OpenRaiser/Intern-Atlas huggingface dataset:
QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) have shown impressive performance on reasoning benchmarks like math and logic. While many works have largely assumed well-defined tasks, real-world queries are often underspecified and only solvable by acquiring missing information. We formalize this information-gathering problem as a constraint satisfaction problem (CSP) with missing variable assignments. Using a special case where only one necessary variable assignment is missing, we can evaluate an LLM's ability to identify the minimal necessary question to ask. We present QuestBench, a set of underspecified reasoning tasks solvable by asking at most one question, which includes: (1) Logic-Q: logical reasoning tasks with one missing proposition, (2) Planning-Q: PDDL planning problems with partially-observed initial states, (3) GSM-Q: human-annotated grade school math problems with one unknown variable, and (4) GSME-Q: equation-based version of GSM-Q. The LLM must select the correct clarification question from multiple options. While current models excel at GSM-Q and GSME-Q, they achieve only 40-50% accuracy on Logic-Q and Planning-Q. Analysis shows that the ability to solve well-specified reasoning problems is not sufficient for success on our benchmark: models struggle to identify the right question even when they can solve the fully specified version. This highlights the need for specifically optimizing models' information acquisition capabilities.
Self-Evolving Multi-Agent Simulations for Realistic Clinical Interactions
Venue: arXiv cs.CL 2025
Year: 2025

In this work, we introduce MedAgentSim, an open-source simulated clinical environment with doctor, patient, and measurement agents designed to evaluate and enhance LLM performance in dynamic diagnostic settings. Unlike prior approaches, our framework requires doctor agents to actively engage with patients through multi-turn conversations, requesting relevant medical examinations (e.g., temperature, blood pressure, ECG) and imaging results (e.g., MRI, X-ray) from a measurement agent to mimic the real-world diagnostic process. Additionally, we incorporate self improvement mechanisms that allow models to iteratively refine their diagnostic strategies. We enhance LLM performance in our simulated setting by integrating multi-agent discussions, chain-of-thought reasoning, and experience-based knowledge retrieval, facilitating progressive learning as doctor agents interact with more patients. We also introduce an evaluation benchmark for assessing the LLM's ability to engage in dynamic, context-aware diagnostic interactions. While MedAgentSim is fully automated, it also supports a user-controlled mode, enabling human interaction with either the doctor or patient agent. Comprehensive evaluations in various simulated diagnostic scenarios demonstrate the effectiveness of our approach. Our code, simulation tool, and benchmark are available at \href{https://medagentsim.netlify.app/}.
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation
Venue: arXiv cs.AI 2025
Year: 2025

Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they largely explore variants of existing codebases or similarly constrained design spaces, and (2) they produce large volumes of research artifacts (such as automatically generated papers and code) that are typically evaluated using conference-style paper review with limited evaluation of code. In this work we introduce CodeScientist, a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain (like prompting a language model). We use this paradigm to conduct hundreds of automated experiments on machine-generated ideas broadly in the domain of agents and virtual environments, with the system returning 19 discoveries, 6 of which were judged as being both at least minimally sound and incrementally novel after a multi-faceted evaluation beyond that typically conducted in prior work, including external (conference-style) review, code review, and replication attempts. Moreover, the discoveries span new tasks, agents, metrics, and data, suggesting a qualitative shift from benchmark optimization to broader discoveries.
Assessing the influence of cybersecurity threats and risks on the adoption and growth of digital banking: a systematic literature review
Venue: arXiv cs.CR 2025
Year: 2025

The rapid digitalization of banking services has significantly transformed financial transactions, offering enhanced convenience and efficiency for consumers. However, the increasing reliance on digital banking has also exposed financial institutions and users to a wide range of cybersecurity threats, including phishing, malware, ransomware, data breaches, and unauthorized access. This study systematically examines the influence of cybersecurity threats on digital banking security, adoption, and regulatory compliance by conducting a comprehensive review of 78 peer-reviewed articles published between 2015 and 2024. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this research critically evaluates the most prevalent cyber threats targeting digital banking platforms, the effectiveness of modern security measures, and the role of regulatory frameworks in mitigating financial cybersecurity risks. The findings reveal that phishing and malware attacks remain the most commonly exploited cyber threats, leading to significant financial losses and consumer distrust. Multi-factor authentication (MFA) and biometric security have been widely adopted to combat unauthorized access, while AI-driven fraud detection and blockchain technology offer promising solutions for securing financial transactions. However, the integration of third-party FinTech solutions introduces additional security risks, necessitating stringent regulatory oversight and cybersecurity protocols. The study also highlights that compliance with global cybersecurity regulations, such as GDPR, PSD2, and GLBA, enhances digital banking security by enforcing strict authentication measures, encryption protocols, and real-time fraud monitoring.
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets
Venue: arXiv cs.SD 2025
Year: 2025

Road rage, often triggered by emotional suppression and sudden outbursts, significantly threatens road safety by causing collisions and aggressive behavior. Speech emotion recognition technologies can mitigate this risk by identifying negative emotions early and issuing timely alerts. However, current SER methods, such as those based on hidden markov models and Long short-term memory networks, primarily handle one-dimensional signals, frequently experience overfitting, and lack calibration, limiting their safety-critical effectiveness. We propose a novel risk-controlled prediction framework providing statistically rigorous guarantees on prediction accuracy. This approach employs a calibration set to define a binary loss function indicating whether the true label is included in the prediction set. Using a data-driven threshold $β$, we optimize a joint loss function to maintain an expected test loss bounded by a user-specified risk level $α$. Evaluations across six baseline models and two benchmark datasets demonstrate our framework consistently achieves a minimum coverage of $1 - α$, effectively controlling marginal error rates despite varying calibration-test split ratios (e.g., 0.1). The robustness and generalizability of the framework are further validated through an extension to small-batch online calibration under a local exchangeability assumption. We construct a non-negative test martingale to maintain prediction validity even in dynamic and non-exchangeable environments. Cross-dataset tests confirm our method's ability to uphold reliable statistical guarantees in realistic, evolving data scenarios.
FeatherWallet: A Lightweight Mobile Cryptocurrency Wallet Using zk-SNARKs
Venue: arXiv cs.CR 2025
Year: 2025

Traditionally, mobile wallets rely on a trusted server that provides them with a current view of the blockchain, and thus, these wallets do not need to validate the header chain or transaction inclusion themselves. If a mobile wallet were to validate a header chain and inclusion of its transactions, it would require significant storage and performance overhead, which is challenging and expensive to ensure on resource-limited devices, such as smartphones. Moreover, such an overhead would be multiplied by the number of cryptocurrencies the user holds in a wallet. Therefore, we introduce a novel approach, called FeatherWallet, to mobile wallet synchronization designed to eliminate trust in a server while providing efficient utilization of resources. Our approach addresses the challenges associated with storage and bandwidth requirements by off-chaining validation of header chains using SNARK-based proofs of chain extension, which are verified by a smart contract. This offers us a means of storing checkpoints in header chains of multiple blockchains. The key feature of our approach is the ability of mobile clients to update their partial local header chains using checkpoints derived from the proof verification results stored in the smart contract. In the evaluation, we created zk-SNARK proofs for the 2, 4, 8, 16, 32, and 64 headers within our trustless off-chain service. For 64-header proofs, the off-chain service producing proofs requires at least 40 GB of RAM, while the minimal gas consumption is achieved for 12 proofs bundled in a single transaction. We achieved a 20-fold reduction in storage overhead for a mobile client in contrast to traditional SPV clients. Although we have developed a proof-of-concept for PoW blockchains, the whole approach can be extended in principle to other consensus mechanisms, e.g., PoS.
Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Representation Engineering (RepE) has emerged as a powerful paradigm for enhancing AI transparency by focusing on high-level representations rather than individual neurons or circuits. It has proven effective in improving interpretability and control, showing that representations can emerge, propagate, and shape final model outputs in large language models (LLMs). However, in Vision-Language Models (VLMs), visual input can override factual linguistic knowledge, leading to hallucinated responses that contradict reality. To address this challenge, we make the first attempt to extend RepE to VLMs, analyzing how multimodal representations are preserved and transformed. Building on our findings and drawing inspiration from successful RepE applications, we develop a theoretical framework that explains the stability of neural activity across layers using the principal eigenvector, uncovering the underlying mechanism of RepE. We empirically validate these instrinsic properties, demonstrating their broad applicability and significance. By bridging theoretical insights with empirical validation, this work transforms RepE from a descriptive tool into a structured theoretical framework, opening new directions for improving AI robustness, fairness, and transparency.
PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids
Venue: arXiv cs.LG 2025
Year: 2025

The increasing penetration of renewable energy sources introduces significant variability and uncertainty in modern power systems, making accurate state prediction critical for reliable grid operation. Conventional forecasting methods often neglect the power grid's inherent topology, limiting their ability to capture complex spatio temporal dependencies. This paper proposes a topology aware Graph Neural Network (GNN) framework for predicting power system states under high renewable integration. We construct a graph based representation of the power network, modeling buses and transmission lines as nodes and edges, and introduce a specialized GNN architecture that integrates GraphSAGE convolutions with Gated Recurrent Units (GRUs) to model both spatial and temporal correlations in system dynamics. The model is trained and evaluated on the NREL 118 test system using realistic, time synchronous renewable generation profiles. Our results show that the proposed GNN outperforms baseline approaches including fully connected neural networks, linear regression, and rolling mean models, achieving substantial improvements in predictive accuracy. The GNN achieves average RMSEs of 0.13 to 0.17 across all predicted variables and demonstrates consistent performance across spatial locations and operational conditions. These results highlight the potential of topology aware learning for scalable and robust power system forecasting in future grids with high renewable penetration.
A Spatial-temporal Deep Probabilistic Diffusion Model for Reliable Hail Nowcasting with Radar Echo Extrapolation
Venue: arXiv cs.LG 2025
Year: 2025

Hail nowcasting is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through precise forecast that has high resolution, long lead times and local details with large landscapes. Existing medium-range weather forecasting methods primarily rely on changes in upper air currents and cloud layers to predict precipitation events, such as heavy rainfall, which are unsuitable for hail nowcasting since it is mainly caused by low-altitude local strong convection associated with terrains. Additionally, radar captures the status of low cloud layers, such as water vapor, droplets, and ice crystals, providing rich signals suitable for hail nowcasting. To this end, we introduce a Spatial-Temporal gEnerAtive Model called SteamCast for hail nowcasting with radar echo extrapolation, it is a deep probabilistic diffusion model based on spatial-temporal representations including radar echoes as well as their position/time embeddings, which we trained on historical reanalysis archive from Yan'an Meteorological Bureau in China, where the crop yield like apple suffers greatly from hail damage. Considering the short-term nature of hail, SteamCast provides 30-minute nowcasts at 6-minute intervals for a single radar reflectivity variable, across 9 different vertical angles, on a latitude-longitude grid with approximately 1 km * 1 km resolution per pixel in Yan'an City, China. By successfully fusing the spatial-temporal features of radar echoes, SteamCast delivers competitive, and in some cases superior, results compared to other deep learning-based models such as PredRNN and VMRNN.
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection
Venue: arXiv cs.LG 2025
Year: 2025

Neural Architecture Search (NAS) is an automated technique to design optimal neural network architectures for a specific workload. Conventionally, evaluating candidate networks in NAS involves extensive training, which requires significant time and computational resources. To address this, training-free NAS has been proposed to expedite network evaluation with minimal search time. However, state-of-the-art training-free NAS algorithms struggle to precisely distinguish well-performing networks from poorly-performing networks, resulting in inaccurate performance predictions and consequently sub-optimal top-1 network accuracy. Moreover, they are less effective in activation function exploration. To tackle the challenges, this paper proposes RBFleX-NAS, a novel training-free NAS framework that accounts for both activation outputs and input features of the last layer with a Radial Basis Function (RBF) kernel. We also present a detection algorithm to identify optimal hyperparameters using the obtained activation outputs and input feature maps. We verify the efficacy of RBFleX-NAS over a variety of NAS benchmarks. RBFleX-NAS significantly outperforms state-of-the-art training-free NAS methods in terms of top-1 accuracy, achieving this with short search time in NAS-Bench-201 and NAS-Bench-SSS. In addition, it demonstrates higher Kendall correlation compared to layer-based training-free NAS algorithms. Furthermore, we propose NAFBee, a new activation design space that extends the activation type to encompass various commonly used functions. In this extended design space, RBFleX-NAS demonstrates its superiority by accurately identifying the best-performing network during activation function search, providing a significant advantage over other NAS algorithms.
A Methodology to extract Geo-Referenced Standard Routes from AIS Data
Venue: arXiv cs.LG 2025
Year: 2025

Maritime AIS (Automatic Identification Systems) data serve as a valuable resource for studying vessel behavior. This study proposes a methodology to analyze route between maritime points of interest and extract geo-referenced standard routes, as maritime patterns of life, from raw AIS data. The underlying assumption is that ships adhere to consistent patterns when travelling in certain maritime areas due to geographical, environmental, or economic factors. Deviations from these patterns may be attributed to weather conditions, seasonality, or illicit activities. This enables maritime surveillance authorities to analyze the navigational behavior between ports, providing insights on vessel route patterns, possibly categorized by vessel characteristics (type, flag, or size). Our methodological process begins by segmenting AIS data into distinct routes using a finite state machine (FSM), which describes routes as seg-ments connecting pairs of points of interest. The extracted segments are ag-gregated based on their departure and destination ports and then modelled using iterative density-based clustering to connect these ports. The cluster-ing parameters are assigned manually to sample and then extended to the en-tire dataset using linear regression. Overall, the approach proposed in this paper is unsupervised and does not require any ground truth to be trained. The approach has been tested on data on the on a six-year AIS dataset cover-ing the Arctic region and the Europe, Middle East, North Africa areas. The total size of our dataset is 1.15 Tbytes. The approach has proved effective in extracting standard routes, with less than 5% outliers, mostly due to routes with either their departure or their destination port not included in the test areas.
CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization
Venue: arXiv cs.LG 2025
Year: 2025

The stock market, as a cornerstone of the financial markets, places forecasting stock price movements at the forefront of challenges in quantitative finance. Emerging learning-based approaches have made significant progress in capturing the intricate and ever-evolving data patterns of modern markets. With the rapid expansion of the stock market, it presents two characteristics, i.e., stock exogeneity and volatility heterogeneity, that heighten the complexity of price forecasting. Specifically, while stock exogeneity reflects the influence of external market factors on price movements, volatility heterogeneity showcases the varying difficulty in movement forecasting against price fluctuations. In this work, we introduce the framework of Cross-market Synergy with Pseudo-volatility Optimization (CSPO). Specifically, CSPO implements an effective deep neural architecture to leverage external futures knowledge. This enriches stock embeddings with cross-market insights and thus enhances the CSPO's predictive capability. Furthermore, CSPO incorporates pseudo-volatility to model stock-specific forecasting confidence, enabling a dynamic adaptation of its optimization process to improve accuracy and robustness. Our extensive experiments, encompassing industrial evaluation and public benchmarking, highlight CSPO's superior performance over existing methods and effectiveness of all proposed modules contained therein.
Adaptive Integrated Layered Attention (AILA)
Venue: arXiv cs.LG 2025
Year: 2025

We propose Adaptive Integrated Layered Attention (AILA), a neural network architecture that combines dense skip connections with different mechanisms for adaptive feature reuse across network layers. We evaluate AILA on three challenging tasks: price forecasting for various commodities and indices (S&P 500, Gold, US dollar Futures, Coffee, Wheat), image recognition using the CIFAR-10 dataset, and sentiment analysis on the IMDB movie review dataset. In all cases, AILA matches strong deep learning baselines (LSTMs, Transformers, and ResNets), achieving it at a fraction of the training and inference time. Notably, we implement and test two versions of the model - AILA-Architecture 1, which uses simple linear layers as the connection mechanism between layers, and AILA-Architecture 2, which implements an attention mechanism to selectively focus on outputs from previous layers. Both architectures are applied in a single-task learning setting, with each model trained separately for individual tasks. Results confirm that AILA's adaptive inter-layer connections yield robust gains by flexibly reusing pertinent features at multiple network depths. The AILA approach thus presents an extension to existing architectures, improving long-range sequence modeling, image recognition with optimised computational speed, and SOTA classification performance in practice.
From Individual to Group: Developing a Context-Aware Multi-Criteria Group Recommender System
Venue: arXiv cs.LG 2025
Year: 2025

Group decision-making is becoming increasingly common in areas such as education, dining, travel, and finance, where collaborative choices must balance diverse individual preferences. While conventional recommender systems are effective in personalization, they fall short in group settings due to their inability to manage conflicting preferences, contextual factors, and multiple evaluation criteria. This study presents the development of a Context-Aware Multi-Criteria Group Recommender System (CA-MCGRS) designed to address these challenges by integrating contextual factors and multiple criteria to enhance recommendation accuracy. By leveraging a Multi-Head Attention mechanism, our model dynamically weighs the importance of different features. Experiments conducted on an educational dataset with varied ratings and contextual variables demonstrate that CA-MCGRS consistently outperforms other approaches across four scenarios. Our findings underscore the importance of incorporating context and multi-criteria evaluations to improve group recommendations, offering valuable insights for developing more effective group recommender systems.
Randomization to Reduce Terror Threats at Large Venues
Venue: arXiv cs.CY 2025
Year: 2025

Can randomness be better than scheduled practices, for securing an event at a large venue such as a stadium or entertainment arena? Perhaps surprisingly, from several perspectives the answer is "yes." This note examines findings from an extensive study of the problem, including interviews and a survey of selected venue security directors. That research indicates that: randomness has several goals; many security directors recognize its potential; but very few have used it much, if at all. Some fear they will not be able to defend using random methods if an adversary does slip through security. Others are concerned that staff may not be able to perform effectively. We discuss ways in which it appears that randomness can improve effectiveness, ways it can be effectively justified to those who must approve security processes, and some potential research or regulatory advances.