OpenRaiser/Intern-Atlas huggingface dataset:
A Systematic Approach for Multi-objective Double-side Clock Tree Synthesis
Venue: arXiv cs.AR 2025
Year: 2025

As the scaling of semiconductor devices nears its limits, utilizing the back-side space of silicon has emerged as a new trend for future integrated circuits. With intense interest, several works have hacked existing backend tools to explore the potential of synthesizing double-side clock trees via nano Through-Silicon-Vias (nTSVs). However, these works lack a systematic perspective on design resource allocation and multi-objective optimization. We propose a systematic approach to design clock trees with double-side metal layers, including hierarchical clock routing, concurrent buffers and nTSVs insertion, and skew refinement. Compared with the state-of-the-art (SOTA) methods, the widely-used open-source tool, our algorithm outperforms them in latency, skew, wirelength, and the number of buffers and nTSVs.
A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry
Venue: arXiv cs.CV 2025
Year: 2025

Accurate and reliable estimation of biases of low-cost Inertial Measurement Units (IMU) is a key factor to maintain the resilience of Visual-Inertial Odometry (VIO), particularly when visual tracking fails in challenging areas. In such cases, bias estimates from the VIO can deviate significantly from the real values because of the insufficient or erroneous vision features, compromising both localization accuracy and system stability. To address this challenge, we propose a novel plug-and-play module featuring the Inertial Prior Network (IPNet), which infers an IMU bias prior by implicitly capturing the motion characteristics of specific platforms. The core idea is inspired intuitively by the observation that different platforms exhibit distinctive motion patterns, while the integration of low-cost IMU measurements suffers from unbounded error that quickly accumulates over time. Therefore, these specific motion patterns can be exploited to infer the underlying IMU bias. In this work, we first directly infer the biases prior only using the raw IMU data using a sliding window approach, eliminating the dependency on recursive bias estimation combining visual features, thus effectively preventing error propagation in challenging areas. Moreover, to compensate for the lack of ground-truth bias in most visual-inertial datasets, we further introduce an iterative method to compute the mean per-sequence IMU bias for network training and release it to benefit society. The framework is trained and evaluated separately on two public datasets and a self-collected dataset. Extensive experiments show that our method significantly improves localization precision and robustness.
Time-EAPCR-T: A Universal Deep Learning Approach for Anomaly Detection in Industrial Equipment
Venue: arXiv cs.LG 2025
Year: 2025

With the advancement of Industry 4.0, intelligent manufacturing extensively employs sensors for real-time multidimensional data collection, playing a crucial role in equipment monitoring, process optimisation, and efficiency enhancement. Industrial data exhibit characteristics such as multi-source heterogeneity, nonlinearity, strong coupling, and temporal interactions, while also being affected by noise interference. These complexities make it challenging for traditional anomaly detection methods to extract key features, impacting detection accuracy and stability. Traditional machine learning approaches often struggle with such complex data due to limitations in processing capacity and generalisation ability, making them inadequate for practical applications. While deep learning feature extraction modules have demonstrated remarkable performance in image and text processing, they remain ineffective when applied to multi-source heterogeneous industrial data lacking explicit correlations. Moreover, existing multi-source heterogeneous data processing techniques still rely on dimensionality reduction and feature selection, which can lead to information loss and difficulty in capturing high-order interactions. To address these challenges, this study applies the EAPCR and Time-EAPCR models proposed in previous research and introduces a new model, Time-EAPCR-T, where Transformer replaces the LSTM module in the time-series processing component of Time-EAPCR. This modification effectively addresses multi-source data heterogeneity, facilitates efficient multi-source feature fusion, and enhances the temporal feature extraction capabilities of multi-source industrial data.Experimental results demonstrate that the proposed method outperforms existing approaches across four industrial datasets, highlighting its broad application potential.
Deblur Gaussian Splatting SLAM
Venue: arXiv cs.CV 2025
Year: 2025

We present Deblur-SLAM, a robust RGB SLAM pipeline designed to recover sharp reconstructions from motion-blurred inputs. The proposed method bridges the strengths of both frame-to-frame and frame-to-model approaches to model sub-frame camera trajectories that lead to high-fidelity reconstructions in motion-blurred settings. Moreover, our pipeline incorporates techniques such as online loop closure and global bundle adjustment to achieve a dense and precise global trajectory. We model the physical image formation process of motion-blurred images and minimize the error between the observed blurry images and rendered blurry images obtained by averaging sharp virtual sub-frame images. Additionally, by utilizing a monocular depth estimator alongside the online deformation of Gaussians, we ensure precise mapping and enhanced image deblurring. The proposed SLAM pipeline integrates all these components to improve the results. We achieve state-of-the-art results for sharp map estimation and sub-frame trajectory recovery both on synthetic and real-world blurry input data.
Progressive Limb-Aware Virtual Try-On
Venue: arXiv cs.CV 2025
Year: 2025

Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the transferred clothing geometry and textures, which causes incomplete and blurred clothing appearances. In addition, these methods usually mask the limb textures of the input for the clothing-agnostic person representation, which results in inaccurate predictions for human limb regions (i.e., the exposed arm skin), especially when transforming between long-sleeved and short-sleeved garments. To address these problems, we present a progressive virtual try-on framework, named PL-VTON, which performs pixel-level clothing warping based on multiple attributes of clothing and embeds explicit limb-aware features to generate photo-realistic try-on results. Specifically, we design a Multi-attribute Clothing Warping (MCW) module that adopts a two-stage alignment strategy based on multiple attributes to progressively estimate pixel-level clothing displacements. A Human Parsing Estimator (HPE) is then introduced to semantically divide the person into various regions, which provides structural constraints on the human body and therefore alleviates texture bleeding between clothing and limb regions. Finally, we propose a Limb-aware Texture Fusion (LTF) module to estimate high-quality details in limb regions by fusing textures of the clothing and the human body with the guidance of explicit limb-aware features. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively. The code is available at https://github.com/xyhanHIT/PL-VTON.
Personalize Anything for Free with Diffusion Transformer
Venue: arXiv cs.CV 2025
Year: 2025

Personalized image generation aims to produce images of user-specified concepts while enabling flexible editing. Recent training-free approaches, while exhibit higher computational efficiency than training-based methods, struggle with identity preservation, applicability, and compatibility with diffusion transformers (DiTs). In this paper, we uncover the untapped potential of DiT, where simply replacing denoising tokens with those of a reference subject achieves zero-shot subject reconstruction. This simple yet effective feature injection technique unlocks diverse scenarios, from personalization to image editing. Building upon this observation, we propose \textbf{Personalize Anything}, a training-free framework that achieves personalized image generation in DiT through: 1) timestep-adaptive token replacement that enforces subject consistency via early-stage injection and enhances flexibility through late-stage regularization, and 2) patch perturbation strategies to boost structural diversity. Our method seamlessly supports layout-guided generation, multi-subject personalization, and mask-controlled editing. Evaluations demonstrate state-of-the-art performance in identity preservation and versatility. Our work establishes new insights into DiTs while delivering a practical paradigm for efficient personalization.
SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Generative machine learning models for exploring chemical space have shown immense promise, but many molecules they generate are too difficult to synthesize, making them impractical for further investigation or development. In this work, we present a novel approach by fine-tuning Meta's Llama3 Large Language Models (LLMs) to create SynLlama, which generates full synthetic pathways made of commonly accessible building blocks and robust organic reaction templates. SynLlama explores a large synthesizable space using significantly less data, and offers strong performance in both forward and bottom-up synthesis planning compared to other state-of-the-art methods. We find that SynLlama, even without training on external building blocks, can effectively generalize to unseen yet purchasable building blocks, meaning that its reconstruction capabilities extend to a broader synthesizable chemical space than the training data. We also demonstrate the use of SynLlama in a pharmaceutical context for synthesis planning of analog molecules and hit expansion leads for proposed inhibitors of target proteins, offering medicinal chemists a valuable tool for discovery.
Rubikon: Intelligent Tutoring for Rubik's Cube Learning Through AR-enabled Physical Task Reconfiguration
Venue: arXiv cs.HC 2025
Year: 2025

Learning to solve a Rubik's Cube requires the learners to repeatedly practice a skill component, e.g., identifying a misplaced square and putting it back. However, for 3D physical tasks such as this, generating sufficient repeated practice opportunities for learners can be challenging, in part because it is difficult for novices to reconfigure the physical object to specific states. We propose Rubikon, an intelligent tutoring system for learning to solve the Rubik's Cube. Rubikon reduces the necessity for repeated manual configurations of the Rubik's Cube without compromising the tactile experience of handling a physical cube. The foundational design of Rubikon is an AR setup, where learners manipulate a physical cube while seeing an AR-rendered cube on a display. Rubikon automatically generates configurations of the Rubik's Cube to target learners' weaknesses and help them exercise diverse knowledge components. In a between-subjects experiment, we showed that Rubikon learners scored 25% higher on a post-test compared to baselines.
Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning
Venue: arXiv cs.LG 2025
Year: 2025

Precise cell classification is essential in biomedical diagnostics and therapeutic monitoring, particularly for identifying diverse cell types involved in various diseases. Traditional cell classification methods such as flow cytometry depend on molecular labeling which is often costly, time-intensive, and can alter cell integrity. To overcome these limitations, we present a label-free machine learning framework for cell classification, designed for real-time sorting applications using bright-field microscopy images. This approach leverages a teacher-student model architecture enhanced by knowledge distillation, achieving high efficiency and scalability across different cell types. Demonstrated through a use case of classifying lymphocyte subsets, our framework accurately classifies T4, T8, and B cell types with a dataset of 80,000 preprocessed images, accessible via an open-source Python package for easy adaptation. Our teacher model attained 98\% accuracy in differentiating T4 cells from B cells and 93\% accuracy in zero-shot classification between T8 and B cells. Remarkably, our student model operates with only 0.02\% of the teacher model's parameters, enabling field-programmable gate array (FPGA) deployment. Our FPGA-accelerated student model achieves an ultra-low inference latency of just 14.5~$μ$s and a complete cell detection-to-sorting trigger time of 24.7~$μ$s, delivering 12x and 40x improvements over the previous state-of-the-art real-time cell analysis algorithm in inference and total latency, respectively, while preserving accuracy comparable to the teacher model. This framework provides a scalable, cost-effective solution for lymphocyte classification, as well as a new SOTA real-time cell sorting implementation for rapid identification of subsets using in situ deep learning on off-the-shelf computing hardware.
SCOOP: CoSt-effective COngestiOn Attacks in Payment Channel Networks
Venue: arXiv cs.CR 2025
Year: 2025

Payment channel networks (PCNs) are a promising solution to address blockchain scalability and throughput challenges, However, the security of PCNs and their vulnerability to attacks are not sufficiently studied. In this paper, we introduce SCOOP, a framework that includes two novel congestion attacks on PCNs. These attacks consider the minimum transferable amount along a path (path capacity) and the number of channels involved (path length), formulated as linear optimization problems. The first attack allocates the attacker's budget to achieve a specific congestion threshold, while the second maximizes congestion under budget constraints. Simulation results show the effectiveness of the proposed attack formulations in comparison to other attack strategies. Specifically, the results indicate that the first attack provides around a 40\% improvement in congestion performance, while the second attack offers approximately a 50\% improvement in comparison to the state-of-the-art. Moreover, in terms of payment to congestion efficiency, the first attack is about 60\% more efficient, and the second attack is around 90\% more efficient in comparison to state-of-the-art
Automated Planning for Optimal Data Pipeline Instantiation
Venue: arXiv cs.AI 2025
Year: 2025

Data pipeline frameworks provide abstractions for implementing sequences of data-intensive transformation operators, automating the deployment and execution of such transformations in a cluster. Deploying a data pipeline, however, requires computing resources to be allocated in a data center, ideally minimizing the overhead for communicating data and executing operators in the pipeline while considering each operator's execution requirements. In this paper, we model the problem of optimal data pipeline deployment as planning with action costs, where we propose heuristics aiming to minimize total execution time. Experimental results indicate that the heuristics can outperform the baseline deployment and that a heuristic based on connections outperforms other strategies.
Understanding Driver Cognition and Decision-Making Behaviors in High-Risk Scenarios: A Drift Diffusion Perspective
Venue: arXiv cs.AI 2025
Year: 2025

Ensuring safe interactions between autonomous vehicles (AVs) and human drivers in mixed traffic systems remains a major challenge, particularly in complex, high-risk scenarios. This paper presents a cognition-decision framework that integrates individual variability and commonalities in driver behavior to quantify risk cognition and model dynamic decision-making. First, a risk sensitivity model based on a multivariate Gaussian distribution is developed to characterize individual differences in risk cognition. Then, a cognitive decision-making model based on the drift diffusion model (DDM) is introduced to capture common decision-making mechanisms in high-risk environments. The DDM dynamically adjusts decision thresholds by integrating initial bias, drift rate, and boundary parameters, adapting to variations in speed, relative distance, and risk sensitivity to reflect diverse driving styles and risk preferences. By simulating high-risk scenarios with lateral, longitudinal, and multidimensional risk sources in a driving simulator, the proposed model accurately predicts cognitive responses and decision behaviors during emergency maneuvers. Specifically, by incorporating driver-specific risk sensitivity, the model enables dynamic adjustments of key DDM parameters, allowing for personalized decision-making representations in diverse scenarios. Comparative analysis with IDM, Gipps, and MOBIL demonstrates that DDM more precisely captures human cognitive processes and adaptive decision-making in high-risk scenarios. These findings provide a theoretical basis for modeling human driving behavior and offer critical insights for enhancing AV-human interaction in real-world traffic environments.
Shape-Kit: A Design Toolkit for Crafting On-Body Expressive Haptics
Venue: arXiv cs.HC 2025
Year: 2025

Driven by the vision of everyday haptics, the HCI community is advocating for "design touch first" and investigating "how to touch well." However, a gap remains between the exploratory nature of haptic design and technical reproducibility. We present Shape-Kit, a hybrid design toolkit embodying our "crafting haptics" metaphor, where hand touch is transduced into dynamic pin-based sensations that can be freely explored across the body. An ad-hoc tracking module captures and digitizes these patterns. Our study with 14 designers and artists demonstrates how Shape-Kit facilitates sensorial exploration for expressive haptic design. We analyze how designers collaboratively ideate, prototype, iterate, and compose touch experiences and show the subtlety and richness of touch that can be achieved through diverse crafting methods with Shape-Kit. Reflecting on the findings, our work contributes key insights into haptic toolkit design and touch design practices centered on the "crafting haptics" metaphor. We discuss in-depth how Shape-Kit's simplicity, though remaining constrained, enables focused crafting for deeper exploration, while its collaborative nature fosters shared sense-making of touch experiences.
Plausibility Vaccine: Injecting LLM Knowledge for Event Plausibility
Venue: arXiv cs.CL 2025
Year: 2025

Despite advances in language modelling, distributional methods that build semantic representations from co-occurrences fail to discriminate between plausible and implausible events. In this work, we investigate how plausibility prediction can be improved by injecting latent knowledge prompted from large language models using parameter-efficient fine-tuning. We train 12 task adapters to learn various physical properties and association measures and perform adapter fusion to compose latent semantic knowledge from each task on top of pre-trained AlBERT embeddings. We automate auxiliary task data generation, which enables us to scale our approach and fine-tune our learned representations across two plausibility datasets. Our code is available at https://github.com/Jacob-Chmura/plausibility-vaccine.
Discovering uncertainty: Gaussian constitutive neural networks with correlated weights
Venue: arXiv cs.CE 2025
Year: 2025

When characterizing materials, it can be important to not only predict their mechanical properties, but also to estimate the probability distribution of these properties across a set of samples. Constitutive neural networks allow for the automated discovery of constitutive models that exactly satisfy physical laws given experimental testing data, but are only capable of predicting the mean stress response. Stochastic methods treat each weight as a random variable and are capable of learning their probability distributions. Bayesian constitutive neural networks combine both methods, but their weights lack physical interpretability and we must sample each weight from a probability distribution to train or evaluate the model. Here we introduce a more interpretable network with fewer parameters, simpler training, and the potential to discover correlated weights: Gaussian constitutive neural networks. We demonstrate the performance of our new Gaussian network on biaxial testing data, and discover a sparse and interpretable four-term model with correlated weights. Importantly, the discovered distributions of material parameters across a set of samples can serve as priors to discover better constitutive models for new samples with limited data. We anticipate that Gaussian constitutive neural networks are a natural first step towards generative constitutive models informed by physical laws and parameter uncertainty.
Modular Mechanism Design Optimization in Large-Scale Systems with Manufacturing Cost Considerations
Venue: arXiv cs.ET 2025
Year: 2025

Modular design maximizes utility by using standardized components in large-scale systems. From a manufacturing perspective, it supports green technology by reducing material waste and improving reusability. Industrially, it offers economic benefits through economies of scale, making it a practical design strategy. Typically, modularization selects a representative design from predefined candidates to meet all performance requirements. However, achieving effective modularization in mechanical mechanisms presents challenges. First, mechanisms depend on geometric relationships for functional motion, and varying loads lead to different optimal parameters, complicating representative design selection. Second, the chosen design often exceeds optimal parameters, causing over-specification and performance deviations, which worsen as scale increases. To address this, we propose a modular mechanism design framework using surrogate-based optimization. This approach finds optimal designs for large-scale systems and partitions them into groups, each assigned an optimized design. This multi-objective optimization (MOO) problem balances economies of scale and performance consistency. Unlike conventional methods based on predefined candidates and simple grouping, our framework optimizes design variables flexibly for modularization. Additionally, we analyze manufacturing cost parameters to develop a decision support system for selecting optimal strategies in diverse design scenarios. This enhances maintainability, improves interchangeability, and fosters environmentally sustainable manufacturing.
Identifying Cooperative Personalities in Multi-agent Contexts through Personality Steering with Representation Engineering
Venue: arXiv cs.AI 2025
Year: 2025

As Large Language Models (LLMs) gain autonomous capabilities, their coordination in multi-agent settings becomes increasingly important. However, they often struggle with cooperation, leading to suboptimal outcomes. Inspired by Axelrod's Iterated Prisoner's Dilemma (IPD) tournaments, we explore how personality traits influence LLM cooperation. Using representation engineering, we steer Big Five traits (e.g., Agreeableness, Conscientiousness) in LLMs and analyze their impact on IPD decision-making. Our results show that higher Agreeableness and Conscientiousness improve cooperation but increase susceptibility to exploitation, highlighting both the potential and limitations of personality-based steering for aligning AI agents.