OpenRaiser/Intern-Atlas huggingface dataset:
SciClaimHunt: A Large Dataset for Evidence-based Scientific Claim Verification
Venue: arXiv cs.CL 2025
Year: 2025

Verifying scientific claims presents a significantly greater challenge than verifying political or news-related claims. Unlike the relatively broad audience for political claims, the users of scientific claim verification systems can vary widely, ranging from researchers testing specific hypotheses to everyday users seeking information on a medication. Additionally, the evidence for scientific claims is often highly complex, involving technical terminology and intricate domain-specific concepts that require specialized models for accurate verification. Despite considerable interest from the research community, there is a noticeable lack of large-scale scientific claim verification datasets to benchmark and train effective models. To bridge this gap, we introduce two large-scale datasets, SciClaimHunt and SciClaimHunt_Num, derived from scientific research papers. We propose several baseline models tailored for scientific claim verification to assess the effectiveness of these datasets. Additionally, we evaluate models trained on SciClaimHunt and SciClaimHunt_Num against existing scientific claim verification datasets to gauge their quality and reliability. Furthermore, we conduct human evaluations of the claims in proposed datasets and perform error analysis to assess the effectiveness of the proposed baseline models. Our findings indicate that SciClaimHunt and SciClaimHunt_Num serve as highly reliable resources for training models in scientific claim verification.
Discovering Polynomial and Quadratic Structure in Nonlinear Ordinary Differential Equations
Venue: arXiv cs.SC 2025
Year: 2025

Dynamical systems with quadratic or polynomial drift exhibit complex dynamics, yet compared to nonlinear systems in general form, are often easier to analyze, simulate, control, and learn. Results going back over a century have shown that the majority of nonpolynomial nonlinear systems can be recast in polynomial form, and their degree can be reduced further to quadratic. This process of polynomialization/quadratization reveals new variables (in most cases, additional variables have to be added to achieve this) in which the system dynamics adhere to that specific form, which leads us to discover new structures of a model. This chapter summarizes the state of the art for the discovery of polynomial and quadratic representations of finite-dimensional dynamical systems. We review known existence results, discuss the two prevalent algorithms for automating the discovery process, and give examples in form of a single-layer neural network and a phenomenological model of cell signaling.
Unlocking Efficient Vehicle Dynamics Modeling via Analytic World Models
Venue: arXiv cs.AI 2025
Year: 2025

Differentiable simulators represent an environment's dynamics as a differentiable function. Within robotics and autonomous driving, this property is used in Analytic Policy Gradients (APG), which relies on backpropagating through the dynamics to train accurate policies for diverse tasks. Here we show that differentiable simulation also has an important role in world modeling, where it can impart predictive, prescriptive, and counterfactual capabilities to an agent. Specifically, we design three novel task setups in which the differentiable dynamics are combined within an end-to-end computation graph not with a policy, but a state predictor. This allows us to learn relative odometry, optimal planners, and optimal inverse states. We collectively call these predictors Analytic World Models (AWMs) and demonstrate how differentiable simulation enables their efficient, end-to-end learning. In autonomous driving scenarios, they have broad applicability and can augment an agent's decision-making beyond reactive control.
Scaling Law Tradeoff Between Throughput and Sensing Distance in Large ISAC Networks
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we investigate the fundamental tradeoff between communication and sensing performance of \emph{ad hoc} integrated sensing and communication (ISAC) wireless networks. Specifically, we consider that $n$ nodes are randomly located in an extended network with area $n$ and transmit ISAC signals. Under the pure path loss channel gain model and the condition that the transmission power scales according to the communication distance, we fully characterize the optimal scaling law tradeoff between throughput and sensing distance by proposing an achievable scheme and proving its converse. Our results can be interpreted as follows: by reducing the throughput by a factor of a function of $n$, the sensing range order improves according to the same function of $n$, raised to the power of the ratio between the path loss factors in communication and sensing. We prove that the same result also holds true for ISAC networks with random fading, despite the uncertainty on the connectivity and power level created by random fading. In addition, we show that the scaling law tradeoff cannot be improved by allowing the transmission power and communication distance to scale freely. To the best of our knowledge, this is the first work formally formulating and characterizing the communication and sensing performance scaling law tradeoff of \emph{ad hoc} ISAC networks.
Towards Polyp Counting In Full-Procedure Colonoscopy Videos
Venue: arXiv cs.CV 2025
Year: 2025

Automated colonoscopy reporting holds great potential for enhancing quality control and improving cost-effectiveness of colonoscopy procedures. A major challenge lies in the automated identification, tracking, and re-association (ReID) of polyps tracklets across full-procedure colonoscopy videos. This is essential for precise polyp counting and enables automated computation of key quality metrics, such as Adenoma Detection Rate (ADR) and Polyps Per Colonoscopy (PPC). However, polyp ReID is challenging due to variations in polyp appearance, frequent disappearance from the field of view, and occlusions. In this work, we leverage the REAL-Colon dataset, the first open-access dataset providing full-procedure videos, to define tasks, data splits and metrics for the problem of automatically count polyps in full-procedure videos, establishing an open-access framework. We re-implement previously proposed SimCLR-based methods for learning representations of polyp tracklets, both single-frame and multi-view, and adapt them to the polyp counting task. We then propose an Affinity Propagation-based clustering method to further improve ReID based on these learned representations, ultimately enhancing polyp counting. Our approach achieves state-of-the-art performance, with a polyp fragmentation rate of 6.30 and a false positive rate (FPR) below 5% on the REAL-Colon dataset. We release code at https://github.com/lparolari/towards-polyp-counting.
Breaking Symmetries from a Set-Covering Perspective
Venue: arXiv cs.LO 2025
Year: 2025

We formalize symmetry breaking as a set-covering problem. For the case of breaking symmetries on graphs, a permutation covers a graph if applying it to the graph yields a smaller graph in a given order. Canonical graphs are those that cannot be made smaller by any permutation. A complete symmetry break is then a set of permutations that covers all non-canonical graphs. A complete symmetry break with a minimal number of permutations can be obtained by solving an optimal set-covering problem. The challenge is in the sizes of the corresponding set-covering problems and in how these can be tamed. The set-covering perspective on symmetry breaking opens up a range of new opportunities deriving from decades of studies on both precise and approximate techniques for this problem. Application of our approach leads to optimal LexLeader symmetry breaks for graphs of order $n\leq 10$ as well as to partial symmetry breaks which improve on the state-of-the-art.
Strassen Multisystolic Array Hardware Architectures
Venue: arXiv cs.AR 2025
Year: 2025

While Strassen's matrix multiplication algorithm reduces the complexity of naive matrix multiplication, general-purpose hardware is not suitable for achieving the algorithm's promised theoretical speedups. This leaves the question of if it could be better exploited in custom hardware architectures designed specifically for executing the algorithm. However, there is limited prior work on this and it is not immediately clear how to derive such architectures or if they can ultimately lead to real improvements. We bridge this gap, presenting and evaluating new systolic array architectures that efficiently translate the theoretical complexity reductions of Strassen's algorithm directly into hardware resource savings. Furthermore, the architectures are multisystolic array designs that can multiply smaller matrices with higher utilization than single-systolic array designs. The proposed designs implemented on FPGA reduce DSP requirements by a factor of $1.14^r$ for $r$ implemented Strassen recursion levels, and otherwise require overall similar soft logic resources when instantiated to support matrix sizes down to 32x32 and 24x24 at 1-2 levels of Strassen recursion, respectively. We evaluate the proposed designs both in isolation and in an end-to-end machine learning accelerator compared to baseline designs and prior works, achieving state-of-the-art performance.
Anthemius: Efficient & Modular Block Assembly for Concurrent Execution
Venue: arXiv cs.DC 2025
Year: 2025

Many blockchains such as Ethereum execute all incoming transactions sequentially significantly limiting the potential throughput. A common approach to scale execution is parallel execution engines that fully utilize modern multi-core architectures. Parallel execution is then either done optimistically, by executing transactions in parallel and detecting conflicts on the fly, or guided, by requiring exhaustive client transaction hints and scheduling transactions accordingly. However, recent studies have shown that the performance of parallel execution engines depends on the nature of the underlying workload. In fact, in some cases, only a 60% speed-up compared to sequential execution could be obtained. This is the case, as transactions that access the same resources must be executed sequentially. For example, if 10% of the transactions in a block access the same resource, the execution cannot meaningfully scale beyond 10 cores. Therefore, a single popular application can bottleneck the execution and limit the potential throughput. In this paper, we introduce Anthemius, a block construction algorithm that optimizes parallel transaction execution throughput. We evaluate Anthemius exhaustively under a range of workloads, and show that Anthemius enables the underlying parallel execution engine to process over twice as many transactions.
Coordinated control of multiple autonomous surface vehicles: challenges and advances -- a systematic review
Venue: arXiv cs.RO 2025
Year: 2025

The increasing use and implementation of Autonomous Surface Vessels (ASVs) for various activities in maritime environments is expected to drive a rise in developments and research on their control. Particularly, the coordination of multiple ASVs presents novel challenges and opportunities, requiring interdisciplinary research efforts at the intersection of robotics, control theory, communication systems, and marine sciences. The wide variety of missions or objectives for which these vessels can be collectively used allows for the application and combination of different control techniques. This includes the exploration of machine learning to consider aspects previously deemed infeasible. This review provides a comprehensive exploration of coordinated ASV control while addressing critical gaps left by previous reviews. Unlike previous works, we adopt a systematic approach to ensure integrity and minimize bias in article selection. We delve into the complex world of sub-actuated ASVs with a focus on customized control strategies and the integration of machine learning techniques for increased autonomy. By synthesizing recent advances and identifying emerging trends, we offer insights that drive this field forward, providing both a comprehensive overview of state-of-the-art techniques and guidance for future research efforts.
A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, the development of smart edge computing systems to process information locally is on the rise. Many near-sensor machine learning (ML) approaches have been implemented to introduce accurate and energy efficient template matching operations in resource-constrained edge sensing systems, such as wearables. To introduce novel solutions that can be viable for extreme edge cases, hybrid solutions combining conventional and emerging technologies have started to be proposed. Deep Neural Networks (DNN) optimised for edge application alongside new approaches of computing (both device and architecture -wise) could be a strong candidate in implementing edge ML solutions that aim at competitive accuracy classification while using a fraction of the power of conventional ML solutions. In this work, we are proposing a hybrid software-hardware edge classifier aimed at the extreme edge near-sensor systems. The classifier consists of two parts: (i) an optimised digital tinyML network, working as a front-end feature extractor, and (ii) a back-end RRAM-CMOS analogue content addressable memory (ACAM), working as a final stage template matching system. The combined hybrid system exhibits a competitive trade-off in accuracy versus energy metric with $E_{front-end}$ = $96.23 nJ$ and $E_{back-end}$ = $1.45 nJ$ for each classification operation compared with 78.06$μ$J for the original teacher model, representing a 792-fold reduction, making it a viable solution for extreme edge applications.
A novel approach to data generation in generative model
Venue: arXiv cs.LG 2025
Year: 2025

Variational Autoencoders (VAEs) and other generative models are widely employed in artificial intelligence to synthesize new data. However, current approaches rely on Euclidean geometric assumptions and statistical approximations that fail to capture the structured and emergent nature of data generation. This paper introduces the Convergent Fusion Paradigm (CFP) theory, a novel geometric framework that redefines data generation by integrating dimensional expansion accompanied by qualitative transformation. By modifying the latent space geometry to interact with emergent high-dimensional structures, CFP theory addresses key challenges such as identifiability issues and unintended artifacts like hallucinations in Large Language Models (LLMs). CFP theory is based on two key conceptual hypotheses that redefine how generative models structure relationships between data and algorithms. Through the lens of CFP theory, we critically examine existing metric-learning approaches. CFP theory advances this perspective by introducing time-reversed metric embeddings and structural convergence mechanisms, leading to a novel geometric approach that better accounts for data generation as a structured epistemic process. Beyond its computational implications, CFP theory provides philosophical insights into the ontological underpinnings of data generation. By offering a systematic framework for high-dimensional learning dynamics, CFP theory contributes to establishing a theoretical foundation for understanding the data-relationship structures in AI. Finally, future research in CFP theory will be led to its implications for fully realizing qualitative transformations, introducing the potential of Hilbert space in generative modeling.
Membership and Conjugacy in Inverse Semigroups
Venue: arXiv cs.CC 2025
Year: 2025

The membership problem for an algebraic structure asks whether a given element is contained in some substructure, which is usually given by generators. In this work we study the membership problem, as well as the conjugacy problem, for finite inverse semigroups. The closely related membership problem for finite semigroups has been shown to be PSPACE-complete in the transformation model by Kozen (1977) and NL-complete in the Cayley table model by Jones, Lien, and Laaser (1976). In the partial bijection model, the membership and the conjugacy problem for finite inverse semigroups were shown to be PSPACE-complete by Birget and Margolis (2008) and by Jack (2023). Here we present a more detailed analysis of the complexity of the membership and conjugacy problems parametrized by varieties of finite inverse semigroups. We establish dichotomy theorems for the partial bijection model and for the Cayley table model. In the partial bijection model these problems are in NC (resp. NP for conjugacy) for strict inverse semigroups and PSPACE-complete otherwise. In the Cayley table model we obtain general LOGSPACE-algorithms as well as NPOLYLOGTIME upper bounds for Clifford semigroups and LOGSPACE-completeness otherwise. Furthermore, by applying our findings, we show the following: the intersection non-emptiness problem for inverse automata is PSPACE-complete even for automata with only two states; the subpower membership problem is in NC for every strict inverse semi-group and PSPACE-complete otherwise; the minimum generating set and the equation satisfiability problems are in NP for varieties of finite strict inverse semigroups and PSPACE-complete otherwise.
Modeling the Impact of Visual Stimuli on Redirection Noticeability with Gaze Behavior in Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

While users could embody virtual avatars that mirror their physical movements in Virtual Reality, these avatars' motions can be redirected to enable novel interactions. Excessive redirection, however, could break the user's sense of embodiment due to perceptual conflicts between vision and proprioception. While prior work focused on avatar-related factors influencing the noticeability of redirection, we investigate how the visual stimuli in the surrounding virtual environment affect user behavior and, in turn, the noticeability of redirection. Given the wide variety of different types of visual stimuli and their tendency to elicit varying individual reactions, we propose to use users' gaze behavior as an indicator of their response to the stimuli and model the noticeability of redirection. We conducted two user studies to collect users' gaze behavior and noticeability, investigating the relationship between them and identifying the most effective gaze behavior features for predicting noticeability. Based on the data, we developed a regression model that takes users' gaze behavior as input and outputs the noticeability of redirection. We then conducted an evaluation study to test our model on unseen visual stimuli, achieving an accuracy of 0.012 MSE. We further implemented an adaptive redirection technique and conducted a proof-of-concept study to evaluate its effectiveness with complex visual stimuli in two applications. The results indicated that participants experienced less physical demanding and a stronger sense of body ownership when using our adaptive technique, demonstrating the potential of our model to support real-world use cases.
"It's Like Not Being Able to Read and Write": Narrowing the Digital Divide for Older Adults and Leveraging the Role of Digital Educators in Ireland
Venue: arXiv cs.CR 2025
Year: 2025

As digital services increasingly replace traditional analogue systems, ensuring that older adults are not left behind is critical to fostering inclusive access. This study explores how digital educators support older adults in developing essential digital skills, drawing insights from interviews with $34$ educators in Ireland. These educators, both professional and volunteer, offer instruction through a range of formats, including workshops, remote calls, and in-person sessions. Our findings highlight the importance of personalized, step-by-step guidance tailored to older adults' learning needs, as well as fostering confidence through hands-on engagement with technology. Key challenges identified include limited transportation options, poor internet connectivity, outdated devices, and a lack of familial support for learning. To address these barriers, we propose enhanced public funding, expanded access to resources, and sustainable strategies such as providing relevant and practical course materials. Additionally, innovative tools like simulated online platforms for practicing digital transactions can help reduce anxiety and enhance digital literacy among older adults. This study underscores the vital role that digital educators play in bridging the digital divide, creating a more inclusive, human-centered approach to digital learning for older adults.
Modeling and Simulating Emerging Memory Technologies: A Tutorial
Venue: arXiv cs.AR 2025
Year: 2025

Non-volatile Memory (NVM) technologies present a promising alternative to traditional volatile memories such as SRAM and DRAM. Due to the limited availability of real NVM devices, simulators play a crucial role in architectural exploration and hardware-software co-design. This tutorial presents a simulation toolchain through four detailed case studies, showcasing its applicability to various domains of system design, including hybrid main-memory and cache, compute-in-memory, and wear-leveling design. These case studies provide the reader with practical insights on customizing the toolchain for their specific research needs. The source code is open-sourced.
From Markov to Laplace: How Mamba In-Context Learns Markov Chains
Venue: arXiv cs.LG 2025
Year: 2025

While transformer-based language models have driven the AI revolution thus far, their computational complexity has spurred growing interest in viable alternatives, such as structured state space sequence models (SSMs) and Selective SSMs. Among these, Mamba (S6) and its variant Mamba-2 have shown remarkable inference speed ups over transformers while achieving comparable or superior performance on complex language modeling tasks. However, despite these architectural innovations and empirical successes, the fundamental learning capabilities of Mamba remain poorly understood. In this paper, we address this gap by studying in-context learning (ICL) on Markov chains and uncovering a surprising phenomenon: unlike transformers, even a single-layer Mamba efficiently learns the in-context Laplacian smoothing estimator, which is both Bayes and minimax optimal, for all Markovian orders. To explain this, we theoretically characterize the representation capacity of Mamba and reveal the fundamental role of convolution in enabling it to represent the optimal Laplacian smoothing. These theoretical insights align strongly with empirical results and, to the best of our knowledge, represent the first formal connection between Mamba and optimal statistical estimators. Finally, we outline promising research directions inspired by these findings.