OpenRaiser/Intern-Atlas huggingface dataset:
Back to the Features: DINO as a Foundation for Video World Models
Venue: arXiv cs.CV 2025
Year: 2025

We present DINO-world, a powerful generalist video world model trained to predict future frames in the latent space of DINOv2. By leveraging a pre-trained image encoder and training a future predictor on a large-scale uncurated video dataset, DINO-world learns the temporal dynamics of diverse scenes, from driving and indoor scenes to simulated environments. We show that DINO-world outperforms previous models on a variety of video prediction benchmarks, e.g. segmentation and depth forecasting, and demonstrates strong understanding of intuitive physics. Furthermore, we show that it is possible to fine-tune the predictor on observation-action trajectories. The resulting action-conditioned world model can be used for planning by simulating candidate trajectories in latent space.
Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts
Venue: arXiv cs.LG 2025
Year: 2025

Many recent papers have studied the development of superforecaster-level event forecasting LLMs. While methodological problems with early studies cast doubt on the use of LLMs for event forecasting, recent studies with improved evaluation methods have shown that state-of-the-art LLMs are gradually reaching superforecaster-level performance, and reinforcement learning has also been reported to improve future forecasting. Additionally, the unprecedented success of recent reasoning models and Deep Research-style models suggests that technology capable of greatly improving forecasting performance has been developed. Therefore, based on these positive recent trends, we argue that the time is ripe for research on large-scale training of superforecaster-level event forecasting LLMs. We discuss two key research directions: training methods and data acquisition. For training, we first introduce three difficulties of LLM-based event forecasting training: noisiness-sparsity, knowledge cut-off, and simple reward structure problems. Then, we present related ideas to mitigate these problems: hypothetical event Bayesian networks, utilizing poorly-recalled and counterfactual events, and auxiliary reward signals. For data, we propose aggressive use of market, public, and crawling datasets to enable large-scale training and evaluation. Finally, we explain how these technical advances could enable AI to provide predictive intelligence to society in broader areas. This position paper presents promising specific paths and considerations for getting closer to superforecaster-level AI technology, aiming to call for researchers' interest in these directions.
Towards the ideals of Self-Recovery and Metadata Privacy in Social Vault Recovery
Venue: arXiv cs.CR 2025
Year: 2025

Social key recovery mechanisms enable users to recover their vaults with the help of trusted contacts, or trustees, avoiding the need for a single point of trust or memorizing complex strings. However, existing mechanisms overlook the memorability demands on users for recovery, such as the need to recall a threshold number of trustees. Therefore, we first formalize the notion of recovery metadata in the context of social key recovery, illustrating the tradeoff between easing the burden of memorizing the metadata and maintaining metadata privacy. We present Apollo, the first framework that addresses this tradeoff by distributing indistinguishable data within a user's social circle, where trustees hold relevant data and non-trustees store random data. Apollo eliminates the need to memorize recovery metadata since a user eventually gathers sufficient data from her social circle for recovery. Due to indistinguishability, Apollo protects metadata privacy by forming an anonymity set that hides the trustees among non-trustees. To make the anonymity set scalable, Apollo proposes a novel multi-layered secret sharing scheme that mitigates the overhead due to the random data distributed among non-trustees. Finally, we provide a prototype implementation of Apollo and report on its performance. Apollo reduces the chances of malicious recovery to between 0.005% and 1.8%, depending on the adversary's ability to compromise. The multi-layered design shows a latency reduction from 1.1x to 740kx compared to a single-layered approach, depending on the number of reconnections.
Confirmation bias: A challenge for scalable oversight
Venue: arXiv cs.HC 2025
Year: 2025

Scalable oversight protocols aim to empower evaluators to accurately verify AI models more capable than themselves. However, human evaluators are subject to biases that can lead to systematic errors. We conduct two studies examining the performance of simple oversight protocols where evaluators know that the model is "correct most of the time, but not all of the time". We find no overall advantage for the tested protocols, although in Study 1, showing arguments in favor of both answers improves accuracy in cases where the model is incorrect. In Study 2, participants in both groups become more confident in the system's answers after conducting online research, even when those answers are incorrect. We also reanalyze data from prior work that was more optimistic about simple protocols, finding that human evaluators possessing knowledge absent from models likely contributed to their positive results--an advantage that diminishes as models continue to scale in capability. These findings underscore the importance of testing the degree to which oversight protocols are robust to evaluator biases, whether they outperform simple deference to the model under evaluation, and whether their performance scales with increasing problem difficulty and model capability.
Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media
Venue: arXiv cs.CL 2025
Year: 2025

The rising prevalence of mental health disorders necessitates the development of robust, automated tools for early detection and monitoring. Recent advances in Natural Language Processing (NLP), particularly transformer-based architectures, have demonstrated significant potential in text analysis. This study provides a comprehensive evaluation of state-of-the-art transformer models (BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA) against Long Short-Term Memory (LSTM) based approaches using different text embedding techniques for mental health disorder classification on Reddit. We construct a large annotated dataset, validating its reliability through statistical judgmental analysis and topic modeling. Experimental results demonstrate the superior performance of transformer models over traditional deep-learning approaches. RoBERTa achieved the highest classification performance, with a 99.54% F1 score on the hold-out test set and a 96.05% F1 score on the external test set. Notably, LSTM models augmented with BERT embeddings proved highly competitive, achieving F1 scores exceeding 94% on the external dataset while requiring significantly fewer computational resources. These findings highlight the effectiveness of transformer-based models for real-time, scalable mental health monitoring. We discuss the implications for clinical applications and digital mental health interventions, offering insights into the capabilities and limitations of state-of-the-art NLP methodologies in mental disorder detection.
Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Accurate spatiotemporal traffic forecasting is vital for intelligent resource management in 5G and beyond. However, conventional AI approaches often fail to capture the intricate spatial and temporal patterns that exist, due to e.g., the mobility of users. We introduce a lightweight, dual-path Spatiotemporal Network that leverages a Scalar LSTM (sLSTM) for efficient temporal modeling and a three-layer Conv3D module for spatial feature extraction. A fusion layer integrates both streams into a cohesive representation, enabling robust forecasting. Our design improves gradient stability and convergence speed while reducing prediction error. Evaluations on real-world datasets show superior forecast performance over ConvLSTM baselines and strong generalization to unseen regions, making it well-suited for large-scale, next-generation network deployments. Experimental evaluation shows a 23% MAE reduction over ConvLSTM, with a 30% improvement in model generalization.
Clinical-Grade Blood Pressure Prediction in ICU Settings: An Ensemble Framework with Uncertainty Quantification and Cross-Institutional Validation
Venue: arXiv cs.LG 2025
Year: 2025

Blood pressure (BP) monitoring is critical in in tensive care units (ICUs) where hemodynamic instability can rapidly progress to cardiovascular collapse. Current machine learning (ML) approaches suffer from three limitations: lack of external validation, absence of uncertainty quantification, and inadequate data leakage prevention. This study presents the first comprehensive framework with novel algorithmic leakage prevention, uncertainty quantification, and cross-institutional validation for electronic health records (EHRs) based BP pre dictions. Our methodology implemented systematic data leakage prevention, uncertainty quantification through quantile regres sion, and external validation between the MIMIC-III and eICU databases. An ensemble framework combines Gradient Boosting, Random Forest, and XGBoost with 74 features across five physiological domains. Internal validation achieved a clinically acceptable performance (for SBP: R^2 = 0.86, RMSE = 6.03 mmHg; DBP: R^2 = 0.49, RMSE = 7.13 mmHg), meeting AAMI standards. External validation showed 30% degradation with critical limitations in patients with hypotensive. Uncertainty quantification generated valid prediction intervals (80.3% SBP and 79.9% DBP coverage), enabling risk-stratified protocols with narrow intervals (< 15 mmHg) for standard monitoring and wide intervals (> 30 mmHg) for manual verification. This framework provides realistic deployment expectations for cross institutional AI-assisted BP monitoring in critical care settings. The source code is publicly available at https://github.com/ mdbasit897/clinical-bp-prediction-ehr.
Swift-Sarsa: Fast and Robust Linear Control
Venue: arXiv cs.LG 2025
Year: 2025

Javed, Sharifnassab, and Sutton (2024) introduced a new algorithm for TD learning -- SwiftTD -- that augments True Online TD($λ$) with step-size optimization, a bound on the effective learning rate, and step-size decay. In their experiments SwiftTD outperformed True Online TD($λ$) and TD($λ$) on a variety of prediction tasks derived from Atari games, and its performance was robust to the choice of hyper-parameters. In this extended abstract we extend SwiftTD to work for control problems. We combine the key ideas behind SwiftTD with True Online Sarsa($λ$) to develop an on-policy reinforcement learning algorithm called $\textit{Swift-Sarsa}$. We propose a simple benchmark for linear on-policy control called the $\textit{operant conditioning benchmark}$. The key challenge in the operant conditioning benchmark is that a very small subset of input signals are relevant for decision making. The majority of the signals are noise sampled from a non-stationary distribution. To learn effectively, the agent must learn to differentiate between the relevant signals and the noisy signals, and minimize prediction errors by assigning credit to the weight parameters associated with the relevant signals. Swift-Sarsa, when applied to the operant conditioning benchmark, learned to assign credit to the relevant signals without any prior knowledge of the structure of the problem. It opens the door for solution methods that learn representations by searching over hundreds of millions of features in parallel without performance degradation due to noisy or bad features.
Origin-Destination Extraction from Large-Scale Route Search Records for Tourism Trend Analysis
Venue: arXiv cs.CY 2025
Year: 2025

This paper presents a novel method for transforming large-scale historical expressway route search records into a three-dimensional (3D) Origin-Destination (OD) map, enabling data compression, efficient spatiotemporal sampling and statistical analysis. The study analyzed over 380 million expressway route search logs to investigate online search behavior related to tourist destinations. Several expressway interchanges (ICs) near popular attractions, such as those associated with spring flower viewing, autumn foliage and winter skiing, are examined and visualized. The results reveal strong correlations between search volume trends and the duration of peak tourism seasons. This approach leverages cyberspace behavioral data as a leading indicator of physical movement, providing a proactive tool for traffic management and tourism planning.
Rainbow Noise: Stress-Testing Multimodal Harmful-Meme Detectors on LGBTQ Content
Venue: arXiv cs.CY 2025
Year: 2025

Hateful memes aimed at LGBTQ\,+ communities often evade detection by tweaking either the caption, the image, or both. We build the first robustness benchmark for this setting, pairing four realistic caption attacks with three canonical image corruptions and testing all combinations on the PrideMM dataset. Two state-of-the-art detectors, MemeCLIP and MemeBLIP2, serve as case studies, and we introduce a lightweight \textbf{Text Denoising Adapter (TDA)} to enhance the latter's resilience. Across the grid, MemeCLIP degrades more gently, while MemeBLIP2 is particularly sensitive to the caption edits that disrupt its language processing. However, the addition of the TDA not only remedies this weakness but makes MemeBLIP2 the most robust model overall. Ablations reveal that all systems lean heavily on text, but architectural choices and pre-training data significantly impact robustness. Our benchmark exposes where current multimodal safety models crack and demonstrates that targeted, lightweight modules like the TDA offer a powerful path towards stronger defences.
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) possess extensive world knowledge, including geospatial knowledge, which has been successfully applied to various geospatial tasks such as mobility prediction and social indicator prediction. However, LLMs often generate inaccurate geospatial knowledge, leading to geospatial hallucinations (incorrect or inconsistent representations of geospatial information) that compromise their reliability. While the phenomenon of general knowledge hallucination in LLMs has been widely studied, the systematic evaluation and mitigation of geospatial hallucinations remain largely unexplored. To address this gap, we propose a comprehensive evaluation framework for geospatial hallucinations, leveraging structured geospatial knowledge graphs for controlled assessment. Through extensive evaluation across 20 advanced LLMs, we uncover the hallucinations in their geospatial knowledge. Building on these insights, we introduce a dynamic factuality aligning method based on Kahneman-Tversky Optimization (KTO) to mitigate geospatial hallucinations in LLMs, leading to a performance improvement of over 29.6% on the proposed benchmark. Extensive experimental results demonstrate the effectiveness of our benchmark and learning algorithm in enhancing the trustworthiness of LLMs in geospatial knowledge and reasoning tasks.
SurgPIS: Surgical-instrument-level Instances and Part-level Semantics for Weakly-supervised Part-aware Instance Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Consistent surgical instrument segmentation is critical for automation in robot-assisted surgery. Yet, existing methods only treat instrument-level instance segmentation (IIS) or part-level semantic segmentation (PSS) separately, without interaction between these tasks. In this work, we formulate a surgical tool segmentation as a unified part-aware instance segmentation (PIS) problem and introduce SurgPIS, the first PIS model for surgical instruments. Our method adopts a transformer-based mask classification approach and introduces part-specific queries derived from instrument-level object queries, explicitly linking parts to their parent instrument instances. In order to address the lack of large-scale datasets with both instance- and part-level labels, we propose a weakly-supervised learning strategy for SurgPIS to learn from disjoint datasets labelled for either IIS or PSS purposes. During training, we aggregate our PIS predictions into IIS or PSS masks, thereby allowing us to compute a loss against partially labelled datasets. A student-teacher approach is developed to maintain prediction consistency for missing PIS information in the partially labelled data, e.g., parts of the IIS labelled data. Extensive experiments across multiple datasets validate the effectiveness of SurgPIS, achieving state-of-the-art performance in PIS as well as IIS, PSS, and instrument-level semantic segmentation.
MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts?
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in Large Language Models (LLMs) have significantly enhanced their code generation capabilities. However, their robustness against adversarial misuse, particularly through multi-turn malicious coding prompts, remains underexplored. In this work, we introduce code decomposition attacks, where a malicious coding task is broken down into a series of seemingly benign subtasks across multiple conversational turns to evade safety filters. To facilitate systematic evaluation, we introduce \benchmarkname{}, a large-scale benchmark designed to evaluate the robustness of code LLMs against both single-turn and multi-turn malicious prompts. Empirical results across open- and closed-source models reveal persistent vulnerabilities, especially under multi-turn scenarios. Fine-tuning on MOCHA improves rejection rates while preserving coding ability, and importantly, enhances robustness on external adversarial datasets with up to 32.4% increase in rejection rates without any additional supervision.
Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures
Venue: arXiv cs.CR 2025
Year: 2025

The Internet of Medical Things (IoMT) has the potential to radically improve healthcare by enabling real-time monitoring, remote diagnostics, and AI-driven decision making. However, the connectivity, embedded intelligence, and inclusion of a wide variety of novel sensors expose medical devices to severe cybersecurity threats, compromising patient safety and data privacy. In addition, many devices also have direct capacity - individually or in conjunction with other IoMT devices - to perform actions on the patient, such as delivering an electrical stimulus, administering a drug, or activating a motor, which can potentially be life-threatening. We provide a taxonomy of potential attacks targeting IoMT, presenting attack surfaces, vulnerabilities, and mitigation strategies across all layers of the IoMT architecture. It answers key questions such as: What makes IoMT security different from traditional IT security? What are the cybersecurity threats to medical devices? How can engineers design secure IoMT systems and protect hospital networks from cyberattacks? By analyzing historical cyber incidents, we highlight critical security gaps and propose practical security guidelines for medical device engineers and security professionals. This work bridges the gap between research and implementation, equipping healthcare stakeholders with actionable insights to build resilient and privacy-preserving IoMT ecosystems. Finally, we present the latest standardization and compliance frameworks, that IoMT security designers should be aware of.
Fully Dynamic Spectral and Cut Sparsifiers for Directed Graphs
Venue: arXiv cs.DS 2025
Year: 2025

Recent years have seen extensive research on directed graph sparsification. In this work, we initiate the study of fast fully dynamic spectral and cut sparsification algorithms for directed graphs. We introduce a new notion of spectral sparsification called degree-balance preserving spectral approximation, which maintains the difference between the in-degree and out-degree of each vertex. The approximation error is measured with respect to the corresponding undirected Laplacian. This notion is equivalent to direct Eulerian spectral approximation when the input graph is Eulerian. Our algorithm achieves an amortized update time of $O(\varepsilon^{-2} \cdot \text{polylog}(n))$ and produces a sparsifier of size $O(\varepsilon^{-2} n \cdot \text{polylog}(n))$. Additionally, we present an algorithm that maintains a constant-factor approximation sparsifier of size $O(n \cdot \text{polylog}(n))$ against an adaptive adversary for $O(\text{polylog}(n))$-partially symmetrized graphs, a notion introduced in [Kyng-Meierhans-Probst Gutenberg '22]. A $β$-partial symmetrization of a directed graph $\vec{G}$ is the union of $\vec{G}$ and $β\cdot G$, where $G$ is the corresponding undirected graph of $\vec{G}$. This algorithm also achieves a polylogarithmic amortized update time. Moreover, we develop a fully dynamic algorithm for maintaining a cut sparsifier for $β$-balanced directed graphs, where the ratio between weighted incoming and outgoing edges of any cut is at most $β$. This algorithm explicitly maintains a cut sparsifier of size $O(\varepsilon^{-2}βn \cdot \text{polylog}(n))$ in worst-case update time $O(\varepsilon^{-2}β\cdot \text{polylog}(n))$.
Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions
Venue: arXiv cs.LG 2025
Year: 2025

Stock trading has always been a challenging task due to the highly volatile nature of the stock market. Making sound trading decisions to generate profit is particularly difficult under such conditions. To address this, we propose four novel loss functions to drive decision-making for a portfolio of stocks. These functions account for the potential profits or losses based with respect to buying or shorting respective stocks, enabling potentially any artificial neural network to directly learn an effective trading strategy. Despite the high volatility in stock market fluctuations over time, training time-series models such as transformers on these loss functions resulted in trading strategies that generated significant profits on a portfolio of 50 different S&P 500 company stocks as compared to a benchmark reinforcment learning techniques and a baseline buy and hold method. As an example, using 2021, 2022 and 2023 as three test periods, the Crossformer model adapted with our best loss function was most consistent, resulting in returns of 51.42%, 51.04% and 48.62% respectively. In comparison, the best performing state-of-the-art reinforcement learning methods, PPO and DDPG, only delivered maximum profits of around 41%, 2.81% and 41.58% for the same periods. The code is available at https://anonymous.4open.science/r/bandit-stock-trading-58C8/README.md.