OpenRaiser/Intern-Atlas huggingface dataset:
Spiking World Model with Multi-Compartment Neurons for Model-based Reinforcement Learning
Venue: arXiv cs.NE 2025
Year: 2025

Brain-inspired spiking neural networks (SNNs) have garnered significant research attention in algorithm design and perception applications. However, their potential in the decision-making domain, particularly in model-based reinforcement learning, remains underexplored. The difficulty lies in the need for spiking neurons with long-term temporal memory capabilities, as well as network optimization that can integrate and learn information for accurate predictions. The dynamic dendritic information integration mechanism of biological neurons brings us valuable insights for addressing these challenges. In this study, we propose a multi-compartment neuron model capable of nonlinearly integrating information from multiple dendritic sources to dynamically process long sequential inputs. Based on this model, we construct a Spiking World Model (Spiking-WM), to enable model-based deep reinforcement learning (DRL) with SNNs. We evaluated our model using the DeepMind Control Suite, demonstrating that Spiking-WM outperforms existing SNN-based models and achieves performance comparable to artificial neural network (ANN)-based world models employing Gated Recurrent Units (GRUs). Furthermore, we assess the long-term memory capabilities of the proposed model in speech datasets, including SHD, TIMIT, and LibriSpeech 100h, showing that our multi-compartment neuron model surpasses other SNN-based architectures in processing long sequences. Our findings underscore the critical role of dendritic information integration in shaping neuronal function, emphasizing the importance of cooperative dendritic processing in enhancing neural computation.
Speculative Ad-hoc Querying
Venue: arXiv cs.DB 2025
Year: 2025

Analyzing large datasets requires responsive query execution, but executing SQL queries on massive datasets can be slow. This paper explores whether query execution can begin even before the user has finished typing, allowing results to appear almost instantly. We propose SpeQL, a system that leverages Large Language Models (LLMs) to predict likely queries based on the database schema, the user's past queries, and their incomplete query. Since exact query prediction is infeasible, SpeQL speculates on partial queries in two ways: 1) it predicts the query structure to compile and plan queries in advance, and 2) it precomputes smaller temporary tables that are much smaller than the original database, but are still predicted to contain all information necessary to answer the user's final query. Additionally, SpeQL continuously displays results for speculated queries and subqueries in real time, aiding exploratory analysis. A utility/user study showed that SpeQL improved task completion time, and participants reported that its speculative display of results helped them discover patterns in the data more quickly. In the study, SpeQL improves user's query latency by up to $289\times$ and kept the overhead reasonable, at $\$4$ per hour.
Enhanced Security of Public Key Encryption with Certified Deletion
Venue: arXiv cs.CR 2025
Year: 2025

In classical cryptography, certified deletion is simply impossible. Since classical information can be copied any number of times easily. In quantum cryptography, certified deletion is possible because of theorems of quantum mechanics such as the quantum no-clone theorem, quantum superposition etc. In this paper, we show the PKE-CD (Public Key Encryption with Certified Deletion) scheme constructed in by Bartusek and Khurana in CRYPTO 2023 lack an important security property, which is important in practical applications. Then we show how to enhance this property, and construct a concrete scheme with this property. And we also discuss the relations between PKE-CD and other quantum cryptographic schemes such as quantum seal, quantum bit commitment etc.
MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a multi-resolution reconstruction method for Electrical Impedance Tomography (EIT), referred to as MR-EIT, which is capable of operating in both supervised and unsupervised learning modes. MR-EIT integrates an ordered feature extraction module and an unordered coordinate feature expression module. The former achieves the mapping from voltage to two-dimensional conductivity features through pre-training, while the latter realizes multi-resolution reconstruction independent of the order and size of the input sequence by utilizing symmetric functions and local feature extraction mechanisms. In the data-driven mode, MR-EIT reconstructs high-resolution images from low-resolution data of finite element meshes through two stages of pre-training and joint training, and demonstrates excellent performance in simulation experiments. In the unsupervised learning mode, MR-EIT does not require pre-training data and performs iterative optimization solely based on measured voltages to rapidly achieve image reconstruction from low to high resolution. It shows robustness to noise and efficient super-resolution reconstruction capabilities in both simulation and real water tank experiments. Experimental results indicate that MR-EIT outperforms the comparison methods in terms of Structural Similarity (SSIM) and Relative Image Error (RIE), especially in the unsupervised learning mode, where it can significantly reduce the number of iterations and improve image reconstruction quality.
Optimal Bilinear Equalizer Beamforming Design for Cell-Free Massive MIMO Networks with Arbitrary Channel Estimators
Venue: arXiv cs.IT 2025
Year: 2025

This paper studies the distributed optimal bilinear equalizer (OBE) beamforming design for both the uplink and downlink cell-free massive multiple-input multiple-output networks. We consider arbitrary statistics-based channel estimators over spatially correlated Rician fading channels. In the uplink, we derive the achievable spectral efficiency (SE) performance and OBE combining schemes with arbitrary statistics-based channel estimators and compute their respective closed-form expressions. It is insightful to explore that the achievable SE performance is not dependent on the choice of channel estimator when OBE combining schemes are applied over Rayleigh channels. In the downlink, we derive the achievable SE performance expressions with BE precoding schemes and arbitrary statistics-based channel estimators utilized and compute them in closed form. Then, we obtain the OBE precoding scheme leveraging insights from uplink OBE combining schemes.
LLMs are everywhere: Ubiquitous Utilization of AI Models through Air Computing
Venue: arXiv cs.NI 2025
Year: 2025

We are witnessing a new era where problem-solving and cognitive tasks are being increasingly delegated to Large Language Models (LLMs) across diverse domains, ranging from code generation to holiday planning. This trend also creates a demand for the ubiquitous execution of LLM-powered applications in a wide variety of environments in which traditional terrestrial 2D networking infrastructures may prove insufficient. A promising solution in this context is to extend edge computing into a 3D setting to include aerial platforms organized in multiple layers, a paradigm we refer to as air computing, to augment local devices for running LLM and Generative AI (GenAI) applications. This approach alleviates the strain on existing infrastructure while enhancing service efficiency by offloading computational tasks to the corresponding air units such as UAVs. Furthermore, the coordinated deployment of various air units can significantly improve the Quality of Experience (QoE) by ensuring seamless, adaptive, and resilient task execution. In this study, we investigate the synergy between LLM-based applications and air computing, exploring their potential across various use cases. Additionally, we present a disaster response case study demonstrating how the collaborative utilization of LLMs and air computing can significantly improve outcomes in critical situations.
Shadow: Leveraging Segmentation Masks for Cross-Embodiment Policy Transfer
Venue: arXiv cs.RO 2025
Year: 2025

Data collection in robotics is spread across diverse hardware, and this variation will increase as new hardware is developed. Effective use of this growing body of data requires methods capable of learning from diverse robot embodiments. We consider the setting of training a policy using expert trajectories from a single robot arm (the source), and evaluating on a different robot arm for which no data was collected (the target). We present a data editing scheme termed Shadow, in which the robot during training and evaluation is replaced with a composite segmentation mask of the source and target robots. In this way, the input data distribution at train and test time match closely, enabling robust policy transfer to the new unseen robot while being far more data efficient than approaches that require co-training on large amounts of data from diverse embodiments. We demonstrate that an approach as simple as Shadow is effective both in simulation on varying tasks and robots, and on real robot hardware, where Shadow demonstrates an average of over 2x improvement in success rate compared to the strongest baseline.
Development of a Five-Fingerd Biomimetic Soft Robotic Hand by 3D Printing the Skin and Skeleton as One Unit
Venue: arXiv cs.RO 2025
Year: 2025

Robot hands that imitate the shape of the human body have been actively studied, and various materials and mechanisms have been proposed to imitate the human body. Although the use of soft materials is advantageous in that it can imitate the characteristics of the human body's epidermis, it increases the number of parts and makes assembly difficult in order to perform complex movements. In this study, we propose a skin-skeleton integrated robot hand that has 15 degrees of freedom and consists of four parts. The developed robotic hand is mostly composed of a single flexible part produced by a 3D printer, and while it can be easily assembled, it can perform adduction, flexion, and opposition of the thumb, as well as flexion of four fingers.
An Efficient 3D Convolutional Neural Network with Channel-wise, Spatial-grouped, and Temporal Convolutions
Venue: arXiv cs.CV 2025
Year: 2025

There has been huge progress on video action recognition in recent years. However, many works focus on tweaking existing 2D backbones due to the reliance of ImageNet pretraining, which restrains the models from achieving higher efficiency for video recognition. In this work we introduce a simple and very efficient 3D convolutional neural network for video action recognition. The design of the building block consists of a channel-wise convolution, followed by a spatial group convolution, and finally a temporal convolution. We evaluate the performance and efficiency of our proposed network on several video action recognition datasets by directly training on the target dataset without relying on pertaining. On Something-Something-V1&V2, Kinetics-400 and Multi-Moments in Time, our network can match or even surpass the performance of other models which are several times larger. On the fine-grained action recognition dataset FineGym, we beat the previous state-of-the-art accuracy achieved with 2-stream methods by more than 5% using only RGB input.
RAVE: End-to-end Hierarchical Visual Localization with Rasterized and Vectorized HD map
Venue: arXiv cs.RO 2025
Year: 2025

Accurate localization serves as an important component in autonomous driving systems. Traditional rule-based localization involves many standalone modules, which is theoretically fragile and requires costly hyperparameter tuning, therefore sacrificing the accuracy and generalization. In this paper, we propose an end-to-end visual localization system, RAVE, in which the surrounding images are associated with the HD map data to estimate pose. To ensure high-quality observations for localization, a low-rank flow-based prior fusion module (FLORA) is developed to incorporate misaligned map prior into the perceived BEV features. Pursuing a balance among efficiency, interpretability, and accuracy, a hierarchical localization module is proposed, which efficiently estimates poses through a decoupled BEV neural matching-based pose solver (DEMA) using rasterized HD map, and then refines the estimation through a Transformer-based pose regressor (POET) using vectorized HD map. The experimental results demonstrate that our method can perform robust and accurate localization under varying environmental conditions while running efficiently.
3D Dynamic Fluid Assets from Single-View Videos with Generative Gaussian Splatting
Venue: arXiv cs.GR 2025
Year: 2025

While the generation of 3D content from single-view images has been extensively studied, the creation of physically consistent 3D dynamic scenes from videos remains in its early stages. We propose a novel framework leveraging generative 3D Gaussian Splatting (3DGS) models to extract and re-simulate 3D dynamic fluid objects from single-view videos using simulation methods. The fluid geometry represented by 3DGS is initially generated and optimized from single-view images, then denoised, densified, and aligned across frames. We estimate the fluid surface velocity using optical flow, propose a mainstream extraction algorithm to refine it. The 3D volumetric velocity field is then derived from the velocity of the fluid's enclosed surface. The velocity field is therewith converted into a divergence-free, grid-based representation, enabling the optimization of simulation parameters through its differentiability across frames. This process outputs simulation-ready fluid assets with physical dynamics closely matching those observed in the source video. Our approach is applicable to various liquid fluids, including inviscid and viscous types, and allows users to edit the output geometry or extend movement durations seamlessly. This automatic method for creating 3D dynamic fluid assets from single-view videos, easily obtainable from the internet, shows great potential for generating large-scale 3D fluid assets at a low cost.
Riemannian Integrated Gradients: A Geometric View of Explainable AI
Venue: arXiv cs.LG 2025
Year: 2025

We introduce Riemannian Integrated Gradients (RIG); an extension of Integrated Gradients (IG) to Riemannian manifolds. We demonstrate that RIG restricts to IG when the Riemannian manifold is Euclidean space. We show that feature attribution can be phrased as an eigenvalue problem where attributions correspond to eigenvalues of a symmetric endomorphism.
Semantic-ICP: Iterative Closest Point for Non-rigid Multi-Organ Point Cloud Registration
Venue: arXiv cs.CV 2025
Year: 2025

Point cloud registration is important in computer-aided interventions (CAI). While learning-based point cloud registration methods have been developed, their clinical application is hampered by issues of generalizability and explainability. Therefore, classical point cloud registration methods, including Iterative Closest Point (ICP), are still widely applied in CAI. ICP methods fail to consider that: (1) the points have well-defined semantic meaning, in that each point can be related to a specific anatomical label; (2) the deformation required for registration needs to follow biomechanical energy constraints. In this paper, we present a novel non-rigid semantic ICP (SemICP) method that handles multiple point labels and uses linear elastic energy regularization. We use semantic labels to improve the robustness of closest point matching and propose a novel point cloud deformation representation that incorporates explicit biomechanical energy regularization. Our experiments on four datasets show that our method significantly improves the Hausdorff distance and mean surface distance compared with other point cloud registration methods. We also demonstrate that integrating deep learning segmentation models with our registration pipeline enables effective alignment of US and MR point clouds.
Evidence of conceptual mastery in the application of rules by Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

In this paper we leverage psychological methods to investigate LLMs' conceptual mastery in applying rules. We introduce a novel procedure to match the diversity of thought generated by LLMs to that observed in a human sample. We then conducted two experiments comparing rule-based decision-making in humans and LLMs. Study 1 found that all investigated LLMs replicated human patterns regardless of whether they are prompted with scenarios created before or after their training cut-off. Moreover, we found unanticipated differences between the two sets of scenarios among humans. Surprisingly, even these differences were replicated in LLM responses. Study 2 turned to a contextual feature of human rule application: under forced time delay, human samples rely more heavily on a rule's text than on other considerations such as a rule's purpose.. Our results revealed that some models (Gemini Pro and Claude 3) responded in a human-like manner to a prompt describing either forced delay or time pressure, while others (GPT-4o and Llama 3.2 90b) did not. We argue that the evidence gathered suggests that LLMs have mastery over the concept of rule, with implications for both legal decision making and philosophical inquiry.
Towards An Efficient LLM Training Paradigm for CTR Prediction
Venue: arXiv cs.IR 2025
Year: 2025

Large Language Models (LLMs) have demonstrated tremendous potential as the next-generation ranking-based recommendation system. Many recent works have shown that LLMs can significantly outperform conventional click-through-rate (CTR) prediction approaches. Despite such promising results, the computational inefficiency inherent in the current training paradigm makes it particularly challenging to train LLMs for ranking-based recommendation tasks on large datasets. To train LLMs for CTR prediction, most existing studies adopt the prevalent ''sliding-window'' paradigm. Given a sequence of $m$ user interactions, a unique training prompt is constructed for each interaction by designating it as the prediction target along with its preceding $n$ interactions serving as context. In turn, the sliding-window paradigm results in an overall complexity of $O(mn^2)$ that scales linearly with the length of user interactions. Consequently, a direct adoption to train LLMs with such strategy can result in prohibitively high training costs as the length of interactions grows. To alleviate the computational inefficiency, we propose a novel training paradigm, namely Dynamic Target Isolation (DTI), that structurally parallelizes the training of $k$ (where $k >> 1$) target interactions. Furthermore, we identify two major bottlenecks - hidden-state leakage and positional bias overfitting - that limit DTI to only scale up to a small value of $k$ (e.g., 5) then propose a computationally light solution to effectively tackle each. Through extensive experiments on three widely adopted public CTR datasets, we empirically show that DTI reduces training time by an average of $\textbf{92%}$ (e.g., from $70.5$ hrs to $5.31$ hrs), without compromising CTR prediction performance.
Can We Find the Code? An Empirical Study of Google Scholar's Code Retrieval
Venue: arXiv cs.DL 2025
Year: 2025

Academic codes associated with research papers are valuable resources for scholars. In specialized fields outside computer science, code availability is often limited, making effective code retrieval essential. Google Scholar is a crucial academic search tool. If a code published in the paper is not retrievable via Google Scholar, its accessibility and impact are significantly reduced. This study takes the term "accelerated degradation" combined with "reliability" as an example, and finds that, for papers published by Elsevier, only GitHub links included in abstracts are comprehensively retrieved by Google Scholar. When such links appear within the main body of a paper, even in the "Data Availability" section, they may be ignored and become unsearchable. These findings highlight the importance of strategically placing GitHub links in abstracts to enhance code discoverability on Google Scholar.
Balanced segmentation of CNNs for multi-TPU inference
Venue: arXiv cs.DC 2025
Year: 2025

In this paper, we propose different alternatives for convolutional neural networks (CNNs) segmentation, addressing inference processes on computing architectures composed by multiple Edge TPUs. Specifically, we compare the inference performance for a number of state-of-the-art CNN models taking as a reference inference times on one TPU and a compiler-based pipelined inference implementation as provided by the Google's Edge TPU compiler. Departing from a profiled-based segmentation strategy, we provide further refinements to balance the workload across multiple TPUs, leveraging their cooperative computing power, reducing work imbalance and alleviating the memory access bottleneck due to the limited amount of on-chip memory per TPU. The observed performance results compared with a single TPU yield superlinear speedups and accelerations up to 2.60x compared with the segmentation offered by the compiler targeting multiple TPUs.
Personalize Your LLM: Fake it then Align it
Venue: arXiv cs.LG 2025
Year: 2025

Personalizing large language models (LLMs) is essential for delivering tailored interactions that improve user experience. Many existing personalization methods require fine-tuning LLMs for each user, rendering them prohibitively expensive for widespread adoption. Although retrieval-based approaches offer a more compute-efficient alternative, they still depend on large, high-quality datasets that are not consistently available for all users. To address this challenge, we propose CHAMELEON, a scalable and efficient personalization approach that uses (1) self-generated personal preference data and (2) representation editing to enable quick and cost-effective personalization. Our experiments on various tasks, including those from the LaMP personalization benchmark, show that CHAMELEON efficiently adapts models to personal preferences, improving instruction-tuned models and outperforms two personalization baselines by an average of 40% across two model architectures.
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media
Venue: arXiv cs.CL 2025
Year: 2025

Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increasingly applied to classify mental health conditions from textual data, but selecting the most effective model involves trade-offs in accuracy, interpretability, and computational efficiency. This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside deep learning architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. Our findings indicate that ML and DL models achieve comparable classification performance on medium-sized datasets, with ML models offering greater interpretability through variable importance scores, while DL models are more robust to complex linguistic patterns. Additionally, ML models require explicit feature engineering, whereas DL models learn hierarchical representations directly from text. Logistic regression provides the advantage of capturing both positive and negative associations between features and mental health conditions, whereas tree-based models prioritize decision-making power through split-based feature selection. This study offers empirical insights into the advantages and limitations of different modeling approaches and provides recommendations for selecting appropriate methods based on dataset size, interpretability needs, and computational constraints.