OpenRaiser/Intern-Atlas huggingface dataset:
"Everyone's using it, but no one is allowed to talk about it": College Students' Experiences Navigating the Higher Education Environment in a Generative AI World
Venue: arXiv cs.CY 2026
Year: 2026

Higher education students are increasingly using generative AI in their academic work. However, existing institutional practices have not yet adapted to this shift. Through semi-structured interviews with 23 college students, our study examines the environmental and social factors that influence students' use of AI. Findings show that institutional pressure factors like deadlines, exam cycles, and grading lead students to engage with AI even when they think it undermines their learning. Social influences, particularly peer micro-communities, establish de-facto AI norms regardless of official AI policies. Campus-wide ``AI shame'' is prevalent, often pushing AI use underground. Current institutional AI policies are perceived as generic, inconsistent, and confusing, resulting in routine noncompliance. Additionally, students develop value-based self-regulation strategies, but environmental pressures create a gap between students' intentions and their behaviors. Our findings show student AI use to be a situated practice, and we discuss implications for institutions, instructors, and system tool designers to effectively support student learning with AI.
Gender and Digital Platform Work During Turbulent Times
Venue: arXiv cs.CY 2026
Year: 2026

This commentary explores how the platform economy shapes labour market responses during times of crisis, with a focus on gendered experiences. Drawing on cases of economic crisis, natural disasters, and refugee displacement, it examines how digital labour platforms offer flexible work opportunities while also reinforcing existing inequalities. Women face distinct constraints (such as caregiving responsibilities, limited mobility, and economic insecurity) that hinder their employment opportunities and earnings potential. These constraints are more pronounced during crises, when access to stable income and safe working conditions becomes more difficult. While platform work can serve as a lifeline, it is not a guaranteed solution, and its benefits are unevenly distributed. The commentary calls for gender-responsive policies and new research to understand how digital infrastructures mediate labour experiences across different crisis contexts. Such research can inform inclusive strategies that promote resilience and equity in platform-based work, particularly for marginalized and displaced populations.
Message-Oriented Middleware Systems: Technology Overview
Venue: arXiv cs.DC 2026
Year: 2026

We present a comprehensive characterization study of open-source message-oriented middleware (MOM) systems. We followed a rigorous methodology to select and study ten popular and diverse MOM systems. For each system, we examine 42 features with a total of 134 different options. We found that MOM systems have evolved to provide a framework for modern cloud applications through high flexibility and configurability and by offering core building blocks for complex applications including transaction support, active messaging, resource management, flow control, and native support for multi-tenancy. We also identify that there is an opportunity for the community to consolidate its efforts on fewer open-source projects. We have also created an annotated data set that makes it easy to verify our findings, which can also be used to help practitioners and developers understand and compare the features of different systems. For a wider impact, we make our data set publicly available.
Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry
Venue: arXiv cs.GT 2026
Year: 2026

Generative model ecosystems increasingly operate as competitive multi-platform markets, where platforms strategically select models from a shared pool and users with heterogeneous preferences choose among them. Understanding how platforms interact, when market equilibria exist, how outcomes are shaped by model-providers, platforms, and user behavior, and how social welfare is affected is critical for fostering a beneficial market environment. In this paper, we formalize a three-layer model-platform-user market game and identify conditions for the existence of pure Nash equilibrium. Our analysis shows that market structure, whether platforms converge on similar models or differentiate by selecting distinct ones, depends not only on models' global average performance but also on their localized attraction to user groups. We further examine welfare outcomes and show that expanding the model pool does not necessarily increase user welfare or market diversity. Finally, we design novel best-response training schemes that allow model providers to strategically introduce new models into competitive markets.
Reinforcement-Learning-Based Assistance Reduces Squat Effort with a Modular Hip--Knee Exoskeleton
Venue: arXiv cs.RO 2026
Year: 2026

Squatting is one of the most demanding lower-limb movements, requiring substantial muscular effort and coordination. Reducing the physical demands of this task through intelligent and personalized assistance has significant implications, particularly in industries involving repetitive low-level assembly activities. In this study, we evaluated the effectiveness of a neural network controller for a modular Hip-Knee exoskeleton designed to assist squatting tasks. The neural network controller was trained via reinforcement learning (RL) in a physics-based, human-exoskeleton interaction simulation environment. The controller generated real-time hip and knee assistance torques based on recent joint-angle and velocity histories. Five healthy adults performed three-minute metronome-guided squats under three conditions: (1) no exoskeleton (No-Exo), (2) exoskeleton with Zero-Torque, and (3) exoskeleton with active assistance (Assistance). Physiological effort was assessed using indirect calorimetry and heart rate monitoring, alongside concurrent kinematic data collection. Results show that the RL-based controller adapts to individuals by producing torque profiles tailored to each subject's kinematics and timing. Compared with the Zero-Torque and No-Exo condition, active assistance reduced the net metabolic rate by approximately 10%, with minor reductions observed in heart rate. However, assisted trials also exhibited reduced squat depth, reflected by smaller hip and knee flexion. These preliminary findings suggest that the proposed controller can effectively lower physiological effort during repetitive squatting, motivating further improvements in both hardware design and control strategies.
Exploiting Liquidity Exhaustion Attacks in Intent-Based Cross-Chain Bridges
Venue: arXiv cs.CR 2026
Year: 2026

Intent-based cross-chain bridges have emerged as an alternative to traditional interoperability protocols by allowing off-chain entities (\emph{solvers}) to immediately fulfill users' orders by fronting their own liquidity. While improving user experience, this approach introduces new systemic risks, such as solver liquidity concentration and delayed settlement. In this paper, we propose a new class of attacks called \emph{liquidity exhaustion attacks} and a replay-based parameterized attack simulation framework. We analyze 3.5 million cross-chain intents that moved \$9.24B worth of tokens between June and November 2025 across three major protocols (Mayan Swift, Across, and deBridge), spanning nine blockchains. For rational attackers, our results show that protocols with higher solver profitability, such as deBridge, are vulnerable under current parameters: 210 historical attack instances yield a mean net profit of \$286.14, with 80.5\% of attacks profitable. In contrast, Across remains robust in all tested configurations due to low solver margins and very high liquidity, while Mayan Swift is generally secure but becomes vulnerable under stress-test conditions. Under byzantine attacks, we show that it is possible to suppress availability across all protocols, causing dozens of failed intents and solver profit losses of up to \$978 roughly every 16 minutes. Finally, we propose an optimized attack strategy that exploits patterns in the data to reduce attack costs by up to 90.5\% compared to the baseline, lowering the barrier to liquidity exhaustion attacks.
StableAML: Machine Learning for Behavioral Wallet Detection in Stablecoin Anti-Money Laundering on Ethereum
Venue: arXiv cs.CR 2026
Year: 2026

Global illicit fund flows exceed an estimated $3.1 trillion annually, with stablecoins emerging as a preferred laundering medium due to their liquidity. While decentralized protocols increasingly adopt zero-knowledge proofs to obfuscate transaction graphs, centralized stablecoins remain critical "transparent choke points" for compliance. Leveraging this persistent visibility, this study analyzes an Ethereum dataset and uses behavioral features to develop a robust AML framework. Our findings demonstrate that domain-informed tree ensemble models achieve higher Macro-F1 score, significantly outperforming graph neural networks, which struggle with the increasing fragmentation of transaction networks. The model's interpretability goes beyond binary detection, successfully dissecting distinct typologies: it differentiates the complex, high-velocity dispersion of cybercrime syndicates from the constrained, static footprints left by sanctioned entities. This framework aligns with the industry shift toward deterministic verification, satisfying the auditability and compliance expectations under regulations such as the EU's MiCA and the U.S. GENIUS Act while minimizing unjustified asset freezes. By automating high-precision detection, we propose an approach that effectively raises the economic cost of financial misconduct without stifling innovation.
Two Calm Ends and the Wild Middle: A Geometric Picture of Memorization in Diffusion Models
Venue: arXiv cs.LG 2026
Year: 2026

Diffusion models generate high-quality samples but can also memorize training data, raising serious privacy concerns. Understanding the mechanisms governing when memorization versus generalization occurs remains an active area of research. In particular, it is unclear where along the noise schedule memorization is induced, how data geometry influences it, and how phenomena at different noise scales interact. We introduce a geometric framework that partitions the noise schedule into three regimes based on the coverage properties of training data by Gaussian shells and the concentration behavior of the posterior, which we argue are two fundamental objects governing memorization and generalization in diffusion models. This perspective reveals that memorization risk is highly non-uniform across noise levels. We further identify a danger zone at medium noise levels where memorization is most pronounced. In contrast, both the small and large noise regimes resist memorization, but through fundamentally different mechanisms: small noise avoids memorization due to limited training coverage, while large noise exhibits low posterior concentration and admits a provably near linear Gaussian denoising behavior. For the medium noise regime, we identify geometric conditions through which we propose a geometry-informed targeted intervention that mitigates memorization.
Neural Prior Estimation: Learning Class Priors from Latent Representations
Venue: arXiv cs.LG 2026
Year: 2026

Class imbalance induces systematic bias in deep neural networks by imposing a skewed effective class prior. This work introduces the Neural Prior Estimator (NPE), a framework that learns feature-conditioned log-prior estimates from latent representations. NPE employs one or more Prior Estimation Modules trained jointly with the backbone via a one-way logistic loss. Under the Neural Collapse regime, NPE is analytically shown to recover the class log-prior up to an additive constant, providing a theoretically grounded adaptive signal without requiring explicit class counts or distribution-specific hyperparameters. The learned estimate is incorporated into logit adjustment, forming NPE-LA, a principled mechanism for bias-aware prediction. Experiments on long-tailed CIFAR and imbalanced semantic segmentation benchmarks (STARE, ADE20K) demonstrate consistent improvements, particularly for underrepresented classes. NPE thus offers a lightweight and theoretically justified approach to learned prior estimation and imbalance-aware prediction.
Automated LLM-Based Accessibility Remediation: From Conventional Websites to Angular Single-Page Applications
Venue: arXiv cs.SE 2026
Year: 2026

Web accessibility remains an unresolved issue for a large part of the web content. There are many tools to detect errors automatically, but fixing those issues is still mostly a manual, slow, and costly process in which it is easy for developers to overlook specific details. The situation becomes even more complex with modern Single-Page Applications (SPAs), whose dynamic nature makes traditional static analysis approaches inadequate. This work proposes a system that aims to address this challenge by using Large Language Models (LLMs) to automate accessibility fixes. The proposal presents a modular workflow applicable to both static websites and complex Angular projects. The framework actively implements corrections within the DOM of static web pages or the source code of SPAs. The system was tested on 12 static websites and 6 open-source Angular projects, fixing 80% of the accessibility issues on public websites and 86% of the issues on Angular applications. Our proposal also generates meaningful visual descriptions for images while preserving the application's design and stability. This work contributes to ensuring that accessibility stops being a technical debt deferred to the future and becomes a natural part of everyday development workflows.
Growing With the Condition: Co-Designing Pediatric Technologies that Adapt Across Developmental Stages
Venue: arXiv cs.HC 2026
Year: 2026

Children with chronic conditions face evolving challenges in daily activities, peer relationships, and clinical care. Younger children often rely on parental support, while older ones seek independence. Prior studies on chronic conditions explored proxy-based, family-centered, and playful approaches to support children's health, but most technologies treat children as a homogeneous group rather than adapting to their developmental differences. To address this gap, we conducted four co-design workshops with 69 children with congenital heart disease (CHD) at a medically supported camp, spanning elementary, middle, and high school groups. Our analysis reveals distinct coping strategies: elementary children relied on comfort objects and reassurance, middle schoolers used mediated communication and selective disclosure, and high schoolers emphasized agency and direct engagement with peers and providers. Through child-centered participatory design, we contribute empirical insights into how children's management of chronic conditions evolves and propose design implications for pediatric health technologies that adapt across developmental trajectories.
ZACH-ViT: Regime-Dependent Inductive Bias in Compact Vision Transformers for Medical Imaging
Venue: arXiv cs.CV 2026
Year: 2026

Vision Transformers rely on positional embeddings and class tokens encoding fixed spatial priors. While effective for natural images, these priors may be suboptimal when spatial layout is weakly informative, a frequent condition in medical imaging. We introduce ZACH-ViT (Zero-token Adaptive Compact Hierarchical Vision Transformer), a compact Vision Transformer that removes positional embeddings and the [CLS] token, achieving permutation-invariant patch processing via global average pooling. Zero-token denotes removal of the dedicated aggregation token and positional encodings. Patch tokens remain unchanged. Adaptive residual projections preserve training stability under strict parameter constraints. We evaluate ZACH-ViT across seven MedMNIST datasets under a strict few-shot protocol (50 samples/class, fixed hyperparameters, five seeds). Results reveal regime-dependent behavior: ZACH-ViT (0.25M parameters, trained from scratch) achieves strongest advantage on BloodMNIST and remains competitive on PathMNIST, while relative advantage decreases on datasets with stronger anatomical priors (OCTMNIST, OrganAMNIST), consistent with our hypothesis. Component and pooling ablations show positional support becomes mildly beneficial as spatial structure increases, whereas reintroducing a [CLS] token is consistently unfavorable. These findings support that architectural alignment with data structure can outweigh universal benchmark dominance. Despite minimal size and no pretraining, ZACH-ViT achieves competitive performance under data-scarce conditions, relevant for compact medical imaging and low-resource settings. Code: https://github.com/Bluesman79/ZACH-ViT
Convergent Gate Elimination and Constructive Circuit Lower Bounds
Venue: arXiv cs.CC 2026
Year: 2026

Towards better understanding of gate elimination, the only method known that can prove complexity lower bounds for explicit functions against unrestricted Boolean circuits, this work contributes: (1) formalizing circuit simplifications as a convergent term graph rewriting system and (2) giving a simple and constructive proof of a classical lower bound using this system. First, we show that circuit simplification is a convergent term graph rewriting system over the DeMorgan and $\{\land, \lor, \oplus\}$ bases. We define local rewriting rules from Boolean identities such that every simplification sequence yields an identical final result (up to circuit isomorphism or bisimulation). Convergence enables rigorous reasoning about structural properties of simplified circuits without dependence on the order of simplification. Then, we show that there is \emph{no similar} convergent formalization of circuit simplification over the $U_2$ and $B_2$ bases. Then, we use our simplification system to give a constructive circuit lower bound, generalizing Schnorr's classical result that the XOR function requires $3(n - 1)$ gates to compute in the DeMorgan basis. A constructive lower bound $f \not\in C$ gives an algorithm (called a "refuter") that efficiently finds counter-examples for every $C$-circuit trying to compute the function $f$. Chen, Jin, Santhanam, and Williams showed that constructivity plays a central role in many longstanding open problems about complexity theory (FOCS 2021), so it is natural to ask for constructive circuit lower bounds from gate elimination arguments. This demonstrates how using convergent simplification can lead to shorter and more modular proofs of circuit lower bounds. Furthermore, until this work, no constructive lower bound had been proved via gate elimination.
CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications
Venue: arXiv cs.CL 2026
Year: 2026

Background: Clinical named entity recognition tools commonly map free text to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). For many downstream tasks, however, the clinically meaningful unit is not a single CUI but a concept set comprising related synonyms, subtypes, and supertypes. Constructing such concept sets is labour-intensive, inconsistently performed, and poorly supported by existing tools, particularly for NLP pipelines that operate directly on UMLS CUIs. Methods We present CUICurate, a Graph-based retrieval-augmented generation (GraphRAG) framework for automated UMLS concept set curation. A UMLS knowledge graph (KG) was constructed and embedded for semantic retrieval. For each target concept, candidate CUIs were retrieved from the KG, followed by large language model (LLM) filtering and classification steps comparing two LLMs (GPT-5 and GPT-5-mini). The framework was evaluated on five lexically heterogeneous clinical concepts against a manually curated benchmark and gold-standard concept sets. Results Across all concepts, CUICurate produced substantially larger and more complete concept sets than the manual benchmarks whilst matching human precision. Comparisons between the two LLMs found that GPT-5-mini achieved higher recall during filtering, while GPT-5 produced classifications that more closely aligned with clinician judgements. Outputs were stable across repeated runs and computationally inexpensive. Conclusions CUICurate offers a scalable and reproducible approach to support UMLS concept set curation that substantially reduces manual effort. By integrating graph-based retrieval with LLM reasoning, the framework produces focused candidate concept sets that can be adapted to clinical NLP pipelines for different phenotyping and analytic requirements.
Domain-Decomposed Lagrangian Data Assimilation for Drifting Sea-Ice Floe Dynamics
Venue: arXiv cs.CE 2026
Year: 2026

Sea ice dynamics are crucial to the global climate system, yet traditional continuum (e.g., viscous-plastic) models often fail to represent the discrete floe interactions that dominate in the marginal ice zone. Lagrangian discrete element methods (DEMs) resolve floe-scale physics more realistically, but their high particle counts make ensemble data assimilation (DA) more expensive. We consider a highly-simplified floe model and propose a scalable, domain-decomposed DA framework that couples Lagrangian particle observations with an ensemble transform Kalman filter (ETKF) to recover the underlying ocean flow field in a multiscale setting. The Eulerian domain is first partitioned into subdomains. We then impose an ETKF in each subdomain to recover the local fine-scale ocean features. A Gaussian-weighted blending step then reconstructs a globally consistent flow field across subdomain boundaries. Numerical experiments demonstrate consistently better skill scores that are characterised by normalised root mean square error (NRMSE) and pattern correlation coefficients (PCC), compared to the global and expensive DA baseline. Results suggest that the domain-decomposed DA method is an alternative, scalable approach for particle-based sea-ice floe dynamics and ocean flow recovery.
PenTiDef: Enhancing Privacy and Robustness in Decentralized Federated Intrusion Detection Systems against Poisoning Attacks
Venue: arXiv cs.CR 2026
Year: 2026

The increasing deployment of Federated Learning (FL) in Intrusion Detection Systems (IDS) introduces new challenges related to data privacy, centralized coordination, and susceptibility to poisoning attacks. While significant research has focused on protecting traditional FL-IDS with centralized aggregation servers, there remains a notable gap in addressing the unique challenges of decentralized FL-IDS (DFL-IDS). This study aims to address the limitations of traditional centralized FL-IDS by proposing a novel defense framework tailored for the decentralized FL-IDS architecture, with a focus on privacy preservation and robustness against poisoning attacks. We propose PenTiDef, a privacy-preserving and robust defense framework for DFL-IDS, which incorporates Distributed Differential Privacy (DDP) to protect data confidentiality and utilizes latent space representations (LSR) derived from neural networks to detect malicious updates in the decentralized model aggregation context. To eliminate single points of failure and enhance trust without a centralized aggregation server, PenTiDef employs a blockchain-based decentralized coordination mechanism that manages model aggregation, tracks update history, and supports trust enforcement through smart contracts. Experimental results on CIC-IDS2018 and Edge-IIoTSet demonstrate that PenTiDef consistently outperforms existing defenses (e.g., FLARE, FedCC) across various attack scenarios and data distributions. These findings highlight the potential of PenTiDef as a scalable and secure framework for deploying DFL-based IDS in adversarial environments. By leveraging privacy protection, malicious behavior detection in hidden data, and working without a central server, it provides a useful security solution against real-world attacks from untrust participants.