OpenRaiser/Intern-Atlas huggingface dataset:
Switch-Based Multi-Part Neural Network
Venue: arXiv cs.NE 2025
Year: 2025

This paper introduces decentralized and modular neural network framework designed to enhance the scalability, interpretability, and performance of artificial intelligence (AI) systems. At the heart of this framework is a dynamic switch mechanism that governs the selective activation and training of individual neurons based on input characteristics, allowing neurons to specialize in distinct segments of the data domain. This approach enables neurons to learn from disjoint subsets of data, mimicking biological brain function by promoting task specialization and improving the interpretability of neural network behavior. Furthermore, the paper explores the application of federated learning and decentralized training for real-world AI deployments, particularly in edge computing and distributed environments. By simulating localized training on non-overlapping data subsets, we demonstrate how modular networks can be efficiently trained and evaluated. The proposed framework also addresses scalability, enabling AI systems to handle large datasets and distributed processing while preserving model transparency and interpretability. Finally, we discuss the potential of this approach in advancing the design of scalable, privacy-preserving, and efficient AI systems for diverse applications.
Demand Private Coded Caching: Small Cache Size
Venue: arXiv cs.IT 2025
Year: 2025

We investigate the demand private coded caching problem, which is an $(N,K)$ coded caching problem with $N$ files, $K$ users, each equipped with a cache of size $M$, and an additional privacy constraint on user demands, i.e., each user can not gain any information about the demands of other users. We focus on scenarios where the size of users' caches is small, aiming to further characterize the fundamental limits of this problem. We first present a new virtual-user-based achievable scheme for arbitrary number of users and files, and two MDS-code-based achievable schemes for the case $N \le K$. With a newly derived converse bound for the case $N \le K$, these proposed schemes lead to the optimal memory-rate tradeoff of the demand private coded caching problem for $M \in \big[0, \frac{N}{(K+1)(N-1)} \big] $ where $N \le K \le 2N-2$, and the optimal memory-rate tradeoff for $M \in \big[0, \frac{1}{K+1} \big] $ where $ K > 2N-2$. Moreover, for the case of 2 files and arbitrary number of users, by deriving another new converse bound, the optimal memory-rate tradeoff is characterized for $M\in \big[0,\frac{2}{K}\big] \cup \big[\frac{2(K-1)}{K+1},2\big]$. Finally, we provide the optimal memory-rate tradeoff of the demand private coded caching problem for 2 files and 3 users.
One-Pass to Reason: Token Duplication and Block-Sparse Mask for Efficient Fine-Tuning on Multi-Turn Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Fine-tuning Large Language Models (LLMs) on multi-turn reasoning datasets requires N (number of turns) separate forward passes per conversation due to reasoning token visibility constraints, as reasoning tokens for a turn are discarded in subsequent turns. We propose duplicating response tokens along with a custom attention mask to enable single-pass processing of entire conversations. We prove our method produces identical losses to the N-pass approach while reducing time complexity from $O\bigl(N^{3}\bigl)$ to $O\bigl(N^{2}\bigl)$ and maintaining the same memory complexity for a transformer based model. Our approach achieves significant training speedup while preserving accuracy. Our implementation is available online (https://github.com/devrev/One-Pass-to-Reason).
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances and depth constraints. Traditional navigation strategies struggle with limited sensor information, making safe and efficient operation difficult. In this paper, we propose a reinforcement learning (RL) framework for ASV navigation under depth constraints, where the vehicle must reach a target while avoiding unsafe areas with only a single depth measurement per timestep from a downward-facing Single Beam Echosounder (SBES). To enhance environmental awareness, we integrate Gaussian Process (GP) regression into the RL framework, enabling the agent to progressively estimate a bathymetric depth map from sparse sonar readings. This approach improves decision-making by providing a richer representation of the environment. Furthermore, we demonstrate effective sim-to-real transfer, ensuring that trained policies generalize well to real-world aquatic conditions. Experimental results validate our method's capability to improve ASV navigation performance while maintaining safety in challenging shallow-water environments.
Clustering of return words in languages of interval exchanges
Venue: arXiv cs.FL 2025
Year: 2025

A word over an ordered alphabet is said to be clustering if identical letters appear adjacently in its Burrows-Wheeler transform. Such words are strictly related to (discrete) interval exchange transformations. We use an extended version of the well-known Rauzy induction to show that every return word in the language generated by a regular interval exchange transformation is clustering, partially answering a question of Lapointe (2021).
Enhancing Long-Term Re-Identification Robustness Using Synthetic Data: A Comparative Analysis
Venue: arXiv cs.CV 2025
Year: 2025

This contribution explores the impact of synthetic training data usage and the prediction of material wear and aging in the context of re-identification. Different experimental setups and gallery set expanding strategies are tested, analyzing their impact on performance over time for aging re-identification subjects. Using a continuously updating gallery, we were able to increase our mean Rank-1 accuracy by 24%, as material aging was taken into account step by step. In addition, using models trained with 10% artificial training data, Rank-1 accuracy could be increased by up to 13%, in comparison to a model trained on only real-world data, significantly boosting generalized performance on hold-out data. Finally, this work introduces a novel, open-source re-identification dataset, pallet-block-2696. This dataset contains 2,696 images of Euro pallets, taken over a period of 4 months. During this time, natural aging processes occurred and some of the pallets were damaged during their usage. These wear and tear processes significantly changed the appearance of the pallets, providing a dataset that can be used to generate synthetically aged pallets or other wooden materials.
Treewidth Parameterized by Feedback Vertex Number
Venue: arXiv cs.DS 2025
Year: 2025

We provide the first algorithm for computing an optimal tree decomposition for a given graph $G$ that runs in single exponential time in the feedback vertex number of $G$, that is, in time $2^{O(\text{fvn}(G))}\cdot n^{O(1)}$, where $\text{fvn}(G)$ is the feedback vertex number of $G$ and $n$ is the number of vertices of $G$. On a classification level, this improves the previously known results by Chapelle et al. [Discrete Applied Mathematics '17] and Fomin et al. [Algorithmica '18], who independently showed that an optimal tree decomposition can be computed in single exponential time in the vertex cover number of $G$. One of the biggest open problems in the area of parameterized complexity is whether we can compute an optimal tree decomposition in single exponential time in the treewidth of the input graph. The currently best known algorithm by Korhonen and Lokshtanov [STOC '23] runs in $2^{O(\text{tw}(G)^2)}\cdot n^4$ time, where $\text{tw}(G)$ is the treewidth of $G$. Our algorithm improves upon this result on graphs $G$ where $\text{fvn}(G)\in o(\text{tw}(G)^2)$. On a different note, since $\text{fvn}(G)$ is an upper bound on $\text{tw}(G)$, our algorithm can also be seen either as an important step towards a positive resolution of the above-mentioned open problem, or, if its answer is negative, then a mark of the tractability border of single exponential time algorithms for the computation of treewidth.
Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillation
Venue: arXiv cs.CV 2025
Year: 2025

Monocular 3D lane detection is challenging due to the difficulty in capturing depth information from single-camera images. A common strategy involves transforming front-view (FV) images into bird's-eye-view (BEV) space through inverse perspective mapping (IPM), facilitating lane detection using BEV features. However, IPM's flat-ground assumption and loss of contextual information lead to inaccuracies in reconstructing 3D information, especially height. In this paper, we introduce a BEV-based framework to address these limitations and improve 3D lane detection accuracy. Our approach incorporates a Hierarchical Depth-Aware Head that provides multi-scale depth features, mitigating the flat-ground assumption by enhancing spatial awareness across varying depths. Additionally, we leverage Depth Prior Distillation to transfer semantic depth knowledge from a teacher model, capturing richer structural and contextual information for complex lane structures. To further refine lane continuity and ensure smooth lane reconstruction, we introduce a Conditional Random Field module that enforces spatial coherence in lane predictions. Extensive experiments validate that our method achieves state-of-the-art performance in terms of z-axis error and outperforms other methods in the field in overall performance. The code is released at: https://anonymous.4open.science/r/Depth3DLane-DCDD.
NRevisit: A Cognitive Behavioral Metric for Code Understandability Assessment
Venue: arXiv cs.SE 2025
Year: 2025

Measuring code understandability is both highly relevant and exceptionally challenging. This paper proposes a dynamic code understandability assessment method, which estimates a personalized code understandability score from the perspective of the specific programmer handling the code. The method consists of dynamically dividing the code unit under development or review in code regions (invisible to the programmer) and using the number of revisits (NRevisit) to each region as the primary feature for estimating the code understandability score. This approach removes the uncertainty related to the concept of a "typical programmer" assumed by static software code complexity metrics and can be easily implemented using a simple, low-cost, and non-intrusive desktop eye tracker or even a standard computer camera. This metric was evaluated using cognitive load measured through electroencephalography (EEG) in a controlled experiment with 35 programmers. Results show a very high correlation ranging from rs = 0.9067 to rs = 0.9860 (with p nearly 0) between the scores obtained with different alternatives of NRevisit and the ground truth represented by the EEG measurements of programmers' cognitive load, demonstrating the effectiveness of our approach in reflecting the cognitive effort required for code comprehension. The paper also discusses possible practical applications of NRevisit, including its use in the context of AI-generated code, which is already widely used today.
Bandit on the Hunt: Dynamic Crawling for Cyber Threat Intelligence
Venue: arXiv cs.CR 2025
Year: 2025

Public information contains valuable Cyber Threat Intelligence (CTI) that is used to prevent future attacks. While standards exist for sharing this information, much appears in non-standardized news articles or blogs. Monitoring online sources for threats is time-consuming and source selection is uncertain. Current research focuses on extracting Indicators of Compromise from known sources, rarely addressing new source identification. This paper proposes a CTI-focused crawler using multi-armed bandit (MAB) and various crawling strategies. It employs SBERT to identify relevant documents while dynamically adapting its crawling path. Our system ThreatCrawl achieves a harvest rate exceeding 25% and expands its seed by over 300% while maintaining topical focus. Additionally, the crawler identifies previously unknown but highly relevant overview pages, datasets, and domains.
Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Cross-domain Sequential Recommendation (CDSR) aims to extract the preference from the user's historical interactions across various domains. Despite some progress in CDSR, two problems set the barrier for further advancements, i.e., overlap dilemma and transition complexity. The former means existing CDSR methods severely rely on users who own interactions on all domains to learn cross-domain item relationships, compromising the practicability. The latter refers to the difficulties in learning the complex transition patterns from the mixed behavior sequences. With powerful representation and reasoning abilities, Large Language Models (LLMs) are promising to address these two problems by bridging the items and capturing the user's preferences from a semantic view. Therefore, we propose an LLMs Enhanced Cross-domain Sequential Recommendation model (LLM4CDSR). To obtain the semantic item relationships, we first propose an LLM-based unified representation module to represent items. Then, a trainable adapter with contrastive regularization is designed to adapt the CDSR task. Besides, a hierarchical LLMs profiling module is designed to summarize user cross-domain preferences. Finally, these two modules are integrated into the proposed tri-thread framework to derive recommendations. We have conducted extensive experiments on three public cross-domain datasets, validating the effectiveness of LLM4CDSR. We have released the code online.
Three Types of Calibration with Properties and their Semantic and Formal Relationships
Venue: arXiv cs.LG 2025
Year: 2025

Fueled by discussions around "trustworthiness" and algorithmic fairness, calibration of predictive systems has regained scholars attention. The vanilla definition and understanding of calibration is, simply put, on all days on which the rain probability has been predicted to be p, the actual frequency of rain days was p. However, the increased attention has led to an immense variety of new notions of "calibration." Some of the notions are incomparable, serve different purposes, or imply each other. In this work, we provide two accounts which motivate calibration: self-realization of forecasted properties and precise estimation of incurred losses of the decision makers relying on forecasts. We substantiate the former via the reflection principle and the latter by actuarial fairness. For both accounts we formulate prototypical definitions via properties $Γ$ of outcome distributions, e.g., the mean or median. The prototypical definition for self-realization, which we call $Γ$-calibration, is equivalent to a certain type of swap regret under certain conditions. These implications are strongly connected to the omniprediction learning paradigm. The prototypical definition for precise loss estimation is a modification of decision calibration adopted from Zhao et al. [73]. For binary outcome sets both prototypical definitions coincide under appropriate choices of reference properties. For higher-dimensional outcome sets, both prototypical definitions can be subsumed by a natural extension of the binary definition, called distribution calibration with respect to a property. We conclude by commenting on the role of groupings in both accounts of calibration often used to obtain multicalibration. In sum, this work provides a semantic map of calibration in order to navigate a fragmented terrain of notions and definitions.
LLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection
Venue: arXiv cs.CR 2025
Year: 2025

Despite the transformative impact of Artificial Intelligence (AI) across various sectors, cyber security continues to rely on traditional static and dynamic analysis tools, hampered by high false positive rates and superficial code comprehension. While generative AI offers promising automation capabilities for software development, leveraging Large Language Models (LLMs) for vulnerability detection presents unique challenges. This paper explores the potential and limitations of LLMs in identifying vulnerabilities, acknowledging inherent weaknesses such as hallucinations, limited context length, and knowledge cut-offs. Previous attempts employing machine learning models for vulnerability detection have proven ineffective due to limited real-world applicability, feature engineering challenges, lack of contextual understanding, and the complexities of training models to keep pace with the evolving threat landscape. Therefore, we propose a robust AI-driven approach focused on mitigating these limitations and ensuring the quality and reliability of LLM based vulnerability detection. Through innovative methodologies combining Retrieval-Augmented Generation (RAG) and Mixtureof-Agents (MoA), this research seeks to leverage the strengths of LLMs while addressing their weaknesses, ultimately paving the way for dependable and efficient AI-powered solutions in securing the ever-evolving software landscape.
FlexiNS: A SmartNIC-Centric, Line-Rate and Flexible Network Stack
Venue: arXiv cs.NI 2025
Year: 2025

As the gap between network and CPU speeds rapidly increases, the CPU-centric network stack proves inadequate due to excessive CPU and memory overhead. While hardware-offloaded network stacks alleviate these issues, they suffer from limited flexibility in both control and data planes. Offloading network stack to off-path SmartNIC seems promising to provide high flexibility; however, throughput remains constrained by inherent SmartNIC architectural limitations. To this end, we design FlexiNS, a SmartNIC-centric network stack with software transport programmability and line-rate packet processing capabilities. To grapple with the limitation of SmartNIC-induced challenges, FlexiNS introduces: (a) a header-only offloading TX path; (b) an unlimited-working-set in-cache processing RX path; (c) a high-performance DMA-only notification pipe; and (d) a programmable offloading engine. We prototype FlexiNS using Nvidia BlueField-3 SmartNIC and provide out-of-the-box RDMA IBV verbs compatibility to users. FlexiNS achieves 2.2$\times$ higher throughput than the microkernel-based baseline in block storage disaggregation and 1.3$\times$ higher throughput than the hardware-offloaded baseline in KVCache transfer.
Iterative Event-based Motion Segmentation by Variational Contrast Maximization
Venue: arXiv cs.CV 2025
Year: 2025

Event cameras provide rich signals that are suitable for motion estimation since they respond to changes in the scene. As any visual changes in the scene produce event data, it is paramount to classify the data into different motions (i.e., motion segmentation), which is useful for various tasks such as object detection and visual servoing. We propose an iterative motion segmentation method, by classifying events into background (e.g., dominant motion hypothesis) and foreground (independent motion residuals), thus extending the Contrast Maximization framework. Experimental results demonstrate that the proposed method successfully classifies event clusters both for public and self-recorded datasets, producing sharp, motion-compensated edge-like images. The proposed method achieves state-of-the-art accuracy on moving object detection benchmarks with an improvement of over 30%, and demonstrates its possibility of applying to more complex and noisy real-world scenes. We hope this work broadens the sensitivity of Contrast Maximization with respect to both motion parameters and input events, thus contributing to theoretical advancements in event-based motion segmentation estimation. https://github.com/aoki-media-lab/event_based_segmentation_vcmax
Probabilistic Shaping in MIMO: Going Beyond 1.53dB AWGN Gain With the Non-Linear Demapper
Venue: arXiv cs.IT 2025
Year: 2025

Constellation shaping is a well-established method to improve upon a regular quadrature amplitude modulation (QAM). It is known that the gain achieved by any shaping method for an additive white Gaussian noise (AWGN) channel is upper-bounded by 1.53dB. However, the situation becomes less clear in the multiple-input and multiple-output (MIMO) setting. In this paper, we study the application of probabilistic shaping for MIMO channels. We utilize an efficient near-optimal demapper based on sphere decoding (SD) and demonstrate that it is possible to achieve more than 2dB gains, breaking the AWGN limit. It becomes possible because both signal and interference are shaped and the non-linear methods can capture this property and leverage on it to improve the demodulation performance.
Facets, Taxonomies, and Syntheses: Navigating Structured Representations in LLM-Assisted Literature Review
Venue: arXiv cs.HC 2025
Year: 2025

Comprehensive literature review requires synthesizing vast amounts of research -- a labor intensive and cognitively demanding process. Most prior work focuses either on helping researchers deeply understand a few papers (e.g., for triaging or reading), or retrieving from and visualizing a vast corpus. Deep analysis and synthesis of large paper collections (e.g., to produce a survey paper) is largely conducted manually with little support. We present DimInd, an interactive system that scaffolds literature review across large paper collections through LLM-generated structured representations. DimInd scaffolds literature understanding with multiple levels of compression, from papers, to faceted literature comparison tables with information extracted from individual papers, to taxonomies of concepts, to narrative syntheses. Users are guided through these successive information transformations while maintaining provenance to source text. In an evaluation with 23 researchers, DimInd supported participants in extracting information and conceptually organizing papers with less effort compared to a ChatGPT-assisted baseline workflow.
DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics
Venue: arXiv cs.CR 2025
Year: 2025

Empirical inference attacks are a popular approach for evaluating the privacy risk of data release mechanisms in practice. While an active attack literature exists to evaluate machine learning models or synthetic data release, we currently lack comparable methods for fixed aggregate statistics, in particular when only a limited number of statistics are released. We here propose an inference attack framework against fixed aggregate statistics and an attribute inference attack called DeSIA. We instantiate DeSIA against the U.S. Census PPMF dataset and show it to strongly outperform reconstruction-based attacks. In particular, we show DeSIA to be highly effective at identifying vulnerable users, achieving a true positive rate of 0.14 at a false positive rate of $10^{-3}$. We then show DeSIA to perform well against users whose attributes cannot be verified and when varying the number of aggregate statistics and level of noise addition. We also perform an extensive ablation study of DeSIA and show how DeSIA can be successfully adapted to the membership inference task. Overall, our results show that aggregation alone is not sufficient to protect privacy, even when a relatively small number of aggregates are being released, and emphasize the need for formal privacy mechanisms and testing before aggregate statistics are released.