OpenRaiser/Intern-Atlas huggingface dataset:
CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching
Venue: arXiv cs.CV 2025
Year: 2025

Surgical image segmentation is highly challenging, primarily due to scarcity of annotated data. Generalist prompted segmentation models like the Segment-Anything Model (SAM) can help tackle this task, but because they require image-specific visual prompts for effective performance, their use is limited to improving data annotation efficiency. Recent approaches extend SAM to automatic segmentation by using a few labeled reference images to predict point prompts; however, they rely on feature matching pipelines that lack robustness to out-of-domain data like surgical images. To tackle this problem, we introduce CycleSAM, an improved visual prompt learning approach that employs a data-efficient training phase and enforces a series of soft constraints to produce high-quality feature similarity maps. CycleSAM label-efficiently addresses domain gap by leveraging surgery-specific self-supervised feature extractors, then adapts the resulting features through a short parameter-efficient training stage, enabling it to produce informative similarity maps. CycleSAM further filters the similarity maps with a series of consistency constraints before robustly sampling diverse point prompts for each object instance. In our experiments on four diverse surgical datasets, we find that CycleSAM outperforms existing few-shot SAM approaches by a factor of 2-4x in both 1-shot and 5-shot settings, while also achieving strong performance gains over traditional linear probing, parameter-efficient adaptation, and pseudo-labeling methods.
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data
Venue: arXiv cs.LG 2025
Year: 2025

As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. This real-world data is complex in many ways and requires the modelling of testee behaviour. To address this, we propose a temporal variational autoencoder (TeVAE) that can detect anomalies with minimal false positives when trained on unlabelled data. Our approach also avoids the bypass phenomenon and introduces a new method to remap individual windows to a continuous time series. Furthermore, we propose metrics to evaluate the detection delay and root-cause capability of our approach and present results from experiments on a real-world industrial data set. When properly configured, TeVAE flags anomalies only 6% of the time wrongly and detects 65% of anomalies present. It also has the potential to perform well with a smaller training and validation subset but requires a more sophisticated threshold estimation method.
DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Frugal Resource Utilization
Venue: arXiv cs.IT 2025
Year: 2025

In this work, we propose a method for efficient resource utilization of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) elements to ensure fair and high data rates. We introduce a subsurface assignment variable that determines the number of STAR-RIS elements allocated to each user and maximizes the sum of the data rates by jointly optimizing the phase shifts of the STAR-RIS and the subsurface assignment variables using an appropriately tailored deep reinforcement learning (DRL) algorithm. The proposed DRL method is also compared with a Dinkelbach algorithm and the designed hybrid DRL approach. A penalty term is incorporated into the DRL model to enhance resource utilization by intelligently deactivating STAR-RIS elements when not required. The proposed DRL method can achieve fair and high data rates for static and mobile users while ensuring efficient resource utilization through extensive simulations. Using the proposed DRL method, up to 27% and 21% of STAR-RIS elements can be deactivated in static and mobile scenarios, respectively, without affecting performance.
An automata-based approach for synchronizable mailbox communication
Venue: arXiv cs.LO 2025
Year: 2025

We revisit finite-state communicating systems with round-based communication under mailbox semantics. Mailboxes correspond to one FIFO buffer per process (instead of one buffer per pair of processes in peer-to-peer systems). Round-based communication corresponds to sequences of rounds in which processes can first send messages, then only receive (and receives must be in the same round as their sends). A system is called synchronizable if every execution can be re-scheduled into an equivalent execution that is a sequence of rounds. Previous work mostly considered the setting where rounds have fixed size. Our main contribution shows that the problem whether a mailbox communication system complies with the round-based policy, with no size limitation on rounds, is Pspace-complete. For this we use a novel automata-based approach, that also allows to determine the precise complexity (Pspace) of several questions considered in previous literature.
Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have shown a remarkable ability to learn and perform complex tasks through in-context learning (ICL). However, a comprehensive understanding of its internal mechanisms is still lacking. This paper explores the role of induction heads in a few-shot ICL setting. We analyse two state-of-the-art models, Llama-3-8B and InternLM2-20B on abstract pattern recognition and NLP tasks. Our results show that even a minimal ablation of induction heads leads to ICL performance decreases of up to ~32% for abstract pattern recognition tasks, bringing the performance close to random. For NLP tasks, this ablation substantially decreases the model's ability to benefit from examples, bringing few-shot ICL performance close to that of zero-shot prompts. We further use attention knockout to disable specific induction patterns, and present fine-grained evidence for the role that the induction mechanism plays in ICL.
Counting Small Induced Subgraphs: Hardness via Fourier Analysis
Venue: arXiv cs.CC 2025
Year: 2025

For a fixed graph property $Φ$ and integer $k \geq 1$, consider the problem of counting the induced $k$-vertex subgraphs satisfying $Φ$ in an input graph $G$. This problem can be solved by brute-force in time $O(n^{k})$. Under ETH, we prove several lower bounds on the optimal exponent in this running time: If $Φ$ is edge-monotone (i.e., closed under deleting edges), then ETH rules out $n^{o(k)}$ time algorithms for this problem. This strengthens a recent lower bound by Döring, Marx and Wellnitz [STOC 2024]. Our result also holds for counting modulo fixed primes. If at most $(2-\varepsilon)^{\binom{k}{2}}$ graphs on $k$ vertices satisfy $Φ$, for some $\varepsilon > 0$, then ETH also rules out an exponent of $o(k)$. This holds even when the graphs in $Φ$ have arbitrary individual weights, generalizing previous results for hereditary properties by Focke and Roth [SIAM J. Comput. 2024]. If $Φ$ is non-trivial and excludes $β_Φ$ edge-densities, then the optimal exponent under ETH is $Ω(β_Φ)$. This holds even when the graphs in $Φ$ have arbitrary individual weights, generalizing previous results by Roth, Schmitt and Wellnitz [SIAM J. Comput. 2024]. In all cases, we also obtain $\mathsf{\#W[1]}$-hardness if $k$ is part of the input and considered as the parameter. We also obtain lower bounds on the Weisfeiler-Leman dimension. As opposed to the nontrivial techniques from combinatorics, group theory, and simplicial topology used before, our results follow from a relatively straightforward ``algebraization'' of the problem in terms of polynomials, combined with applications of simple algebraic facts, which can also be interpreted in terms of Fourier analysis.
Prompting Techniques for Secure Code Generation: A Systematic Investigation
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) are gaining momentum in software development with prompt-driven programming enabling developers to create code from natural language (NL) instructions. However, studies have questioned their ability to produce secure code and, thereby, the quality of prompt-generated software. Alongside, various prompting techniques that carefully tailor prompts have emerged to elicit optimal responses from LLMs. Still, the interplay between such prompting strategies and secure code generation remains under-explored and calls for further investigations. OBJECTIVE: In this study, we investigate the impact of different prompting techniques on the security of code generated from NL instructions by LLMs. METHOD: First we perform a systematic literature review to identify the existing prompting techniques that can be used for code generation tasks. A subset of these techniques are evaluated on GPT-3, GPT-3.5, and GPT-4 models for secure code generation. For this, we used an existing dataset consisting of 150 NL security-relevant code-generation prompts. RESULTS: Our work (i) classifies potential prompting techniques for code generation (ii) adapts and evaluates a subset of the identified techniques for secure code generation tasks and (iii) observes a reduction in security weaknesses across the tested LLMs, especially after using an existing technique called Recursive Criticism and Improvement (RCI), contributing valuable insights to the ongoing discourse on LLM-generated code security.
Learning treatment effects while treating those in need
Venue: arXiv cs.LG 2025
Year: 2025

Many social programs attempt to allocate scarce resources to people with the greatest need. Indeed, public services increasingly use algorithmic risk assessments motivated by this goal. However, targeting the highest-need recipients often conflicts with attempting to evaluate the causal effect of the program as a whole, as the best evaluations would be obtained by randomizing the allocation. We propose a framework to design randomized allocation rules which optimally balance targeting high-need individuals with learning treatment effects, presenting policymakers with a Pareto frontier between the two goals. We give sample complexity guarantees for the policy learning problem and provide a computationally efficient strategy to implement it. We then collaborate with the human services department of Allegheny County, Pennsylvania to evaluate our methods on data from real service delivery settings. Optimized policies can substantially mitigate the tradeoff between learning and targeting. For example, it is often possible to obtain 90% of the optimal utility in targeting high-need individuals while ensuring that the average treatment effect can be estimated with less than 2 times the samples that a randomized controlled trial would require. Mechanisms for targeting public services often focus on measuring need as accurately as possible. However, our results suggest that algorithmic systems in public services can be most impactful if they incorporate program evaluation as an explicit goal alongside targeting.
iiANET: Inception Inspired Attention Hybrid Network for efficient Long-Range Dependency
Venue: arXiv cs.CV 2025
Year: 2025

The recent emergence of hybrid models has introduced a transformative approach to computer vision, gradually moving beyond conventional convolutional neural net-works and vision transformers. However, efficiently combining these two paradigms to better capture long-range dependencies in complex images remains a challenge. In this paper, we present iiANET (Inception Inspired Attention Network), an efficient hybrid visual backbone designed to improve the modeling of long-range dependen-cies. The core innovation of iiANET is the iiABlock, a unified building block that in-tegrates global r-MHSA (Multi-Head Self-Attention) and convolutional layers in paral-lel. This design enables iiABlock to simultaneously capture global context and local details, making it highly effective for extracting rich and diverse features. By effi-ciently fusing these complementary representations, iiABlock allows iiANET to achieve strong feature interaction while maintaining computational efficiency. Exten-sive qualitative and quantitative evaluations across various benchmarks show im-proved performance over several state-of-the-art models.
An in-depth study of the power function $x^{q+2}$ over the finite field $\mathbb{F}_{q^2}$: the differential, boomerang, and Walsh spectra, with an application to coding theory
Venue: arXiv cs.CR 2025
Year: 2025

Let $q = p^m$, where $p$ is an odd prime number and $m$ is a positive integer. In this paper, we examine the finite field $\mathbb{F}_{q^2}$, which consists of $q^2$ elements. We first present an alternative method to determine the differential spectrum of the power function $f(x) = x^{q+2}$ on $\mathbb{F}_{q^2}$, incorporating several key simplifications. This methodology provides a new proof of the results established by Man, Xia, Li, and Helleseth in Finite Fields and Their Applications 84 (2022), 102100, which not only completely determine the differential spectrum of $f$ but also facilitate the analysis of its boomerang uniformity. Specifically, we determine the boomerang uniformity of $f$ for the cases where $q \equiv 1$ or $3$ (mod $6$), with the exception of the scenario where $p = 5$ and $m$ is even. Furthermore, for $p = 3$, we investigate the value distribution of the Walsh spectrum of $f$, demonstrating that it takes on only four distinct values. Using this result, we derive the weight distribution of a ternary cyclic code with four Hamming weights. The article integrates refined mathematical techniques from algebraic number theory and the theory of finite fields, employing several ingredients, such as exponential sums, to explore the cryptographic analysis of functions over finite fields. They can be used to explore the differential/boomerang uniformity across a wider range of functions.
Text2VP: Generative AI for Visual Programming and Parametric Modeling
Venue: arXiv cs.HC 2025
Year: 2025

The integration of generative artificial intelligence (AI) into architectural design has advanced significantly, enabling the generation of text, images, and 3D models. However, prior AI applications lack support for text-to-parametric models, essential for generating and optimizing diverse parametric design options. This study introduces Text-to-Visual Programming (Text2VP) GPT, a novel generative AI derived from GPT-4.1, designed to automate graph-based visual programming workflows, parameters, and their interconnections. Text2VP leverages detailed documentation, specific instructions, and example-driven few-shot learning to reflect user intentions accurately and facilitate interactive parameter adjustments. Testing demonstrates Text2VP's capability in generating functional parametric models, although higher complexity models present increased error rates. This research highlights generative AI's potential in visual programming and parametric modeling, laying groundwork for future improvements to manage complex modeling tasks. Ultimately, Text2VP aims to enable designers to easily create and modify parametric models without extensive training in specialized platforms like Grasshopper.
Functional Type Expressions of Sequential Circuits with the Notion of Referring Forms
Venue: arXiv cs.AR 2025
Year: 2025

This paper introduces the notion of referring forms as a new metric for analyzing sequential circuits from a functional perspective. Sequential circuits are modeled as causal stream functions, the outputs of which depend solely on the past and current inputs. Referring forms are defined based on the type expressions of functions and represent how a circuit refers to past inputs. The key contribution of this study is identifying a universal property in multiple clock domain circuits using referring forms. This theoretical framework is expected to enhance the comprehension and analysis of sequential circuits.
Model-agnostic clean-label backdoor mitigation in cybersecurity environments
Venue: arXiv cs.CR 2025
Year: 2025

The training phase of machine learning models is a delicate step, especially in cybersecurity contexts. Recent research has surfaced a series of insidious training-time attacks that inject backdoors in models designed for security classification tasks without altering the training labels. With this work, we propose new techniques that leverage insights in cybersecurity threat models to effectively mitigate these clean-label poisoning attacks, while preserving the model utility. By performing density-based clustering on a carefully chosen feature subspace, and progressively isolating the suspicious clusters through a novel iterative scoring procedure, our defensive mechanism can mitigate the attacks without requiring many of the common assumptions in the existing backdoor defense literature. To show the generality of our proposed mitigation, we evaluate it on two clean-label model-agnostic attacks on two different classic cybersecurity data modalities: network flows classification and malware classification, using gradient boosting and neural network models.
The Approximate Fisher Influence Function: Faster Estimation of Data Influence in Statistical Models
Venue: arXiv cs.LG 2025
Year: 2025

Quantifying the influence of infinitesimal changes in training data on model performance is crucial for understanding and improving machine learning models. In this work, we reformulate this problem as a weighted empirical risk minimization and enhance existing influence function-based methods by using information geometry to derive a new algorithm to estimate influence. Our formulation proves versatile across various applications, and we further demonstrate in simulations how it remains informative even in non-convex cases. Furthermore, we show that our method offers significant computational advantages over current Newton step-based methods.
Global Patterns of Viral Content on WhatsApp
Venue: arXiv cs.SI 2025
Year: 2025

This paper explores the nature and spread of viral WhatsApp content among everyday users in three diverse countries: India, Indonesia, and Colombia. By analyzing hundreds of viral messages collected with participants' consent from private WhatsApp groups, we provide one of the first cross-cultural categorizations of viral content on WhatsApp. Despite the differences in cultural and geographic settings, our findings reveal striking similarities in the types of groups users engage with and the viral content they receive, particularly in the prevalence of misinformation. Our comparative analysis shows that viral content often includes political and religious narratives, with misinformation frequently recirculated despite prior debunking by fact-checking organizations. These parallels suggest that closed messaging platforms like WhatsApp facilitate similar patterns of information dissemination across different cultural contexts. This work contributes to the broader understanding of global digital communication ecosystems and provides a foundation for future research on information flow and moderation strategies in private messaging platforms.
Preventing Radio Fingerprinting through Low-Power Jamming
Venue: arXiv cs.CR 2025
Year: 2025

Radio Frequency fingerprinting enables a passive receiver to recognize and authenticate a transmitter without the need for cryptographic tools. Authentication is achieved by isolating specific features of the transmitted signal that are unique to the transmitter's hardware. Much research has focused on improving the effectiveness and efficiency of radio frequency fingerprinting to maximize its performance in various scenarios and conditions, while little research examined how to protect devices from being subject to radio fingerprinting in the wild. In this paper, we explore a novel point of view. We examine the threat posed by radio frequency fingerprinting, which facilitates the unauthorized identification of wireless devices in the field by malicious entities. We also suggest a method to sanitize the transmitted signal of its fingerprint using a low-power jammer, deployed on purpose to improve devices' anonymity on the channel while still guaranteeing the link's quality of service. Our experimental results and subsequent analysis demonstrate that a low-power jammer can effectively block a malicious eavesdropper from identifying a device without affecting the quality of the wireless link, thereby restoring the privacy of the user when accessing the radio spectrum.
Approximate Degree Composition for Recursive Functions
Venue: arXiv cs.CC 2025
Year: 2025

Determining the approximate degree composition for Boolean functions remains a significant unsolved problem in Boolean function complexity. In recent decades, researchers have concentrated on proving that approximate degree composes for special types of inner and outer functions. An important and extensively studied class of functions are the recursive functions, i.e.~functions obtained by composing a base function with itself a number of times. Let $h^d$ denote the standard $d$-fold composition of the base function $h$. The main result of this work is to show that the approximate degree composes if either of the following conditions holds: (I) The outer function $f:\{0,1\}^n\to \{0,1\}$ is a recursive function of the form $h^d$, with $h$ being any base function and $d= Ω(\log\log n)$. (II) The inner function is a recursive function of the form $h^d$, with $h$ being any constant arity base function (other than AND and OR) and $d= Ω(\log\log n)$, where $n$ is the arity of the outer function. In terms of proof techniques, we first observe that the lower bound for composition can be obtained by introducing majority in between the inner and the outer functions. We then show that majority can be \emph{efficiently eliminated} if the inner or outer function is a recursive function.
Generalizable Implicit Motion Modeling for Video Frame Interpolation
Venue: arXiv cs.CV 2025
Year: 2025

Motion modeling is critical in flow-based Video Frame Interpolation (VFI). Existing paradigms either consider linear combinations of bidirectional flows or directly predict bilateral flows for given timestamps without exploring favorable motion priors, thus lacking the capability of effectively modeling spatiotemporal dynamics in real-world videos. To address this limitation, in this study, we introduce Generalizable Implicit Motion Modeling (GIMM), a novel and effective approach to motion modeling for VFI. Specifically, to enable GIMM as an effective motion modeling paradigm, we design a motion encoding pipeline to model spatiotemporal motion latent from bidirectional flows extracted from pre-trained flow estimators, effectively representing input-specific motion priors. Then, we implicitly predict arbitrary-timestep optical flows within two adjacent input frames via an adaptive coordinate-based neural network, with spatiotemporal coordinates and motion latent as inputs. Our GIMM can be easily integrated with existing flow-based VFI works by supplying accurately modeled motion. We show that GIMM performs better than the current state of the art on standard VFI benchmarks.