OpenRaiser/Intern-Atlas huggingface dataset:
Optical Tag-Based Neuronavigation and Augmentation System for Non-Invasive Brain Stimulation
Venue: arXiv cs.HC 2026
Year: 2026

Accurate neuronavigation is critical for effective transcranial magnetic stimulation (TMS), as stimulation outcomes depend directly on precise coil placement. Existing neuronavigation systems are often costly, complex, and prone to tracking errors. To address these limitations, we present a computer vision based neuronavigation system that enables real time tracking of the patient and TMS instrumentation. The system integrates a multi camera optical tracking setup with consumer grade hardware and visible markers to drive a digital twin of the stimulation process. A dynamic 3D brain model in Unity updates in real time to visualize coil position and estimated stimulation targets. Augmented reality (AR) is further incorporated to project this model directly onto the patient's head, enabling intuitive, in situ coil adjustment without reliance on abstract numerical displays. Overall, the proposed approach improves spatial precision and accuracy while enhancing usability.
MAGE-KT: Multi-Agent Graph-Enhanced Knowledge Tracing with Subgraph Retrieval and Asymmetric Fusion
Venue: arXiv cs.AI 2026
Year: 2026

Knowledge Tracing (KT) aims to model a student's learning trajectory and predict performance on the next question. A key challenge is how to better represent the relationships among students, questions, and knowledge concepts (KCs). Recently, graph-based KT paradigms have shown promise for this problem. However, existing methods have not sufficiently explored inter-concept relations, often inferred solely from interaction sequences. In addition, the scale and heterogeneity of KT graphs make full-graph encoding both computationally both costly and noise-prone, causing attention to bleed into student-irrelevant regions and degrading the fidelity of inter-KC relations. To address these issues, we propose a novel framework: Multi-Agent Graph-Enhanced Knowledge Tracing (MAGE-KT). It constructs a multi-view heterogeneous graph by combining a multi-agent KC relation extractor and a student-question interaction graph, capturing complementary semantic and behavioral signals. Conditioned on the target student's history, it retrieves compact, high-value subgraphs and integrates them using an Asymmetric Cross-attention Fusion Module to enhance prediction while avoiding attention diffusion and irrelevant computation. Experiments on three widely used KT datasets show substantial improvements in KC-relation accuracy and clear gains in next-question prediction over existing methods.
Conditionally Tight Algorithms for Maximum k-Coverage and Partial k-Dominating Set via Arity-Reducing Hypercuts
Venue: arXiv cs.DS 2026
Year: 2026

We revisit the classic Maximum $k$-Coverage problem: Determine the largest number $t$ of elements that can be covered by choosing $k$ sets from a given family $\mathcal{F} = \{S_1,\dots, S_n\}$ of a size-$u$ universe. A notable special case is Partial $k$-Dominating Set, where one chooses $k$ vertices in a graph to maximize the number of dominated vertices. Extensive research has established strong hardness results for various aspects of Maximum $k$-Coverage, such as tight inapproximability results, $W[2]$-hardness, and a conditionally tight worst-case running time of $n^{k\pm o(1)}$. In this paper we ask: (1) Can this time bound be improved for small $t$, at least for Partial $k$-Dominating Set, ideally to time~$t^{k\pm O(1)}$? (2) More ambitiously, can we even determine the best-possible running time of Maximum $k$-Coverage with respect to the perhaps most natural parameters: the universe size $u$, the maximum set size $s$, and the maximum frequency $f$? We successfully resolve both questions. (1) We give an algorithm that solves Partial $k$-Dominating Set in time $O(nt + t^{\frac{2ω}{3} k+O(1)})$ if $ω\ge 2.25$ and time $O(nt+ t^{\frac{3}{2} k+O(1)})$ if $ω\le 2.25$, where $ω\le 2.372$ is the matrix multiplication exponent. From this we derive a time bound that is conditionally optimal, regardless of $ω$, based on the well-established $k$-clique and 3-uniform hyperclique hypotheses from fine-grained complexity. We also obtain matching upper and lower bounds for sparse graphs. To address (2) we design an algorithm for Maximum $k$-Coverage running in time $$ \min \left\{ (f\cdot \min\{\sqrt[3]{u}, \sqrt{s}\})^k + \min\{n,f\cdot \min\{\sqrt{u}, s\}\}^{kω/3}, n^k\right\} \cdot g(k)n^{\pm O(1)}, $$ and, surprisingly, further show that this complicated time bound is also conditionally optimal.
AgentDrive: An Open Benchmark Dataset for Agentic AI Reasoning with LLM-Generated Scenarios in Autonomous Systems
Venue: arXiv cs.AI 2026
Year: 2026

The rapid advancement of large language models (LLMs) has sparked growing interest in their integration into autonomous systems for reasoning-driven perception, planning, and decision-making. However, evaluating and training such agentic AI models remains challenging due to the lack of large-scale, structured, and safety-critical benchmarks. This paper introduces AgentDrive, an open benchmark dataset containing 300,000 LLM-generated driving scenarios designed for training, fine-tuning, and evaluating autonomous agents under diverse conditions. AgentDrive formalizes a factorized scenario space across seven orthogonal axes: scenario type, driver behavior, environment, road layout, objective, difficulty, and traffic density. An LLM-driven prompt-to-JSON pipeline generates semantically rich, simulation-ready specifications that are validated against physical and schema constraints. Each scenario undergoes simulation rollouts, surrogate safety metric computation, and rule-based outcome labeling. To complement simulation-based evaluation, we introduce AgentDrive-MCQ, a 100,000-question multiple-choice benchmark spanning five reasoning dimensions: physics, policy, hybrid, scenario, and comparative reasoning. We conduct a large-scale evaluation of fifty leading LLMs on AgentDrive-MCQ. Results show that while proprietary frontier models perform best in contextual and policy reasoning, advanced open models are rapidly closing the gap in structured and physics-grounded reasoning. We release the AgentDrive dataset, AgentDrive-MCQ benchmark, evaluation code, and related materials at https://github.com/maferrag/AgentDrive
VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents
Venue: arXiv cs.CV 2026
Year: 2026

Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long horizons. We introduce VisGym, a gymnasium of 17 environments for evaluating and training VLMs. The suite spans symbolic puzzles, real-image understanding, navigation, and manipulation, and provides flexible controls over difficulty, input representation, planning horizon, and feedback. We also provide multi-step solvers that generate structured demonstrations, enabling supervised finetuning. Our evaluations show that all frontier models struggle in interactive settings, achieving low success rates in both the easy (46.6%) and hard (26.0%) configurations. Our experiments reveal notable limitations: models struggle to effectively leverage long context, performing worse with an unbounded history than with truncated windows. Furthermore, we find that several text-based symbolic tasks become substantially harder once rendered visually. However, explicit goal observations, textual feedback, and exploratory demonstrations in partially observable or unknown-dynamics settings for supervised finetuning yield consistent gains, highlighting concrete failure modes and pathways for improving multi-step visual decision-making. Code, data, and models can be found at: https://visgym.github.io/.
Uncertainty Quantification for Named Entity Recognition via Full-Sequence and Subsequence Conformal Prediction
Venue: arXiv cs.CL 2026
Year: 2026

Named Entity Recognition (NER) serves as a foundational component in many natural language processing (NLP) pipelines. However, current NER models typically output a single predicted label sequence without any accompanying measure of uncertainty, leaving downstream applications vulnerable to cascading errors. In this paper, we introduce a general framework for adapting sequence-labeling-based NER models to produce uncertainty-aware prediction sets. These prediction sets are collections of full-sentence labelings that are guaranteed to contain the correct labeling with a user-specified confidence level. This approach serves a role analogous to confidence intervals in classical statistics by providing formal guarantees about the reliability of model predictions. Our method builds on conformal prediction, which offers finite-sample coverage guarantees under minimal assumptions. We design efficient nonconformity scoring functions to construct efficient, well-calibrated prediction sets that support both unconditional and class-conditional coverage. This framework accounts for heterogeneity across sentence length, language, entity type, and number of entities within a sentence. Empirical experiments on four NER models across three benchmark datasets demonstrate the broad applicability, validity, and efficiency of the proposed methods.
Beyond Simulations: What 20,000 Real Conversations Reveal About Mental Health AI Safety
Venue: arXiv cs.CY 2026
Year: 2026

Large language models (LLMs) are increasingly used for mental health support, yet existing safety evaluations rely primarily on small, simulation-based test sets that have an unknown relationship to the linguistic distribution of real usage. In this study, we present replications of four published safety test sets targeting suicide risk assessment, harmful content generation, refusal robustness, and adversarial jailbreaks for a leading frontier generic AI model alongside an AI purpose built for mental health support. We then propose and conduct an ecological audit on over 20,000 real-world user conversations with the purpose-built AI designed with layered suicide and non-suicidal self-injury (NSSI) safeguards to compare test set performance to real world performance. While the purpose-built AI was significantly less likely than general-purpose LLMs to produce enabling or harmful content across suicide/NSSI (.4-11.27% vs 29.0-54.4%), eating disorder (8.4% vs 54.0%), and substance use (9.9% vs 45.0%) benchmark prompts, test set failure rates for suicide/NSSI were far higher than in real-world deployment. Clinician review of flagged conversations from the ecological audit identified zero cases of suicide risk that failed to receive crisis resources. Across all 20,000 conversations, three mentions of NSSI risk (.015%) did not trigger a crisis intervention; among sessions flagged by the LLM judge, this corresponds to an end-to-end system false negative rate of .38%, providing a lower bound on real-world safety failures. These findings support a shift toward continuous, deployment-relevant safety assurance for AI mental-health systems rather than limited set benchmark certification.
Bayesian Robust Financial Trading with Adversarial Synthetic Market Data
Venue: arXiv cs.LG 2026
Year: 2026

Algorithmic trading relies on machine learning models to make trading decisions. Despite strong in-sample performance, these models often degrade when confronted with evolving real-world market regimes, which can shift dramatically due to macroeconomic changes-e.g., monetary policy updates or unanticipated fluctuations in participant behavior. We identify two challenges that perpetuate this mismatch: (1) insufficient robustness in existing policy against uncertainties in high-level market fluctuations, and (2) the absence of a realistic and diverse simulation environment for training, leading to policy overfitting. To address these issues, we propose a Bayesian Robust Framework that systematically integrates a macro-conditioned generative model with robust policy learning. On the data side, to generate realistic and diverse data, we propose a macro-conditioned GAN-based generator that leverages macroeconomic indicators as primary control variables, synthesizing data with faithful temporal, cross-instrument, and macro correlations. On the policy side, to learn robust policy against market fluctuations, we cast the trading process as a two-player zero-sum Bayesian Markov game, wherein an adversarial agent simulates shifting regimes by perturbing macroeconomic indicators in the macro-conditioned generator, while the trading agent-guided by a quantile belief network-maintains and updates its belief over hidden market states. The trading agent seeks a Robust Perfect Bayesian Equilibrium via Bayesian neural fictitious self-play, stabilizing learning under adversarial market perturbations. Extensive experiments on 9 financial instruments demonstrate that our framework outperforms 9 state-of-the-art baselines. In extreme events like the COVID, our method shows improved profitability and risk management, offering a reliable solution for trading under uncertain and shifting market dynamics.
Diagnosis Support of Sickle Cell Anemia by Classifying Red Blood Cell Shape in Peripheral Blood Images
Venue: arXiv cs.CV 2026
Year: 2026

Red blood cell (RBC) deformation is the consequence of several diseases, including sickle cell anemia, which causes recurring episodes of pain and severe pronounced anemia. Monitoring patients with these diseases involves the observation of peripheral blood samples under a microscope, a time-consuming procedure. Moreover, a specialist is required to perform this technique, and owing to the subjective nature of the observation of isolated RBCs, the error rate is high. In this paper, we propose an automated method for differentially enumerating RBCs that uses peripheral blood smear image analysis. In this method, the objects of interest in the image are segmented using a Chan-Vese active contour model. An analysis is then performed to classify the RBCs, also called erythrocytes, as normal or elongated or having other deformations, using the basic shape analysis descriptors: circular shape factor (CSF) and elliptical shape factor (ESF). To analyze cells that become partially occluded in a cluster during sample preparation, an elliptical adjustment is performed to allow the analysis of erythrocytes with discoidal and elongated shapes. The images of patient blood samples used in the study were acquired by a clinical laboratory specialist in the Special Hematology Department of the ``Dr. Juan Bruno Zayas'' General Hospital in Santiago de Cuba. A comparison of the results obtained by the proposed method in our experiments with those obtained by some state-of-the-art methods showed that the proposed method is superior for the diagnosis of sickle cell anemia. This superiority is achieved for evidenced by the obtained F-measure value (0.97 for normal cells and 0.95 for elongated ones) and several overall multiclass performance measures. The results achieved by the proposed method are suitable for the purpose of clinical treatment and diagnostic support of sickle cell anemia.
LLM-Generated or Human-Written? Comparing Review and Non-Review Papers on ArXiv
Venue: arXiv cs.DL 2026
Year: 2026

ArXiv recently prohibited the upload of unpublished review papers to its servers in the Computer Science domain, citing a high prevalence of LLM-generated content in these categories. However, this decision was not accompanied by quantitative evidence. In this work, we investigate this claim by measuring the proportion of LLM-generated content in review vs. non-review research papers in recent years. Using two high-quality detection methods, we find a substantial increase in LLM-generated content across both review and non-review papers, with a higher prevalence in review papers. However, when considering the number of LLM-generated papers published in each category, the estimates of non-review LLM-generated papers are almost six times higher. Furthermore, we find that this policy will affect papers in certain domains far more than others, with the CS subdiscipline Computers & Society potentially facing cuts of 50%. Our analysis provides an evidence-based framework for evaluating such policy decisions, and we release our code to facilitate future investigations at: https://github.com/yanaiela/llm-review-arxiv.
FP-THD: Full page transcription of historical documents
Venue: arXiv cs.CV 2026
Year: 2026

The transcription of historical documents written in Latin in XV and XVI centuries has special challenges as it must maintain the characters and special symbols that have distinct meanings to ensure that historical texts retain their original style and significance. This work proposes a pipeline for the transcription of historical documents preserving these special features. We propose to extend an existing text line recognition method with a layout analysis model. We analyze historical text images using a layout analysis model to extract text lines, which are then processed by an OCR model to generate a fully digitized page. We showed that our pipeline facilitates the processing of the page and produces an efficient result. We evaluated our approach on multiple datasets and demonstrate that the masked autoencoder effectively processes different types of text, including handwritten, printed and multi-language.
Atomic Depth Estimation From Noisy Electron Microscopy Data Via Deep Learning
Venue: arXiv cs.CV 2026
Year: 2026

We present a novel approach for extracting 3D atomic-level information from transmission electron microscopy (TEM) images affected by significant noise. The approach is based on formulating depth estimation as a semantic segmentation problem. We address the resulting segmentation problem by training a deep convolutional neural network to generate pixel-wise depth segmentation maps using simulated data corrupted by synthetic noise. The proposed method was applied to estimate the depth of atomic columns in CeO2 nanoparticles from simulated images and real-world TEM data. Our experiments show that the resulting depth estimates are accurate, calibrated and robust to noise.
AI, Metacognition, and the Verification Bottleneck: A Three-Wave Longitudinal Study of Human Problem-Solving
Venue: arXiv cs.CY 2026
Year: 2026

This longitudinal pilot study tracked how generative AI reshapes problem-solving over six months across three waves in an academic setting. AI integration reached saturation by Wave 3, with daily use rising from 52.4% to 95.7% and ChatGPT adoption from 85.7% to 100%. A dominant hybrid workflow increased 2.7-fold, adopted by 39.1% of participants. The verification paradox emerged: participants relied most heavily on AI for difficult tasks (73.9%) yet showed declining verification confidence (68.1%) where performance was worst (47.8% accuracy on complex tasks). Objective performance declined systematically: 95.2% to 81.0% to 66.7% to 47.8% across problem difficulty, with belief-performance gaps widening to 34.6 percentage points. This indicates a fundamental shift where verification, not solution generation, became the bottleneck in human-AI problem-solving. The ACTIVE Framework synthesizes findings grounded in cognitive load theory: Awareness and task-AI alignment, Critical verification protocols, Transparent human-in-the-loop integration, Iterative skill development countering cognitive offloading, Verification confidence calibration, and Ethical evaluation. The authors provide implementation pathways for institutions and practitioners. Key limitations include sample homogeneity (academic cohort only, convenience sampling) limiting generalizability to corporate, clinical, or regulated professional contexts; self-report bias in confidence measures (32.2 percentage point divergence from objective performance); lack of control conditions; restriction to mathematical/analytical problems; and insufficient timeframe to assess long-term skill trajectories. Results generalize primarily to early-adopter, academically affiliated populations. Causal validation requires randomized controlled trials.
Frequency-aware Adaptive Contrastive Learning for Sequential Recommendation
Venue: arXiv cs.IR 2026
Year: 2026

In this paper, we revisited the role of data augmentation in contrastive learning for sequential recommendation, revealing its inherent bias against low-frequency items and sparse user behaviors. To address this limitation, we proposed FACL, a frequency-aware adaptive contrastive learning framework that introduces micro-level adaptive perturbation to protect the integrity of rare items, as well as macro-level reweighting to amplify the influence of sparse and rare-interaction sequences during training. Comprehensive experiments on five public benchmark datasets demonstrated that FACL consistently outperforms state-of-the-art data augmentation and model augmentation-based methods, achieving up to 3.8% improvement in recommendation accuracy. Moreover, fine-grained analyses confirm that FACL significantly alleviates the performance drop on low-frequency items and users, highlighting its robust intent-preserving ability and its superior applicability to real-world, long-tail recommendation scenarios.
A Computer Vision Pipeline for Iterative Bullet Hole Tracking in Rifle Zeroing
Venue: arXiv cs.CV 2026
Year: 2026

Adjusting rifle sights, a process commonly called "zeroing," requires shooters to identify and differentiate bullet holes from multiple firing iterations. Traditionally, this process demands physical inspection, introducing delays due to range safety protocols and increasing the risk of human error. We present an end-to-end computer vision system for automated bullet hole detection and iteration-based tracking directly from images taken at the firing line. Our approach combines YOLOv8 for accurate small-object detection with Intersection over Union (IoU) analysis to differentiate bullet holes across sequential images. To address the scarcity of labeled sequential data, we propose a novel data augmentation technique that removes rather than adds objects to simulate realistic firing sequences. Additionally, we introduce a preprocessing pipeline that standardizes target orientation using ORB-based perspective correction, improving model accuracy. Our system achieves 97.0% mean average precision on bullet hole detection and 88.8% accuracy in assigning bullet holes to the correct firing iteration. While designed for rifle zeroing, this framework offers broader applicability in domains requiring the temporal differentiation of visually similar objects.
Between Search and Platform: ChatGPT Under the DSA
Venue: arXiv cs.CY 2026
Year: 2026

This article examines the applicability of the Digital Services Act (DSA) to ChatGPT, arguing that it should be classified as a hybrid of the two types of hosting services: online search engines and platforms. This requires classifying search engines as hosting services, which we show is appropriate under the DSA, thereby resolving an ambiguity in the legal framework. ChatGPT performs core search functions and stores user-provided inputs and custom GPTs, meeting the definition of hosting service. We compare ChatGPT's systemic risks with those of existing Very Large Online Search Engines (VLOSEs) and Platforms (VLOPs), showing that it raises similarly serious concerns regarding illegal content, fundamental rights, democratic integrity, and public health. Now that ChatGPT has reached the 45 million EU user threshold, it should be subject to the most onerous DSA obligations, requiring the assessment and mitigation of risk emanating from both its online search engine- and platform-like characteristics.