OpenRaiser/Intern-Atlas huggingface dataset:
Towards Efficient LUT-based PIM: A Scalable and Low-Power Approach for Modern Workloads
Venue: arXiv cs.AR 2025
Year: 2025

Data movement in memory-intensive workloads, such as deep learning, incurs energy costs that are over three orders of magnitude higher than the cost of computation. Since these workloads involve frequent data transfers between memory and processing units, addressing data movement overheads is crucial for improving performance. Processing-using-memory (PuM) offers an effective solution by enabling in-memory computation, thereby minimizing data transfers. In this paper we propose Lama, a LUT-based PuM architecture designed to efficiently execute SIMD operations by supporting independent column accesses within each mat of a DRAM subarray. Lama exploits DRAM's mat-level parallelism and open-page policy to significantly reduce the number of energy-intensive memory activation (ACT) commands, which are the primary source of overhead in most PuM architectures. Unlike prior PuM solutions, Lama supports up to 8-bit operand precision without decomposing computations, while incurring only a 2.47% area overhead. Our evaluation shows Lama achieves an average performance improvement of 8.5x over state-of-the-art PuM architectures and a 3.8x improvement over CPU, along with energy efficiency gains of 6.9x/8x, respectively, for bulk 8-bit multiplication. We also introduce LamaAccel, an HBM-based PuM accelerator that utilizes Lama to accelerate the inference of attention-based models. LamaAccel employs exponential quantization to optimize product/accumulation in dot-product operations, transforming them into simpler tasks like addition and counting. LamaAccel delivers up to 9.3x/19.2x reduction in energy and 4.8x/9.8x speedup over TPU/GPU, along with up to 5.8x energy reduction and 2.1x speedup over a state-of-the-art PuM baseline.
Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing
Venue: arXiv cs.AI 2025
Year: 2025

Safety alignment is an essential research topic for real-world AI applications. Despite the multifaceted nature of safety and trustworthiness in AI, current safety alignment methods often focus on a comprehensive notion of safety. By carefully assessing models from the existing safety-alignment methods, we found that, while they generally improved overall safety performance, they failed to ensure safety in specific categories. Our study first identified the difficulty of eliminating such vulnerabilities without sacrificing the model's helpfulness. We observed that, while smaller KL penalty parameters, increased training iterations, and dataset cleansing can enhance safety, they do not necessarily improve the trade-off between safety and helpfulness. We discovered that safety alignment could even induce undesired effects and result in a model that prefers generating negative tokens leading to rejective responses, regardless of the input context. To address this, we introduced a learning-free method, Token-level Safety-Debiased Inference (TSDI), to estimate and correct this bias during the generation process using randomly constructed prompts. Our experiments demonstrated that our method could enhance the model's helpfulness while maintaining safety, thus improving the trade-off Pareto-front.
Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach
Venue: arXiv cs.NI 2025
Year: 2025

Mobility performance has been a key focus in cellular networks up to 5G. To enhance handover (HO) performance, 3GPP introduced Conditional Handover (CHO) and Layer 1/Layer 2 Triggered Mobility (LTM) mechanisms in 5G. While these reactive HO strategies address the trade-off between HO failures (HOF) and ping-pong effects, they often result in inefficient radio resource utilization due to additional HO preparations. To overcome these challenges, this article proposes a proactive HO framework for mobility management in O-RAN, leveraging user-cell link predictions to identify the optimal target cell for HO. We explore various categories of Graph Neural Networks (GNNs) for link prediction and analyze the complexity of applying them to the mobility management domain. Two GNN models are compared using a real-world dataset, with experimental results demonstrating their ability to capture the dynamic and graph-structured nature of cellular networks. Finally, we present key insights from our study and outline future steps to enable the integration of GNN-based link prediction for mobility management in O-RAN networks.
EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues
Venue: arXiv cs.MM 2025
Year: 2025

We present EditIQ, a completely automated framework for cinematically editing scenes captured via a stationary, large field-of-view and high-resolution camera. From the static camera feed, EditIQ initially generates multiple virtual feeds, emulating a team of cameramen. These virtual camera shots termed rushes are subsequently assembled using an automated editing algorithm, whose objective is to present the viewer with the most vivid scene content. To understand key scene elements and guide the editing process, we employ a two-pronged approach: (1) a large language model (LLM)-based dialogue understanding module to analyze conversational flow, coupled with (2) visual saliency prediction to identify meaningful scene elements and camera shots therefrom. We then formulate cinematic video editing as an energy minimization problem over shot selection, where cinematic constraints determine shot choices, transitions, and continuity. EditIQ synthesizes an aesthetically and visually compelling representation of the original narrative while maintaining cinematic coherence and a smooth viewing experience. Efficacy of EditIQ against competing baselines is demonstrated via a psychophysical study involving twenty participants on the BBC Old School dataset plus eleven theatre performance videos. Video samples from EditIQ can be found at https://editiq-ave.github.io/.
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization
Venue: arXiv cs.NE 2025
Year: 2025

Quality-Diversity has emerged as a powerful family of evolutionary algorithms that generate diverse populations of high-performing solutions by implementing local competition principles inspired by biological evolution. While these algorithms successfully foster diversity and innovation, their specific mechanisms rely on heuristics, such as grid-based competition in MAP-Elites or nearest-neighbor competition in unstructured archives. In this work, we propose a fundamentally different approach: using meta-learning to automatically discover novel Quality-Diversity algorithms. By parameterizing the competition rules using attention-based neural architectures, we evolve new algorithms that capture complex relationships between individuals in the descriptor space. Our discovered algorithms demonstrate competitive or superior performance compared to established Quality-Diversity baselines while exhibiting strong generalization to higher dimensions, larger populations, and out-of-distribution domains like robot control. Notably, even when optimized solely for fitness, these algorithms naturally maintain diverse populations, suggesting meta-learning rediscovers that diversity is fundamental to effective optimization.
From Uncertain to Safe: Conformal Fine-Tuning of Diffusion Models for Safe PDE Control
Venue: arXiv cs.LG 2025
Year: 2025

The application of deep learning for partial differential equation (PDE)-constrained control is gaining increasing attention. However, existing methods rarely consider safety requirements crucial in real-world applications. To address this limitation, we propose Safe Diffusion Models for PDE Control (SafeDiffCon), which introduce the uncertainty quantile as model uncertainty quantification to achieve optimal control under safety constraints through both post-training and inference phases. Firstly, our approach post-trains a pre-trained diffusion model to generate control sequences that better satisfy safety constraints while achieving improved control objectives via a reweighted diffusion loss, which incorporates the uncertainty quantile estimated using conformal prediction. Secondly, during inference, the diffusion model dynamically adjusts both its generation process and parameters through iterative guidance and fine-tuning, conditioned on control targets while simultaneously integrating the estimated uncertainty quantile. We evaluate SafeDiffCon on three control tasks: 1D Burgers' equation, 2D incompressible fluid, and controlled nuclear fusion problem. Results demonstrate that SafeDiffCon is the only method that satisfies all safety constraints, whereas other classical and deep learning baselines fail. Furthermore, while adhering to safety constraints, SafeDiffCon achieves the best control performance. The code can be found at https://github.com/AI4Science-WestlakeU/safediffcon.
On the Existential Theory of the Reals Enriched with Integer Powers of a Computable Number
Venue: arXiv cs.LO 2025
Year: 2025

This paper investigates $\exists\mathbb{R}(r^{\mathbb{Z}})$, that is the extension of the existential theory of the reals by an additional unary predicate $r^{\mathbb{Z}}$ for the integer powers of a fixed computable real number $r > 0$. If all we have access to is a Turing machine computing $r$, it is not possible to decide whether an input formula from this theory satisfiable. However, we show an algorithm to decide this problem when: 1. $r$ is known to be transcendental, or 2. $r$ is a root of some given integer polynomial (that is, $r$ is algebraic). In other words, knowing the algebraicity of $r$ suffices to circumvent undecidability. Furthermore, we establish complexity results under the proviso that $r$ enjoys what we call a polynomial root barrier. Using this notion, we show that the satisfiability problem of $\exists\mathbb{R}(r^{\mathbb{Z}})$ is 1. in NEXPTIME if $r$ is a natural number, 2. in EXPSPACE if $r$ is an algebraic number, and 3. in 3EXP if $r$ belongs to a family of transcendental numbers including $π$ and Euler's $e$. As a by-product of our results, we are able to remove the appeal to Schanuel's conjecture from the proof of decidability of the entropic risk threshold problem for stochastic games with rational probabilities, rewards and threshold [Baier et al., MFCS'23]: when the base of the entropic risk is Euler's $e$ and the aversion factor is a fixed algebraic number, the problem is in EXP.
A Robust Remote Photoplethysmography Method
Venue: arXiv cs.CV 2025
Year: 2025

Remote photoplethysmography (rPPG) is a method for measuring a subjects heart rate remotely using a camera. Factors such as subject movement, ambient light level, makeup etc. complicate such measurements by distorting the observed pulse. Recent works on this topic have proposed a variety of approaches for accurately measuring heart rate in humans, however these methods were tested in ideal conditions, where the subject does not make significant movements and all measurements are taken at the same level of illumination. In more realistic conditions these methods suffer from decreased accuracy. The study proposes a more robust method that is less susceptible to distortions and has minimal hardware requirements. The proposed method uses a combination of mathematical transforms to calculate the subjects heart rate. It performs best when used with a camera that has been modified by removing its infrared filter, although using an unmodified camera is also possible. The method was tested on 26 videos taken from 19 volunteers of varying gender and age. The obtained results were compared to reference data and the average mean absolute error was found to be at 1.95 beats per minute, which is noticeably better than the results from previous works. The remote photoplethysmography method proposed in the present article is more resistant to distortions than methods from previous publications and thus allows one to remotely and accurately measure the subjects heart rate without imposing any significant limitations on the subjects behavior.
Adversarial ML Problems Are Getting Harder to Solve and to Evaluate
Venue: arXiv cs.LG 2025
Year: 2025

In the past decade, considerable research effort has been devoted to securing machine learning (ML) models that operate in adversarial settings. Yet, progress has been slow even for simple "toy" problems (e.g., robustness to small adversarial perturbations) and is often hindered by non-rigorous evaluations. Today, adversarial ML research has shifted towards studying larger, general-purpose language models. In this position paper, we argue that the situation is now even worse: in the era of LLMs, the field of adversarial ML studies problems that are (1) less clearly defined, (2) harder to solve, and (3) even more challenging to evaluate. As a result, we caution that yet another decade of work on adversarial ML may fail to produce meaningful progress.
Survey of Quantization Techniques for On-Device Vision-based Crack Detection
Venue: arXiv cs.CV 2025
Year: 2025

Structural Health Monitoring (SHM) ensures the safety and longevity of infrastructure by enabling timely damage detection. Vision-based crack detection, combined with UAVs, addresses the limitations of traditional sensor-based SHM methods but requires the deployment of efficient deep learning models on resource-constrained devices. This study evaluates two lightweight convolutional neural network models, MobileNetV1x0.25 and MobileNetV2x0.5, across TensorFlow, PyTorch, and Open Neural Network Exchange platforms using three quantization techniques: dynamic quantization, post-training quantization (PTQ), and quantization-aware training (QAT). Results show that QAT consistently achieves near-floating-point accuracy, such as an F1-score of 0.8376 for MBNV2x0.5 with Torch-QAT, while maintaining efficient resource usage. PTQ significantly reduces memory and energy consumption but suffers from accuracy loss, particularly in TensorFlow. Dynamic quantization preserves accuracy but faces deployment challenges on PyTorch. By leveraging QAT, this work enables real-time, low-power crack detection on UAVs, enhancing safety, scalability, and cost-efficiency in SHM applications, while providing insights into balancing accuracy and efficiency across different platforms for autonomous inspections.
An Anatomy of 488 Faults from Defects4J Based on the Control- and Data-Flow Graph Representations of Programs
Venue: arXiv cs.SE 2025
Year: 2025

Software fault datasets such as Defects4J provide for each individual fault its location and repair, but do not characterize the faults. Current classifications use the repairs as proxies, but these do not capture the intrinsic nature of the fault. In this paper, we propose a new, direct fault classification scheme based on the control- and data-flow graph representations of programs. Our scheme comprises six control-flow and two data-flow fault classes. We manually apply this scheme to 488 faults from seven projects in the Defects4J dataset. We find that the majority of the faults are assigned between one and three classes. We also find that one of the data-flow fault classes (definition fault) is the most common individual class but that the majority of faults are classified with at least one control-flow fault class. Our proposed classification can be applied to other fault datasets and can be used to improve fault localization and automated program repair techniques for specific fault classes.
MAGNNET: Multi-Agent Graph Neural Network-based Efficient Task Allocation for Autonomous Vehicles with Deep Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

This paper addresses the challenge of decentralized task allocation within heterogeneous multi-agent systems operating under communication constraints. We introduce a novel framework that integrates graph neural networks (GNNs) with a centralized training and decentralized execution (CTDE) paradigm, further enhanced by a tailored Proximal Policy Optimization (PPO) algorithm for multi-agent deep reinforcement learning (MARL). Our approach enables unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to dynamically allocate tasks efficiently without necessitating central coordination in a 3D grid environment. The framework minimizes total travel time while simultaneously avoiding conflicts in task assignments. For the cost calculation and routing, we employ reservation-based A* and R* path planners. Experimental results revealed that our method achieves a high 92.5% conflict-free success rate, with only a 7.49% performance gap compared to the centralized Hungarian method, while outperforming the heuristic decentralized baseline based on greedy approach. Additionally, the framework exhibits scalability with up to 20 agents with allocation processing of 2.8 s and robustness in responding to dynamically generated tasks, underscoring its potential for real-world applications in complex multi-agent scenarios.
Extending Asynchronous Byzantine Agreement with Crusader Agreement
Venue: arXiv cs.DC 2025
Year: 2025

In this work, we study multivalued byzantine agreement (BA) in an asynchronous network of $n$ parties where up to $t < \frac{n}{3}$ parties are byzantine. We present a new reduction from multivalued BA to binary BA. It allows one to achieve BA on $\ell$-bit inputs with one instance of binary BA, one instance of crusader agreement (CA) on $\ell$-bit inputs and $Θ(\ell n + n^2)$ bits of additional communication. As our reduction uses multivalued CA, we also design two new information-theoretic CA protocols for $\ell$-bit inputs. In the first one, we use almost-universal hashing to achieve statistical security with probability $1 - 2^{-λ}$ against $t < \frac{n}{3}$ faults with $Θ(\ell n + n^2(λ+ \log n))$ bits of communication. Following this, we replace the hashes with error correcting code symbols and add a preliminary step based on the synchronous multivalued BA protocol COOL [DISC '21] to obtain a second, perfectly secure CA protocol that can for any $\varepsilon > 0$ be set to tolerate $t \leq \frac{n}{3 + \varepsilon}$ faults with $\mathcal{O}\bigl(\frac{\ell n}{\min(1, \varepsilon^2)} + n^2\max\bigl(1, \log \frac{1}{\varepsilon}\bigr) \bigr)$ bits of communication. Our CA protocols allow one to extend binary BA to multivalued BA with a constant round overhead, a quadratic-in-$n$ communication overhead, and information-theoretic security.
Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

In offline reinforcement learning-based recommender systems (RLRS), learning effective state representations is crucial for capturing user preferences that directly impact long-term rewards. However, raw state representations often contain high-dimensional, noisy information and components that are not causally relevant to the reward. Additionally, missing transitions in offline data make it challenging to accurately identify features that are most relevant to user satisfaction. To address these challenges, we propose Policy-Guided Causal Representation (PGCR), a novel two-stage framework for causal feature selection and state representation learning in offline RLRS. In the first stage, we learn a causal feature selection policy that generates modified states by isolating and retaining only the causally relevant components (CRCs) while altering irrelevant components. This policy is guided by a reward function based on the Wasserstein distance, which measures the causal effect of state components on the reward and encourages the preservation of CRCs that directly influence user interests. In the second stage, we train an encoder to learn compact state representations by minimizing the mean squared error (MSE) loss between the latent representations of the original and modified states, ensuring that the representations focus on CRCs. We provide a theoretical analysis proving the identifiability of causal effects from interventions, validating the ability of PGCR to isolate critical state components for decision-making. Extensive experiments demonstrate that PGCR significantly improves recommendation performance, confirming its effectiveness for offline RL-based recommender systems.
Rule-ATT&CK Mapper (RAM): Mapping SIEM Rules to TTPs Using LLMs
Venue: arXiv cs.CR 2025
Year: 2025

The growing frequency of cyberattacks has heightened the demand for accurate and efficient threat detection systems. SIEM platforms are important for analyzing log data and detecting adversarial activities through rule-based queries, also known as SIEM rules. The efficiency of the threat analysis process relies heavily on mapping these SIEM rules to the relevant attack techniques in the MITRE ATT&CK framework. Inaccurate annotation of SIEM rules can result in the misinterpretation of attacks, increasing the likelihood that threats will be overlooked. Existing solutions for annotating SIEM rules with MITRE ATT&CK technique labels have notable limitations: manual annotation of SIEM rules is both time-consuming and prone to errors, and ML-based approaches mainly focus on annotating unstructured free text sources rather than structured data like SIEM rules. Structured data often contains limited information, further complicating the annotation process and making it a challenging task. To address these challenges, we propose Rule-ATT&CK Mapper (RAM), a novel framework that leverages LLMs to automate the mapping of structured SIEM rules to MITRE ATT&CK techniques. RAM's multi-stage pipeline, which was inspired by the prompt chaining technique, enhances mapping accuracy without requiring LLM pre-training or fine-tuning. Using the Splunk Security Content dataset, we evaluate RAM's performance using several LLMs, including GPT-4-Turbo, Qwen, IBM Granite, and Mistral. Our evaluation highlights GPT-4-Turbo's superior performance, which derives from its enriched knowledge base, and an ablation study emphasizes the importance of external contextual knowledge in overcoming the limitations of LLMs' implicit knowledge for domain-specific tasks. These findings demonstrate RAM's potential in automating cybersecurity workflows and provide valuable insights for future advancements in this field.
Exploring the Feasibility of AI-Assisted Spine MRI Protocol Optimization Using DICOM Image Metadata
Venue: arXiv cs.LG 2025
Year: 2025

Artificial intelligence (AI) is increasingly being utilized to optimize magnetic resonance imaging (MRI) protocols. Given that image details are critical for diagnostic accuracy, optimizing MRI acquisition protocols is essential for enhancing image quality. While medical physicists are responsible for this optimization, the variability in equipment usage and the wide range of MRI protocols in clinical settings pose significant challenges. This study aims to validate the application of AI in optimizing MRI protocols using dynamic data from clinical practice, specifically DICOM metadata. To achieve this, four MRI spine exam databases were created, with the target attribute being the binary classification of image quality (good or bad). Five AI models were trained to identify trends in acquisition parameters that influence image quality, grounded in MRI theory. These trends were analyzed using SHAP graphs. The models achieved F1 performance ranging from 77% to 93% for datasets containing 292 or more instances, with the observed trends aligning with MRI theory. The models effectively reflected the practical realities of clinical MRI settings, offering a valuable tool for medical physicists in quality control tasks. In conclusion, AI has demonstrated its potential to optimize MRI protocols, supporting medical physicists in improving image quality and enhancing the efficiency of quality control in clinical practice.