OpenRaiser/Intern-Atlas huggingface dataset:
AURA: Development and Validation of an Augmented Unplanned Removal Alert System using Synthetic ICU Videos
Venue: arXiv cs.AI 2025
Year: 2025

Unplanned extubation (UE) remains a critical patient safety concern in intensive care units (ICUs), often leading to severe complications or death. Real-time UE detection has been limited, largely due to the ethical and privacy challenges of obtaining annotated ICU video data. We propose Augmented Unplanned Removal Alert (AURA), a vision-based risk detection system developed and validated entirely on a fully synthetic video dataset. By leveraging text-to-video diffusion, we generated diverse and clinically realistic ICU scenarios capturing a range of patient behaviors and care contexts. The system applies pose estimation to identify two high-risk movement patterns: collision, defined as hand entry into spatial zones near airway tubes, and agitation, quantified by the velocity of tracked anatomical keypoints. Expert assessments confirmed the realism of the synthetic data, and performance evaluations showed high accuracy for collision detection and moderate performance for agitation recognition. This work demonstrates a novel pathway for developing privacy-preserving, reproducible patient safety monitoring systems with potential for deployment in intensive care settings.
ViConBERT: Context-Gloss Aligned Vietnamese Word Embedding for Polysemous and Sense-Aware Representations
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in contextualized word embeddings have greatly improved semantic tasks such as Word Sense Disambiguation (WSD) and contextual similarity, but most progress has been limited to high-resource languages like English. Vietnamese, in contrast, still lacks robust models and evaluation resources for fine-grained semantic understanding. In this paper, we present ViConBERT, a novel framework for learning Vietnamese contextualized embeddings that integrates contrastive learning (SimCLR) and gloss-based distillation to better capture word meaning. We also introduce ViConWSD, the first large-scale synthetic dataset for evaluating semantic understanding in Vietnamese, covering both WSD and contextual similarity. Experimental results show that ViConBERT outperforms strong baselines on WSD (F1 = 0.87) and achieves competitive performance on ViCon (AP = 0.88) and ViSim-400 (Spearman's rho = 0.60), demonstrating its effectiveness in modeling both discrete senses and graded semantic relations. Our code, models, and data are available at https://github.com/tkhangg0910/ViConBERT
A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation
Venue: arXiv cs.CV 2025
Year: 2025

Radiology report generation from chest X-rays is an important task in artificial intelligence with the potential to greatly reduce radiologists' workload and shorten patient wait times. Despite recent advances, existing approaches often lack sufficient disease-awareness in visual representations and adequate vision-language alignment to meet the specialized requirements of medical image analysis. As a result, these models usually overlook critical pathological features on chest X-rays and struggle to generate clinically accurate reports. To address these limitations, we propose a novel dual-stage disease-aware framework for chest X-ray report generation. In Stage~1, our model learns Disease-Aware Semantic Tokens (DASTs) corresponding to specific pathology categories through cross-attention mechanisms and multi-label classification, while simultaneously aligning vision and language representations via contrastive learning. In Stage~2, we introduce a Disease-Visual Attention Fusion (DVAF) module to integrate disease-aware representations with visual features, along with a Dual-Modal Similarity Retrieval (DMSR) mechanism that combines visual and disease-specific similarities to retrieve relevant exemplars, providing contextual guidance during report generation. Extensive experiments on benchmark datasets (i.e., CheXpert Plus, IU X-ray, and MIMIC-CXR) demonstrate that our disease-aware framework achieves state-of-the-art performance in chest X-ray report generation, with significant improvements in clinical accuracy and linguistic quality.
Calibrated Adversarial Sampling: Multi-Armed Bandit-Guided Generalization Against Unforeseen Attacks
Venue: arXiv cs.LG 2025
Year: 2025

Deep Neural Networks (DNNs) are known to be vulnerable to various adversarial perturbations. To address the safety concerns arising from these vulnerabilities, adversarial training (AT) has emerged as one of the most effective paradigms for enhancing the robustness of DNNs. However, existing AT frameworks primarily focus on a single or a limited set of attack types, leaving DNNs still exposed to attack types that may be encountered in practice but not addressed during training. In this paper, we propose an efficient fine-tuning method called Calibrated Adversarial Sampling (CAS) to address these issues. From the optimization perspective within the multi-armed bandit framework, it dynamically designs rewards and balances exploration and exploitation by considering the dynamic and interdependent characteristics of multiple robustness dimensions. Experiments on benchmark datasets show that CAS achieves superior overall robustness while maintaining high clean accuracy, providing a new paradigm for robust generalization of DNNs.
MoralReason: Generalizable Moral Decision Alignment For LLM Agents Using Reasoning-Level Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

Large language models are increasingly influencing human moral decisions, yet current approaches focus primarily on evaluating rather than actively steering their moral decisions. We formulate this as an out-of-distribution moral alignment problem, where LLM agents must learn to apply consistent moral reasoning frameworks to scenarios beyond their training distribution. We introduce Moral-Reason-QA, a novel dataset extending 680 human-annotated, high-ambiguity moral scenarios with framework-specific reasoning traces across utilitarian, deontological, and virtue ethics, enabling systematic evaluation of moral generalization in realistic decision contexts. Our learning approach employs Group Relative Policy Optimization with composite rewards that simultaneously optimize decision alignment and framework-specific reasoning processes to facilitate learning of the underlying moral frameworks. Experimental results demonstrate successful generalization to unseen moral scenarios, with softmax-normalized alignment scores improving by +0.757 for utilitarian and +0.450 for deontological frameworks when tested on out-of-distribution evaluation sets. The experiments also reveal training challenges and promising directions that inform future research. These findings establish that LLM agents can be systematically trained to internalize and apply specific moral frameworks to novel situations, providing a critical foundation for AI safety as language models become more integrated into human decision-making processes.
Reflections on the design, applications and implementations of the normative specification language eFLINT
Venue: arXiv cs.SE 2025
Year: 2025

Checking the compliance of software against laws, regulations and contracts is increasingly important and costly as the embedding of software into societal practices is getting more pervasive. Moreover, the digitalised services provided by governmental organisations and companies are governed by an increasing amount of laws and regulations, requiring highly adaptable compliance practices. A potential solution is to automate compliance using software. However, automating compliance is difficult for various reasons. Legal practices involve subjective processes such as interpretation and qualification. New laws and regulations come into effect regularly and laws and regulations, as well as their interpretations, are subjected to constant revision. In addition, computational reasoning with laws requires a cross-disciplinary process involving both legal and software expertise. This paper reflects on the domain-specific software language eFLINT developed to experiment with novel solutions. The language combines declarative and procedural elements to reason about situations and scenarios respectively, explicates and formalises connections between legal concepts and computational concepts, and is designed to automate compliance checks both before, during and after a software system runs. The various goals and applications areas for the language give rise to (conflicting) requirements. This paper reflects on the current design of the language by recalling various applications, the requirements they imposed, and subsequent design decisions. As such, this paper reports on results and insights of an investigation that can benefit language developers within the field of automated compliance.
Tight Lower Bounds on the Bandwidth Cost of MDS Convertible Codes in the Split Regime
Venue: arXiv cs.IT 2025
Year: 2025

Recent advances in erasure coding for distributed storage systems have demonstrated that adapting redundancy to varying disk failure rates can lead to substantial storage savings. Such adaptation requires code conversion, wherein data encoded under an initial $[k^I + r^I, k^I]$ code is transformed into data encoded under a final $[k^F + r^F, k^F]$ code - an operation that can be resource-intensive. Convertible codes are a class of codes designed to facilitate this transformation efficiently while preserving desirable properties such as the MDS property. In this work, we investigate the fundamental limits on the bandwidth cost of conversion (total amount of data transferred between the storage nodes during conversion) for systematic MDS convertible codes. Specifically, we study the subclass of conversions known as the split regime (a single initial codeword is converted into multiple final codewords). In this setting, prior to this work, the best known lower bounds on the bandwidth cost of conversion for all parameters were derived by Maturana and Rashmi under certain uniformity assumptions on the number of symbols downloaded from each node. Further, these bounds were shown to be tight for the parameter regime where $r^F \geq k^F$ or $r^I \leq r^F$. In this work, we derive lower bounds on the bandwidth cost of systematic MDS convertible codes for all parameters in the split regime without the uniformity assumption. Moreover, our bounds are tight for the broader parameter regime where $r^F \geq k^F$ or $r^I \leq k^F$. Subsequently, our bounds also partially resolve the conjecture proposed by Maturana and Rashmi. We employ a novel information-theoretic framework, which assumes only that the initial and final codes are systematic and does not rely on any linearity assumptions or the aforementioned uniformity assumptions.
D$^{3}$ToM: Decider-Guided Dynamic Token Merging for Accelerating Diffusion MLLMs
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion-based multimodal large language models (Diffusion MLLMs) have recently demonstrated impressive non-autoregressive generative capabilities across vision-and-language tasks. However, Diffusion MLLMs exhibit substantially slower inference than autoregressive models: Each denoising step employs full bidirectional self-attention over the entire sequence, resulting in cubic decoding complexity that becomes computationally impractical with thousands of visual tokens. To address this challenge, we propose D$^{3}$ToM, a Decider-guided dynamic token merging method that dynamically merges redundant visual tokens at different denoising steps to accelerate inference in Diffusion MLLMs. At each denoising step, D$^{3}$ToM uses decider tokens-the tokens generated in the previous denoising step-to build an importance map over all visual tokens. Then it maintains a proportion of the most salient tokens and merges the remainder through similarity-based aggregation. This plug-and-play module integrates into a single transformer layer, physically shortening the visual token sequence for all subsequent layers without altering model parameters. Moreover, D$^{3}$ToM employs a merge ratio that dynamically varies with each denoising step, aligns with the native decoding process of Diffusion MLLMs, achieving superior performance under equivalent computational budgets. Extensive experiments show that D$^{3}$ToM accelerates inference while preserving competitive performance. The code is released at https://github.com/bcmi/D3ToM-Diffusion-MLLM.
AugAbEx : Way Forward for Extractive Case Summarization
Venue: arXiv cs.CL 2025
Year: 2025

Summarization of legal judgments poses a heavy cognitive burden on law practitioners due to the complexity of the language, context-sensitive legal jargon, and the length of the document. Therefore, the automatic summarization of legal documents has attracted serious attention from natural language processing researchers. Since the abstractive summaries of legal documents generated by deep neural methods remain prone to the risk of misrepresenting nuanced legal jargon or overlooking key contextual details, we envisage a rising trend toward the use of extractive case summarizers. Given the high cost of human annotation for gold standard extractive summaries, we engineer a light and transparent pipeline that leverages existing abstractive gold standard summaries to create the corresponding extractive gold standard versions. The approach ensures that the experts` opinions ensconced in the original gold standard abstractive summaries are carried over to the transformed extractive summaries. We aim to augment seven existing case summarization datasets, which include abstractive summaries, by incorporating corresponding extractive summaries and create an enriched data resource for case summarization research community. To ensure the quality of the augmented extractive summaries, we perform an extensive comparative evaluation with the original abstractive gold standard summaries covering structural, lexical, and semantic dimensions. We also compare the domain-level information of the two summaries. We commit to release the augmented datasets in the public domain for use by the research community and believe that the resource will offer opportunities to advance the field of automatic summarization of legal documents.
Privacy-Preserving Prompt Injection Detection for LLMs Using Federated Learning and Embedding-Based NLP Classification
Venue: arXiv cs.CR 2025
Year: 2025

Prompt injection attacks are an emerging threat to large language models (LLMs), enabling malicious users to manipulate outputs through carefully designed inputs. Existing detection approaches often require centralizing prompt data, creating significant privacy risks. This paper proposes a privacy-preserving prompt injection detection framework based on federated learning and embedding-based classification. A curated dataset of benign and adversarial prompts was encoded with sentence embedding and used to train both centralized and federated logistic regression models. The federated approach preserved privacy by sharing only model parameters across clients, while achieving detection performance comparable to centralized training. Results demonstrate that effective prompt injection detection is feasible without exposing raw data, making this one of the first explorations of federated security for LLMs. Although the dataset is limited in scale, the findings establish a strong proof-of-concept and highlight new directions for building secure and privacy-aware LLM systems.
LiDAR-GS++:Improving LiDAR Gaussian Reconstruction via Diffusion Priors
Venue: arXiv cs.CV 2025
Year: 2025

Recent GS-based rendering has made significant progress for LiDAR, surpassing Neural Radiance Fields (NeRF) in both quality and speed. However, these methods exhibit artifacts in extrapolated novel view synthesis due to the incomplete reconstruction from single traversal scans. To address this limitation, we present LiDAR-GS++, a LiDAR Gaussian Splatting reconstruction method enhanced by diffusion priors for real-time and high-fidelity re-simulation on public urban roads. Specifically, we introduce a controllable LiDAR generation model conditioned on coarsely extrapolated rendering to produce extra geometry-consistent scans and employ an effective distillation mechanism for expansive reconstruction. By extending reconstruction to under-fitted regions, our approach ensures global geometric consistency for extrapolative novel views while preserving detailed scene surfaces captured by sensors. Experiments on multiple public datasets demonstrate that LiDAR-GS++ achieves state-of-the-art performance for both interpolated and extrapolated viewpoints, surpassing existing GS and NeRF-based methods.
MMSense: Adapting Vision-based Foundation Model for Multi-task Multi-modal Wireless Sensing
Venue: arXiv cs.LG 2025
Year: 2025

Large AI models have been widely adopted in wireless communications for channel modeling, beamforming, and resource optimization. However, most existing efforts remain limited to single-modality inputs and channel-specific objec- tives, overlooking the broader potential of large foundation models for unified wireless sensing. To bridge this gap, we propose MMSense, a multi-modal, multi-task foundation model that jointly addresses channel-centric, environment-aware, and human-centered sensing. Our framework integrates image, radar, LiDAR, and textual data by transforming them into vision- compatible representations, enabling effective cross-modal align- ment within a unified feature space. A modality gating mecha- nism adaptively fuses these representations, while a vision-based large language model backbone enables unified feature align- ment and instruction-driven task adaptation. Furthermore, task- specific sequential attention and uncertainty-based loss weighting mechanisms enhance cross-task generalization. Experiments on real wireless scenario datasets show that our approach outper- forms both task-specific and large-model baselines, confirming its strong generalization across heterogeneous sensing tasks.
Dyadic-Chaotic Lifting S-Boxes for Enhanced Physical-Layer Security within 6G Networks
Venue: arXiv cs.CR 2025
Year: 2025

Sixth-Generation (6G) wireless networks will interconnect billions of resource-constrained devices and time-critical services, where classical, fixed, and heavy cryptography strains latency and energy budgets and struggles against large-scale, pre-computation attacks. Physical-Layer Security (PLS) is therefore pivotal to deliver lightweight, information-theoretic protection, but still requires strong, reconfigurable confusion components that can be diversified per slice, session, or device to blunt large-scale precomputation and side-channel attacks. In order to address the above requirement, we introduce the first-ever chaos-lifted substitution box (S-box) for PLS that couples a $β$-transformation-driven dynamical system with dyadic conditional sampling to generate time-varying, seedable 8-bit permutations on demand. This construction preserves uniformity via ergodicity, yields full 8-bit bijections, and supports on-the-fly diversification across sessions. The resulting S-box attains optimal algebraic degree 7 on every output bit and high average nonlinearity 102.5 (85% of the 8-bit bound), strengthening resistance to algebraic and linear cryptanalysis. Differential and linear profiling report max DDT entry 10 (probability 0.039) and max linear probability 0.648, motivating deployment within a multi-round cipher with a strong diffusion layer, where the security-to-efficiency trade-off is compelling. Our proposed reconfigurable, lightweight S-box directly fulfills key PLS requirements of 6G networks by delivering fast, hardware-amenable confusion components with built-in agility against evolving threats.
SAC-MoE: Reinforcement Learning with Mixture-of-Experts for Control of Hybrid Dynamical Systems with Uncertainty
Venue: arXiv cs.RO 2025
Year: 2025

Hybrid dynamical systems result from the interaction of continuous-variable dynamics with discrete events and encompass various systems such as legged robots, vehicles and aircrafts. Challenges arise when the system's modes are characterized by unobservable (latent) parameters and the events that cause system dynamics to switch between different modes are also unobservable. Model-based control approaches typically do not account for such uncertainty in the hybrid dynamics, while standard model-free RL methods fail to account for abrupt mode switches, leading to poor generalization. To overcome this, we propose SAC-MoE which models the actor of the Soft Actor-Critic (SAC) framework as a Mixture-of-Experts (MoE) with a learned router that adaptively selects among learned experts. To further improve robustness, we develop a curriculum-based training algorithm to prioritize data collection in challenging settings, allowing better generalization to unseen modes and switching locations. Simulation studies in hybrid autonomous racing and legged locomotion tasks show that SAC-MoE outperforms baselines (up to 6x) in zero-shot generalization to unseen environments. Our curriculum strategy consistently improves performance across all evaluated policies. Qualitative analysis shows that the interpretable MoE router activates different experts for distinct latent modes.
Random-Key Metaheuristic and Linearization for the Quadratic Multiple Constraints Variable-Sized Bin Packing Problem
Venue: arXiv cs.NE 2025
Year: 2025

This paper addresses the Quadratic Multiple Constraints Variable-Sized Bin Packing Problem (QMC-VSBPP), a challenging combinatorial optimization problem that generalizes the classical bin packing by incorporating multiple capacity dimensions, heterogeneous bin types, and quadratic interaction costs between items. We propose two complementary methods that advance the current state-of-the-art. First, a linearized mathematical formulation is introduced to eliminate quadratic terms, enabling the use of exact solvers such as Gurobi to compute strong lower bounds - reported here for the first time for this problem. Second, we develop RKO-ACO, a continuous-domain Ant Colony Optimization algorithm within the Random-Key Optimization framework, enhanced with adaptive Q-learning parameter control and efficient local search. Extensive computational experiments on benchmark instances show that the proposed linearized model produces significantly tighter lower bounds than the original quadratic formulation, while RKO-ACO consistently matches or improves upon all best-known solutions in the literature, establishing new upper bounds for large-scale instances. These results provide new reference values for future studies and demonstrate the effectiveness of evolutionary and random-key metaheuristic approaches for solving complex quadratic packing problems. Source code and data available at https://github.com/nataliaalves03/RKO-ACO