{-α})$ time for $d \geq 4$ . Thus, the whole $τ$ can be simplified in $O(\varepsilon^{-α}|τ|\log\frac{1}{\varepsilon})$ time. Ignoring polynomial factors in $1/\varepsilon$, this running time is a factor $|τ|$ faster than the best static algorithm that offers the same guarantees. We present another streaming algorithm that, for any integer $k \geq 2$ and any $\varepsilon \in (0,\frac{1}{17})$, maintains a curve $σ$ such that $|σ| \leq 2k-2$ and $d_F(σ,τ[v_1,v_i])\le (1+\varepsilon) \cdot \min\{d_F(σ',τ[v_1,v_i]): |σ'| \leq k\}$, where $τ[v_1,v_i]$ is the prefix in the stream so far. The working storage is $O((k\varepsilon^{-1}+\varepsilon^{-(α+1)})\log \frac{1}{\varepsilon})$. Each vertex is processed in $O(k\varepsilon^{-(α+1)}\log^2\frac{1}{\varepsilon})$ time for $d \in \{2,3\}$ and $O(k\varepsilon^{-(α+1)}\log\frac{1}{\varepsilon})$ time for $d \geq 4$.
Reproducibility Companion Paper: Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
Venue: arXiv cs.IR 2025
Year: 2025

In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which was published in the proceedings of the 31st ACM International Conference on Multimedia. This paper aims to validate the effectiveness of our proposed method and help others reproduce our experimental results. We provide detailed descriptions of our preprocessed datasets, source code structure, configuration file settings, experimental environment, and reproduced experimental results.
Function Fitting Based on Kolmogorov-Arnold Theorem and Kernel Functions
Venue: arXiv cs.LG 2025
Year: 2025

This paper proposes a unified theoretical framework based on the Kolmogorov-Arnold representation theorem and kernel methods. By analyzing the mathematical relationship among kernels, B-spline basis functions in Kolmogorov-Arnold Networks (KANs) and the inner product operation in self-attention mechanisms, we establish a kernel-based feature fitting framework that unifies the two models as linear combinations of kernel functions. Under this framework, we propose a low-rank Pseudo-Multi-Head Self-Attention module (Pseudo-MHSA), which reduces the parameter count of traditional MHSA by nearly 50\%. Furthermore, we design a Gaussian kernel multi-head self-attention variant (Gaussian-MHSA) to validate the effectiveness of nonlinear kernel functions in feature extraction. Experiments on the CIFAR-10 dataset demonstrate that Pseudo-MHSA model achieves performance comparable to the ViT model of the same dimensionality under the MAE framework and visualization analysis reveals their similarity of multi-head distribution patterns. Our code is publicly available.
STSA: Spatial-Temporal Semantic Alignment for Visual Dubbing
Venue: arXiv cs.CV 2025
Year: 2025

Existing audio-driven visual dubbing methods have achieved great success. Despite this, we observe that the semantic ambiguity between spatial and temporal domains significantly degrades the synthesis stability for the dynamic faces. We argue that aligning the semantic features from spatial and temporal domains is a promising approach to stabilizing facial motion. To achieve this, we propose a Spatial-Temporal Semantic Alignment (STSA) method, which introduces a dual-path alignment mechanism and a differentiable semantic representation. The former leverages a Consistent Information Learning (CIL) module to maximize the mutual information at multiple scales, thereby reducing the manifold differences between spatial and temporal domains. The latter utilizes probabilistic heatmap as ambiguity-tolerant guidance to avoid the abnormal dynamics of the synthesized faces caused by slight semantic jittering. Extensive experimental results demonstrate the superiority of the proposed STSA, especially in terms of image quality and synthesis stability. Pre-trained weights and inference code are available at https://github.com/SCAILab-USTC/STSA.