OpenRaiser/Intern-Atlas huggingface dataset:
Enhanced Mean Field Game for Interactive Decision-Making with Varied Stylish Multi-Vehicles
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents an MFG-based decision-making framework for autonomous driving in heterogeneous traffic. To capture diverse human behaviors, we propose a quantitative driving style representation that maps abstract traits to parameters such as speed, safety factors, and reaction time. These parameters are embedded into the MFG through a spatial influence field model. To ensure safe operation in dense traffic, we introduce a safety-critical lane-changing algorithm that leverages dynamic safety margins, time-to-collision analysis, and multi-layered constraints. Real-world NGSIM data is employed for style calibration and empirical validation. Experimental results demonstrate zero collisions across six style combinations, two 15-vehicle scenarios, and NGSIM-based trials, consistently outperforming conventional game-theoretic baselines. Overall, our approach provides a scalable, interpretable, and behavior-aware planning framework for real-world autonomous driving applications.
Towards Integrating Multi-Spectral Imaging with Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

We present a study of how to integrate color (RGB) and multi-spectral imagery (red, green, red-edge, and near-infrared) into the 3D Gaussian Splatting (3DGS) framework, a state-of-the-art explicit radiance-field-based method for fast and high-fidelity 3D reconstruction from multi-view images. While 3DGS excels on RGB data, naive per-band optimization of additional spectra yields poor reconstructions due to inconsistently appearing geometry in the spectral domain. This problem is prominent, even though the actual geometry is the same, regardless of spectral modality. To investigate this, we evaluate three strategies: 1) Separate per-band reconstruction with no shared structure. 2) Splitting optimization, in which we first optimize RGB geometry, copy it, and then fit each new band to the model by optimizing both geometry and band representation. 3) Joint, in which the modalities are jointly optimized, optionally with an initial RGB-only phase. We showcase through quantitative metrics and qualitative novel-view renderings on multi-spectral datasets the effectiveness of our dedicated optimized Joint strategy, increasing overall spectral reconstruction as well as enhancing RGB results through spectral cross-talk. We therefore suggest integrating multi-spectral data directly into the spherical harmonics color components to compactly model each Gaussian's multi-spectral reflectance. Moreover, our analysis reveals several key trade-offs in when and how to introduce spectral bands during optimization, offering practical insights for robust multi-modal 3DGS reconstruction.
TARA: A Low-Cost 3D-Printed Robotic Arm for Accessible Robotics Education
Venue: arXiv cs.RO 2025
Year: 2025

The high cost of robotic platforms limits students' ability to gain practical skills directly applicable in real-world scenarios. To address this challenge, this paper presents TARA, a low-cost, 3D-printed robotic arm designed for accessible robotics education. TARA includes an open-source repository with design files, assembly instructions, and baseline code, enabling users to build and customize the platform. The system balances affordability and functionality, offering a highly capable robotic arm for approximately 200 USD, significantly lower than industrial systems that often cost thousands of dollars. Experimental validation confirmed accurate performance in basic manipulation tasks. Rather than focusing on performance benchmarking, this work prioritizes educational reproducibility, providing a platform that students and educators can reliably replicate and extend.
A Dynamic Fusion Model for Consistent Crisis Response
Venue: arXiv cs.CL 2025
Year: 2025

In response to the urgent need for effective communication with crisis-affected populations, automated responses driven by language models have been proposed to assist in crisis communications. A critical yet often overlooked factor is the consistency of response style, which could affect the trust of affected individuals in responders. Despite its importance, few studies have explored methods for maintaining stylistic consistency across generated responses. To address this gap, we propose a novel metric for evaluating style consistency and introduce a fusion-based generation approach grounded in this metric. Our method employs a two-stage process: it first assesses the style of candidate responses and then optimizes and integrates them at the instance level through a fusion process. This enables the generation of high-quality responses while significantly reducing stylistic variation between instances. Experimental results across multiple datasets demonstrate that our approach consistently outperforms baselines in both response quality and stylistic uniformity.
Almost Tight Approximation Hardness and Online Algorithms for Resource Scheduling
Venue: arXiv cs.DS 2025
Year: 2025

We study the precedence-constrained resource scheduling problem [SICOMP'75]. There are $n$ jobs where each job takes a certain time to finish and has a resource requirement throughout the execution time. There are precedence among the jobs. The problem asks that given a resource budget, schedule the jobs obeying the precedence constraints to minimize makespan (maximum completion time of a job) such that at any point in time, the total resource being used by all the jobs is at most the given resource budget. In the offline setting, an important open question is whether a polynomial-time $O(1)$-factor approximation algorithm can be found. We prove almost tight hardness of approximation: For some constant $α> 0$, there is no $o((\log t_{\max})^α)$-factor ( or $o( ( \log n )^α)$-factor ) approximation algorithm with $n$ jobs of maximum job length $t_{\max}$, unless P = NP ( or NP $\subset$ DTIME$(O( 2^{\text{polylog}(n)}))$ ). We further show a connection between this scheduling problem and a seemingly unrelated problem called the shortest common super-sequence (SCS) problem, which has wide application in Biology and Genomics. We prove that an $o(\log t_{\max})$-factor approximation of the scheduling problem would imply the existence of an $o(|Σ|)$-approximation algorithm for SCS with alphabet $Σ$. We then consider the online setting. We present $Ω(\log n)$ and $Ω(\log t_{\max})$ lower bounds of the competitive ratio of any randomized online algorithm. Moreover, we present a matching $O(\min\{\log n, \log t_{\max}\})$-competitive deterministic online algorithm.
A novel parameter estimation method for pneumatic soft hand control applying logarithmic decrement for pseudo rigid body modeling
Venue: arXiv cs.RO 2025
Year: 2025

The rapid advancement in physical human-robot interaction (HRI) has accelerated the development of soft robot designs and controllers. Controlling soft robots, especially soft hand grasping, is challenging due to their continuous deformation, motivating the use of reduced model-based controllers for real-time dynamic performance. Most existing models, however, suffer from computational inefficiency and complex parameter identification, limiting their real-time applicability. To address this, we propose a paradigm coupling Pseudo-Rigid Body Modeling with the Logarithmic Decrement Method for parameter estimation (PRBM plus LDM). Using a soft robotic hand test bed, we validate PRBM plus LDM for predicting position and force output from pressure input and benchmark its performance. We then implement PRBM plus LDM as the basis for closed-loop position and force controllers. Compared to a simple PID controller, the PRBM plus LDM position controller achieves lower error (average maximum error across all fingers: 4.37 degrees versus 20.38 degrees). For force control, PRBM plus LDM outperforms constant pressure grasping in pinching tasks on delicate objects: potato chip 86 versus 82.5, screwdriver 74.42 versus 70, brass coin 64.75 versus 35. These results demonstrate PRBM plus LDM as a computationally efficient and accurate modeling technique for soft actuators, enabling stable and flexible grasping with precise force regulation.
Towards Propagation-aware Representation Learning for Supervised Social Media Graph Analytics
Venue: arXiv cs.SI 2025
Year: 2025

Social media platforms generate vast, complex graph-structured data, facilitating diverse tasks such as rumor detection, bot identification, and influence modeling. Real-world applications like public opinion monitoring and stock trading -- which have a strong attachment to social media -- demand models that are performant across diverse tasks and datasets. However, most existing solutions are purely data-driven, exhibiting vulnerability to the inherent noise within social media data. Moreover, the reliance on task-specific model design challenges efficient reuse of the same model architecture on different tasks, incurring repetitive engineering efforts. To address these challenges in social media graph analytics, we propose a general representation learning framework that integrates a dual-encoder structure with a kinetic-guided propagation module. In addition to jointly modeling structural and contextual information with two encoders, our framework innovatively captures the information propagation dynamics within social media graphs by integrating principled kinetic knowledge. By deriving a propagation-aware encoder and corresponding optimization objective from a Markov chain-based transmission model, the representation learning pipeline receives a boost in its robustness to noisy data and versatility in diverse tasks. Extensive experiments verify that our approach achieves state-of-the-art performance with a unified architecture on a variety of social media graph mining tasks spanning graph classification, node classification, and link prediction. Besides, our solution exhibits strong zero-shot and few-shot transferability across datasets, demonstrating practicality when handling data-scarce tasks.
RealMat: Realistic Materials with Diffusion and Reinforcement Learning
Venue: arXiv cs.GR 2025
Year: 2025

Generative models for high-quality materials are particularly desirable to make 3D content authoring more accessible. However, the majority of material generation methods are trained on synthetic data. Synthetic data provides precise supervision for material maps, which is convenient but also tends to create a significant visual gap with real-world materials. Alternatively, recent work used a small dataset of real flash photographs to guarantee realism, however such data is limited in scale and diversity. To address these limitations, we propose RealMat, a diffusion-based material generator that leverages realistic priors, including a text-to-image model and a dataset of realistic material photos under natural lighting. In RealMat, we first finetune a pretrained Stable Diffusion XL (SDXL) with synthetic material maps arranged in $2 \times 2$ grids. This way, our model inherits some realism of SDXL while learning the data distribution of the synthetic material grids. Still, this creates a realism gap, with some generated materials appearing synthetic. We propose to further finetune our model through reinforcement learning (RL), encouraging the generation of realistic materials. We develop a realism reward function for any material image under natural lighting, by collecting a large-scale dataset of realistic material images. We show that this approach increases generated materials' realism compared to our base model and related work.
MATL-DC: A Multi-domain Aggregation Transfer Learning Framework for EEG Emotion Recognition with Domain-Class Prototype under Unseen Targets
Venue: arXiv cs.LG 2025
Year: 2025

Emotion recognition based on electroencephalography (EEG) signals is increasingly becoming a key research hotspot in affective Brain-Computer Interfaces (aBCIs). However, the current transfer learning model greatly depends on the source domain and target domain data, which hinder the practical application of emotion recognition. Therefore, we propose a Multi-domain Aggregation Transfer Learning framework for EEG emotion recognition with Domain-Class prototype under unseen targets (MATL-DC). We design the feature decoupling module to decouple class-invariant domain features from domain-invariant class features from shallow features. In the model training stage, the multi-domain aggregation mechanism aggregates the domain feature space to form a superdomain, which enhances the characteristics of emotional EEG signals. In each superdomain, we further extract the class prototype representation by class features. In addition, we adopt the pairwise learning strategy to transform the sample classification problem into the similarity problem between sample pairs, which effectively alleviates the influence of label noise. It is worth noting that the target domain is completely unseen during the training process. In the inference stage, we use the trained domain-class prototypes for inference, and then realize emotion recognition. We rigorously validate it on the publicly available databases (SEED, SEED-IV and SEED-V). The results show that the accuracy of MATL-DC model is 84.70\%, 68.11\% and 61.08\%, respectively. MATL-DC achieves comparable or even better performance than methods that rely on both source and target domains. The source code is available at https://github.com/WuCB-BCI/MATL-DC.
Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA
Venue: arXiv cs.CL 2025
Year: 2025

Chinese information extraction (IE) involves multiple tasks across diverse temporal domains, including Classical and Modern documents. Fine-tuning a single model on heterogeneous tasks and across different eras may lead to interference and reduced performance. Therefore, in this paper, we propose Tea-MOELoRA, a parameter-efficient multi-task framework that combines LoRA with a Mixture-of-Experts (MoE) design. Multiple low-rank LoRA experts specialize in different IE tasks and eras, while a task-era-aware router mechanism dynamically allocates expert contributions. Experiments show that Tea-MOELoRA outperforms both single-task and joint LoRA baselines, demonstrating its ability to leverage task and temporal knowledge effectively.
DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Reconstruction dynamic visual scenes from electroencephalography (EEG) signals remains a primary challenge in brain decoding, limited by the low spatial resolution of EEG, a temporal mismatch between neural recordings and video dynamics, and the insufficient use of semantic information within brain activity. Therefore, existing methods often inadequately resolve both the dynamic coherence and the complex semantic context of the perceived visual stimuli. To overcome these limitations, we introduce DynaMind, a novel framework that reconstructs video by jointly modeling neural dynamics and semantic features via three core modules: a Regional-aware Semantic Mapper (RSM), a Temporal-aware Dynamic Aligner (TDA), and a Dual-Guidance Video Reconstructor (DGVR). The RSM first utilizes a regional-aware encoder to extract multimodal semantic features from EEG signals across distinct brain regions, aggregating them into a unified diffusion prior. In the mean time, the TDA generates a dynamic latent sequence, or blueprint, to enforce temporal consistency between the feature representations and the original neural recordings. Together, guided by the semantic diffusion prior, the DGVR translates the temporal-aware blueprint into a high-fidelity video reconstruction. On the SEED-DV dataset, DynaMind sets a new state-of-the-art (SOTA), boosting reconstructed video accuracies (video- and frame-based) by 12.5 and 10.3 percentage points, respectively. It also achieves a leap in pixel-level quality, showing exceptional visual fidelity and temporal coherence with a 9.4% SSIM improvement and a 19.7% FVMD reduction. This marks a critical advancement, bridging the gap between neural dynamics and high-fidelity visual semantics.
Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping
Venue: arXiv cs.AI 2025
Year: 2025

Identifying whether two product listings refer to the same Stock Keeping Unit (SKU) is a persistent challenge in ecommerce, especially when explicit identifiers are missing and product names vary widely across platforms. Rule based heuristics and keyword similarity often misclassify products by overlooking subtle distinctions in brand, specification, or bundle configuration. To overcome these limitations, we propose Question to Knowledge (Q2K), a multi agent framework that leverages Large Language Models (LLMs) for reliable SKU mapping. Q2K integrates: (1) a Reasoning Agent that generates targeted disambiguation questions, (2) a Knowledge Agent that resolves them via focused web searches, and (3) a Deduplication Agent that reuses validated reasoning traces to reduce redundancy and ensure consistency. A human in the loop mechanism further refines uncertain cases. Experiments on real world consumer goods datasets show that Q2K surpasses strong baselines, achieving higher accuracy and robustness in difficult scenarios such as bundle identification and brand origin disambiguation. By reusing retrieved reasoning instead of issuing repeated searches, Q2K balances accuracy with efficiency, offering a scalable and interpretable solution for product integration.
DcMatch: Unsupervised Multi-Shape Matching with Dual-Level Consistency
Venue: arXiv cs.CV 2025
Year: 2025

Establishing point-to-point correspondences across multiple 3D shapes is a fundamental problem in computer vision and graphics. In this paper, we introduce DcMatch, a novel unsupervised learning framework for non-rigid multi-shape matching. Unlike existing methods that learn a canonical embedding from a single shape, our approach leverages a shape graph attention network to capture the underlying manifold structure of the entire shape collection. This enables the construction of a more expressive and robust shared latent space, leading to more consistent shape-to-universe correspondences via a universe predictor. Simultaneously, we represent these correspondences in both the spatial and spectral domains and enforce their alignment in the shared universe space through a novel cycle consistency loss. This dual-level consistency fosters more accurate and coherent mappings. Extensive experiments on several challenging benchmarks demonstrate that our method consistently outperforms previous state-of-the-art approaches across diverse multi-shape matching scenarios.
EndoGMDE: Generalizable Monocular Depth Estimation with Mixture of Low-Rank Experts for Diverse Endoscopic Scenes
Venue: arXiv cs.CV 2025
Year: 2025

Self-supervised monocular depth estimation is a significant task for low-cost and efficient 3D scene perception and measurement in endoscopy. However, the variety of illumination conditions and scene features is still the primary challenges for depth estimation in endoscopic scenes. In this work, a novel self-supervised framework is proposed for monocular depth estimation in diverse endoscopy. Firstly, considering the diverse features in endoscopic scenes with different tissues, a novel block-wise mixture of dynamic low-rank experts is proposed to efficiently finetune the foundation model for endoscopic depth estimation. In the proposed module, based on the input feature, different experts with a small amount of trainable parameters are adaptively selected for weighted inference, from low-rank experts which are allocated based on the generalization of each block. Moreover, a novel self-supervised training framework is proposed to jointly cope with brightness inconsistency and reflectance interference. The proposed method outperforms state-of-the-art works on SCARED dataset and SimCol dataset. Furthermore, the proposed network also achieves the best generalization based on zero-shot depth estimation on C3VD, Hamlyn and SERV-CT dataset. The outstanding performance of our model is further demonstrated with 3D reconstruction and ego-motion estimation. The proposed method could contribute to accurate endoscopy for minimally invasive measurement and surgery. The evaluation codes will be released upon acceptance, while the demo videos can be found on: https://endo-gmde.netlify.app/.
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data. With many open-source LLMs available, selecting the best model for fine-tuning downstream tasks is challenging, primarily focusing on how to quickly identify the optimal LLM. We introduce a Data and Model Compression Framework (DaMoC) that addresses this challenge by: 1) Data Level: A systematic categorization of data filtering methodologies for LLMs is first established, classifying them into three distinct paradigms: (1) distribution-aware methods, (2) quality-aware methods, and (3) hybrid approaches considering both dimensions. Further, we enhance the density of key tokens in the text achieving token compression. Subsequently, we use an LLM to iterative rewrite the text to optimize its expression. 2) Model Level: We use layer similarity scores to assess each layer's importance and remove those with lower importance. Then, we introduce a sparse merging paradigm to preserve as much of the original model's capability as possible. Extensive experiments on four datasets, medical Q&A, financial Q&A, general Q&A, and reading comprehension, show that we can select the optimal LLM while saving approximately 20-fold in training time.
Unpacking Personal(?!) Health Informatics for Proactive Collective Care in India
Venue: arXiv cs.HC 2025
Year: 2025

Personal Health Informatics (PHI), which leverages digital tools and information systems to support health assessment and self-care, promises more proactive, user-centered care, yet adoption and meaningful utilization barriers in India remain underexplored. Through a sequential mixed-methods study in urban India (Initial survey (n=87) and semi-structured interviews (n=22), follow-up survey = 116, and co-design workshops (n=6)), we surface practices, perceptions, and behaviors to identify ways PHI can be better utilized for proactive care in the Indian context. We find that PHI is valued for monitoring and enabling collective care; however, its adoption is constrained by low health and technology literacy, usability and integration issues, fragmented and costly technology ecosystems, and mistrust of digital health platforms. From triangulated evidence, we derive concrete design requirements, including user-controlled sharing, accessible analytics, and verifiable health information, and present a culturally grounded design vision for an integrated platform for collective care through design and evaluation of a figma prototype. The prototype evaluation provides further directions for design and development to better orient PHI for proactive care through the PHI-Proact operational map, which involves agency, elicitation, and engagement. Finally, using PHI-Proact, we conclude with concrete recommendations for designing and responsibly deploying PHI systems for proactive collective care in emerging contexts, which differ socially, culturally, infrastructurally, and technologically from WEIRD contexts.