OpenRaiser/Intern-Atlas huggingface dataset:
Downside Risk-Aware Equilibria for Strategic Decision-Making
Venue: arXiv cs.GT 2025
Year: 2025

Game theory has traditionally had a relatively limited view of risk based on how a player's expected reward is impacted by the uncertainty of the actions of other players. Recently, a new game-theoretic approach provides a more holistic view of risk also considering the reward-variance. However, these variance-based approaches measure variance of the reward on both the upside and downside. In many domains, such as finance, downside risk only is of key importance, as this represents the potential losses associated with a decision. In contrast, large upside "risk" (e.g. profits) are not an issue. To address this restrictive view of risk, we propose a novel solution concept, downside risk aware equilibria (DRAE) based on lower partial moments. DRAE restricts downside risk, while placing no restrictions on upside risk, and additionally, models higher-order risk preferences. We demonstrate the applicability of DRAE on several games, successfully finding equilibria which balance downside risk with expected reward, and prove the existence and optimality of this equilibria.
Denoising of Two-Phase Optically Sectioned Structured Illumination Reconstructions Using Encoder-Decoder Networks
Venue: arXiv cs.CV 2025
Year: 2025

Structured illumination (SI) enhances image resolution and contrast by projecting patterned light onto a sample. In two-phase optical-sectioning SI (OS-SI), reduced acquisition time introduces residual artifacts that conventional denoising struggles to suppress. Deep learning offers an alternative to traditional methods; however, supervised training is limited by the lack of clean, optically sectioned ground-truth data. We investigate encoder-decoder networks for artifact reduction in two-phase OS-SI, using synthetic training pairs formed by applying real artifact fields to synthetic images. An asymmetrical denoising autoencoder (DAE) and a U-Net are trained on the synthetic data, then evaluated on real OS-SI images. Both networks improve image clarity, with each excelling against different artifact types. These results demonstrate that synthetic training enables supervised denoising of OS-SI images and highlight the potential of encoder-decoder networks to streamline reconstruction workflows.
Optimal swimming with body compliance in an overdamped medium
Venue: arXiv cs.RO 2025
Year: 2025

Elongate animals and robots use undulatory body waves to locomote through diverse environments. Geometric mechanics provides a framework to model and optimize such systems in highly damped environments, connecting a prescribed shape change pattern (gait) with locomotion displacement. However, the practical applicability of controlling compliant physical robots remains to be demonstrated. In this work, we develop a framework based on geometric mechanics to predict locomotor performance and search for optimal swimming strategies of compliant swimmers. We introduce a compliant extension of Purcell's three-link swimmer by incorporating series-connected springs at the joints. Body dynamics are derived using resistive force theory. Geometric mechanics is incorporated into movement prediction and into an optimization framework that identifies strategies for controlling compliant swimmers to achieve maximal displacement. We validate our framework on a physical cable-driven three-link limbless robot and demonstrate accurate prediction and optimization of locomotor performance under varied programmed, state-dependent compliance in a granular medium. Our results establish a systematic, physics-based approach for modeling and controlling compliant swimming locomotion, highlighting compliance as a design feature that can be exploited for robust movement in both homogeneous and heterogeneous environments.
Bridging LLM Planning Agents and Formal Methods: A Case Study in Plan Verification
Venue: arXiv cs.AI 2025
Year: 2025

We introduce a novel framework for evaluating the alignment between natural language plans and their expected behavior by converting them into Kripke structures and Linear Temporal Logic (LTL) using Large Language Models (LLMs) and performing model checking. We systematically evaluate this framework on a simplified version of the PlanBench plan verification dataset and report on metrics like Accuracy, Precision, Recall and F1 scores. Our experiments demonstrate that GPT-5 achieves excellent classification performance (F1 score of 96.3%) while almost always producing syntactically perfect formal representations that can act as guarantees. However, the synthesis of semantically perfect formal models remains an area for future exploration.
How to Set $β_1, β_2$ in Adam: An Online Learning Perspective
Venue: arXiv cs.LG 2025
Year: 2025

While Adam is one of the most effective optimizer for training large-scale machine learning models, a theoretical understanding of how to optimally set its momentum factors, $β_1$ and $β_2$, remains largely incomplete. Prior works have shown that Adam can be seen as an instance of Follow-the-Regularized-Leader (FTRL), one of the most important class of algorithms in online learning. The prior analyses in these works required setting $β_1 = \sqrt{β_2}$, which does not cover the more practical cases with $β_1 \neq \sqrt{β_2}$. We derive novel, more general analyses that hold for both $β_1 \geq \sqrt{β_2}$ and $β_1 \leq \sqrt{β_2}$. In both cases, our results strictly generalize the existing bounds. Furthermore, we show that our bounds are tight in the worst case. We also prove that setting $β_1 = \sqrt{β_2}$ is optimal for an oblivious adversary, but sub-optimal for an non-oblivious adversary.
Analyzing the Performance of a 2.72kWp Rooftop Grid tied Photovoltaic System in Tarlac City, Philippines
Venue: arXiv cs.CY 2025
Year: 2025

Residential and industrial areas are using rooftop grid-tied Photovoltaic (PV) systems, which are becoming increasingly popular. This is because solar energy reduces electrical consumption and provides free energy, while also lowering carbon emissions to create a more sustainable environment. This paper aims to analyze the 2.72kW p rooftop grid-tied PV system performance between 2020 and 2023 in Tarlac City, Philippines. The PV generated yearly is measured by Array Yield (YA), Reference Yield (YR), and Final Yield (YF), which were found to be valued at 3.12, 3.9, and 3.01 kWh/kWp, respectively. The efficiency can decrease due to System Loss (LS) and Capture Loss (LC), which were 0.78 and 0.12 kWh/kWp, respectively. This results in a Capacity Utilization Factor (CUF) of 15.52% and a Performance Ratio (PR) of 77.10%. The productivity of PV resulted in an array efficiency was 12.89%, an inverter efficiency was 94.3%, and a system efficiency was 12.16%. PV energy generation was 3,699 kWh, with 2380 kWh fed into the grid annually. The system's annual revenue is $690.59. The payback period is 6 years with a 238.2% Return On Investment (ROI). Carbon emissions are reduced by 0.379 tCO2/kWp/yr.
Real-Time Threaded Houbara Detection and Segmentation for Wildlife Conservation using Mobile Platforms
Venue: arXiv cs.CV 2025
Year: 2025

Real-time animal detection and segmentation in natural environments are vital for wildlife conservation, enabling non-invasive monitoring through remote camera streams. However, these tasks remain challenging due to limited computational resources and the cryptic appearance of many species. We propose a mobile-optimized two-stage deep learning framework that integrates a Threading Detection Model (TDM) to parallelize YOLOv10-based detection and MobileSAM-based segmentation. Unlike prior YOLO+SAM pipelines, our approach improves real-time performance by reducing latency through threading. YOLOv10 handles detection while MobileSAM performs lightweight segmentation, both executed concurrently for efficient resource use. On the cryptic Houbara Bustard, a conservation-priority species, our model achieves mAP50 of 0.9627, mAP75 of 0.7731, mAP95 of 0.7178, and a MobileSAM mIoU of 0.7421. YOLOv10 operates at 43.7 ms per frame, confirming real-time readiness. We introduce a curated Houbara dataset of 40,000 annotated images to support model training and evaluation across diverse conditions. The code and dataset used in this study are publicly available on GitHub at https://github.com/LyesSaadSaoud/mobile-houbara-detseg. For interactive demos and additional resources, visit https://lyessaadsaoud.github.io/LyesSaadSaoud-Threaded-YOLO-SAM-Houbara.
D2 Actor Critic: Diffusion Actor Meets Distributional Critic
Venue: arXiv cs.LG 2025
Year: 2025

We introduce D2AC, a new model-free reinforcement learning (RL) algorithm designed to train expressive diffusion policies online effectively. At its core is a policy improvement objective that avoids the high variance of typical policy gradients and the complexity of backpropagation through time. This stable learning process is critically enabled by our second contribution: a robust distributional critic, which we design through a fusion of distributional RL and clipped double Q-learning. The resulting algorithm is highly effective, achieving state-of-the-art performance on a benchmark of eighteen hard RL tasks, including Humanoid, Dog, and Shadow Hand domains, spanning both dense-reward and goal-conditioned RL scenarios. Beyond standard benchmarks, we also evaluate a biologically motivated predator-prey task to examine the behavioral robustness and generalization capacity of our approach.
Red Lines and Grey Zones in the Fog of War: Benchmarking Legal Risk, Moral Harm, and Regional Bias in Large Language Model Military Decision-Making
Venue: arXiv cs.CY 2025
Year: 2025

As military organisations consider integrating large language models (LLMs) into command and control (C2) systems for planning and decision support, understanding their behavioural tendencies is critical. This study develops a benchmarking framework for evaluating aspects of legal and moral risk in targeting behaviour by comparing LLMs acting as agents in multi-turn simulated conflict. We introduce four metrics grounded in International Humanitarian Law (IHL) and military doctrine: Civilian Target Rate (CTR) and Dual-use Target Rate (DTR) assess compliance with legal targeting principles, while Mean and Max Simulated Non-combatant Casualty Value (SNCV) quantify tolerance for civilian harm. We evaluate three frontier models, GPT-4o, Gemini-2.5, and LLaMA-3.1, through 90 multi-agent, multi-turn crisis simulations across three geographic regions. Our findings reveal that off-the-shelf LLMs exhibit concerning and unpredictable targeting behaviour in simulated conflict environments. All models violated the IHL principle of distinction by targeting civilian objects, with breach rates ranging from 16.7% to 66.7%. Harm tolerance escalated through crisis simulations with MeanSNCV increasing from 16.5 in early turns to 27.7 in late turns. Significant inter-model variation emerged: LLaMA-3.1 selected an average of 3.47 civilian strikes per simulation with MeanSNCV of 28.4, while Gemini-2.5 selected 0.90 civilian strikes with MeanSNCV of 17.6. These differences indicate that model selection for deployment constitutes a choice about acceptable legal and moral risk profiles in military operations. This work seeks to provide a proof-of-concept of potential behavioural risks that could emerge from the use of LLMs in Decision Support Systems (AI DSS) as well as a reproducible benchmarking framework with interpretable metrics for standardising pre-deployment testing.
RAPID: An Efficient Reinforcement Learning Algorithm for Small Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) has emerged as a promising strategy for finetuning small language models (SLMs) to solve targeted tasks such as math and coding. However, RL algorithms tend to be resource-intensive, taking a significant amount of time to train. We propose RAPID, a novel RL algorithm that can substantially reduce the running time of RL. Our key insight is that RL tends to be costly due to the need to perform both inference and backpropagation during training. To maximize use of computational resources, our algorithm performs inference in large batches, and then performs off-policy policy gradient updates in mini-batches. For off-policy updates, we incorporate group advantage estimation into the policy gradient algorithm, and derive an importance weighted estimator to correct for the bias arising from off-policy learning. Our experiments demonstrate that our algorithm can reduce running time by 11%-34% on three benchmarks compared to state-of-the-art RL algorithms while maintaining similar or better accuracy.
Sample, Align, Synthesize: Graph-Based Response Synthesis with ConGrs
Venue: arXiv cs.CL 2025
Year: 2025

Language models can be sampled multiple times to access the distribution underlying their responses, but existing methods cannot efficiently synthesize rich epistemic signals across different long-form responses. We introduce Consensus Graphs (ConGrs), a flexible DAG-based data structure that represents shared information, as well as semantic variation in a set of sampled LM responses to the same prompt. We construct ConGrs using a light-weight lexical sequence alignment algorithm from bioinformatics, supplemented by the targeted usage of a secondary LM judge. Further, we design task-dependent decoding methods to synthesize a single, final response from our ConGr data structure. Our experiments show that synthesizing responses from ConGrs improves factual precision on two biography generation tasks by up to 31% over an average response and reduces reliance on LM judges by more than 80% compared to other methods. We also use ConGrs for three refusal-based tasks requiring abstention on unanswerable queries and find that abstention rate is increased by up to 56%. We apply our approach to the MATH and AIME reasoning tasks and find an improvement over self-verification and majority vote baselines by up to 6 points of accuracy. We show that ConGrs provide a flexible method for capturing variation in LM responses and using the epistemic signals provided by response variation to synthesize more effective responses.
Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance
Venue: arXiv cs.CL 2025
Year: 2025

Instruction-tuning plays a vital role in enhancing the task-solving abilities of large language models (LLMs), improving their usability in generating helpful responses on various tasks. However, previous work has demonstrated that they are sensitive to minor variations in instruction phrasing. In this paper, we explore whether introducing perturbations in instruction-tuning data can enhance LLMs' resistance against noisy instructions. We focus on how instruction-tuning with perturbations, such as removing stop words or shuffling words, affects LLMs' performance on the original and perturbed versions of widely-used benchmarks (MMLU, BBH, GSM8K). We further assess learning dynamics and potential shifts in model behavior. Surprisingly, our results suggest that instruction-tuning on perturbed instructions can, in some cases, improve downstream performance. These findings highlight the importance of including perturbed instructions in instruction-tuning, which can make LLMs more resilient to noisy user inputs.
A Multi-Layer Electronic and Cyber Interference Model for AI-Driven Cruise Missiles: The Case of Khuzestan Province
Venue: arXiv cs.CR 2025
Year: 2025

The rapid advancement of Artificial Intelligence has enabled the development of cruise missiles endowed with high levels of autonomy, adaptability, and precision. These AI driven missiles integrating deep learning algorithms, real time data processing, and advanced guidance systems pose critical threats to strategic infrastructures, especially under complex geographic and climatic conditions such as those found in Irans Khuzestan Province. In this paper, we propose a multi layer interference model, encompassing electronic warfare, cyberattacks, and deception strategies, to degrade the performance of AI guided cruise missiles significantly. Our experimental results, derived from 400 simulation runs across four distinct scenarios, demonstrate notable improvements when employing the integrated multi layer approach compared to single layer or no interference baselines. Specifically, the average missile deviation from its intended target increases from 0.25 to 8.65 under multi layer interference a more than 3300 increase in angular deviation. Furthermore, the target acquisition success rate is reduced from 92.7 in the baseline scenario to 31.5, indicating a 66 decrease in successful strikes. While resource consumption for multi layer strategies rises by approximately 25 compared to single layer methods, the significant drop in missile accuracy and reliability justifies the more intensive deployment of jamming power, cyber resources, and decoy measures. Beyond these quantitative improvements, the proposed framework uses a deep reinforcement learning based defense coordinator to adaptively select the optimal configuration of EW, cyber, and deception tactics in real time.
Real-Time Assessment of Bystander Situation Awareness in Drone-Assisted First Aid
Venue: arXiv cs.CV 2025
Year: 2025

Rapid naloxone delivery via drones offers a promising solution for responding to opioid overdose emergencies (OOEs), by extending lifesaving interventions to medically untrained bystanders before emergency medical services (EMS) arrive. Recognizing the critical role of bystander situational awareness (SA) in human-autonomy teaming (HAT), we address a key research gap in real-time SA assessment by introducing the Drone-Assisted Naloxone Delivery Simulation Dataset (DANDSD). This pioneering dataset captures HAT during simulated OOEs, where college students without medical training act as bystanders tasked with administering intranasal naloxone to a mock overdose victim. Leveraging this dataset, we propose a video-based real-time SA assessment framework that utilizes graph embeddings and transformer models to assess bystander SA in real time. Our approach integrates visual perception and comprehension cues--such as geometric, kinematic, and interaction graph features--and achieves high-performance SA prediction. It also demonstrates strong temporal segmentation accuracy, outperforming the FINCH baseline by 9% in Mean over Frames (MoF) and 5% in Intersection over Union (IoU). This work supports the development of adaptive drone systems capable of guiding bystanders effectively, ultimately improving emergency response outcomes and saving lives.
PrivacyMotiv: Speculative Persona Journeys for Empathic and Motivating Privacy Reviews in UX Design
Venue: arXiv cs.CR 2025
Year: 2025

UX professionals routinely conduct design reviews, yet privacy concerns are often overlooked -- not only due to limited tools, but more critically because of low intrinsic motivation. Limited privacy knowledge, weak empathy for unexpectedly affected users, and low confidence in identifying harms make it difficult to address risks. We present PrivacyMotiv, an LLM-powered system that supports privacy-oriented design diagnosis by generating speculative personas with UX user journeys centered on individuals vulnerable to privacy risks. Drawing on narrative strategies, the system constructs relatable and attention-drawing scenarios that show how ordinary design choices may cause unintended harms, expanding the scope of privacy reflection in UX. In a within-subjects study with professional UX practitioners (N=16), we compared participants' self-proposed methods with PrivacyMotiv across two privacy review tasks. Results show significant improvements in empathy, intrinsic motivation, and perceived usefulness. This work contributes a promising privacy review approach which addresses the motivational barriers in privacy-aware UX.
LLM, Reporting In! Medical Information Extraction Across Prompting, Fine-tuning and Post-correction
Venue: arXiv cs.CL 2025
Year: 2025

This work presents our participation in the EvalLLM 2025 challenge on biomedical Named Entity Recognition (NER) and health event extraction in French (few-shot setting). For NER, we propose three approaches combining large language models (LLMs), annotation guidelines, synthetic data, and post-processing: (1) in-context learning (ICL) with GPT-4.1, incorporating automatic selection of 10 examples and a summary of the annotation guidelines into the prompt, (2) the universal NER system GLiNER, fine-tuned on a synthetic corpus and then verified by an LLM in post-processing, and (3) the open LLM LLaMA-3.1-8B-Instruct, fine-tuned on the same synthetic corpus. Event extraction uses the same ICL strategy with GPT-4.1, reusing the guideline summary in the prompt. Results show GPT-4.1 leads with a macro-F1 of 61.53% for NER and 15.02% for event extraction, highlighting the importance of well-crafted prompting to maximize performance in very low-resource scenarios.
Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends
Venue: arXiv cs.LG 2025
Year: 2025

Remaining Useful Life (RUL) prediction for turbofan engines plays a vital role in predictive maintenance, ensuring operational safety and efficiency in aviation. Although data-driven approaches using machine learning and deep learning have shown potential, they face challenges such as limited data and distribution shifts caused by varying operating conditions. Domain Adaptation (DA) has emerged as a promising solution, enabling knowledge transfer from source domains with abundant data to target domains with scarce data while mitigating distributional shifts. Given the unique properties of turbofan engines, such as complex operating conditions, high-dimensional sensor data, and slower-changing signals, it is essential to conduct a focused review of DA techniques specifically tailored to turbofan engines. To address this need, this paper provides a comprehensive review of DA solutions for turbofan engine RUL prediction, analyzing key methodologies, challenges, and recent advancements. A novel taxonomy tailored to turbofan engines is introduced, organizing approaches into methodology-based (how DA is applied), alignment-based (where distributional shifts occur due to operational variations), and problem-based (why certain adaptations are needed to address specific challenges). This taxonomy offers a multidimensional view that goes beyond traditional classifications by accounting for the distinctive characteristics of turbofan engine data and the standard process of applying DA techniques to this area. Additionally, we evaluate selected DA techniques on turbofan engine datasets, providing practical insights for practitioners and identifying key challenges. Future research directions are identified to guide the development of more effective DA techniques, advancing the state of RUL prediction for turbofan engines.