OpenRaiser/Intern-Atlas huggingface dataset:
Controlled Yet Natural: A Hybrid BDI-LLM Conversational Agent for Child Helpline Training
Venue: arXiv cs.HC 2025
Year: 2025

Child helpline training often relies on human-led roleplay, which is both time- and resource-consuming. To address this, rule-based interactive agent simulations have been proposed to provide a structured training experience for new counsellors. However, these agents might suffer from limited language understanding and response variety. To overcome these limitations, we present a hybrid interactive agent that integrates Large Language Models (LLMs) into a rule-based Belief-Desire-Intention (BDI) framework, simulating more realistic virtual child chat conversations. This hybrid solution incorporates LLMs into three components: intent recognition, response generation, and a bypass mechanism. We evaluated the system through two studies: a script-based assessment comparing LLM-generated responses to human-crafted responses, and a within-subject experiment (N=37) comparing the LLM-integrated agent with a rule-based version. The first study provided evidence that the three LLM components were non-inferior to human-crafted responses. In the second study, we found credible support for two hypotheses: participants perceived the LLM-integrated agent as more believable and reported more positive attitudes toward it than the rule-based agent. Additionally, although weaker, there was some support for increased engagement (posterior probability = 0.845, 95% HDI [-0.149, 0.465]). Our findings demonstrate the potential of integrating LLMs into rule-based systems, offering a promising direction for more flexible but controlled training systems.
Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies
Venue: arXiv cs.CL 2025
Year: 2025

Aspect-based sentiment analysis (ABSA) in natural language processing enables organizations to understand customer opinions on specific product aspects. While deep learning models are widely used for English ABSA, their application in Arabic is limited due to the scarcity of labeled data. Researchers have attempted to tackle this issue by using pre-trained contextualized language models such as BERT. However, these models are often based on fact-based data, which can introduce bias in domain-specific tasks like ABSA. To our knowledge, no studies have applied adaptive pre-training with Arabic contextualized models for ABSA. This research proposes a novel approach using domain-adaptive pre-training for aspect-sentiment classification (ASC) and opinion target expression (OTE) extraction. We examine fine-tuning strategies - feature extraction, full fine-tuning, and adapter-based methods - to enhance performance and efficiency, utilizing multiple adaptation corpora and contextualized models. Our results show that in-domain adaptive pre-training yields modest improvements. Adapter-based fine-tuning is a computationally efficient method that achieves competitive results. However, error analyses reveal issues with model predictions and dataset labeling. In ASC, common problems include incorrect sentiment labeling, misinterpretation of contrastive markers, positivity bias for early terms, and challenges with conflicting opinions and subword tokenization. For OTE, issues involve mislabeling targets, confusion over syntactic roles, difficulty with multi-word expressions, and reliance on shallow heuristics. These findings underscore the need for syntax- and semantics-aware models, such as graph convolutional networks, to more effectively capture long-distance relations and complex aspect-based opinion alignments.
SMART-3D: Three-Dimensional Self-Morphing Adaptive Replanning Tree
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents SMART-3D, an extension of the SMART algorithm to 3D environments. SMART-3D is a tree-based adaptive replanning algorithm for dynamic environments with fast moving obstacles. SMART-3D morphs the underlying tree to find a new path in real-time whenever the current path is blocked by obstacles. SMART-3D removed the grid decomposition requirement of the SMART algorithm by replacing the concept of hot-spots with that of hot-nodes, thus making it computationally efficient and scalable to 3D environments. The hot-nodes are nodes which allow for efficient reconnections to morph the existing tree to find a new safe and reliable path. The performance of SMART-3D is evaluated by extensive simulations in 2D and 3D environments populated with randomly moving dynamic obstacles. The results show that SMART-3D achieves high success rates and low replanning times, thus highlighting its suitability for real-time onboard applications.
Development of a Mobile Application for at-Home Analysis of Retinal Fundus Images
Venue: arXiv cs.HC 2025
Year: 2025

Machine learning is gaining significant attention as a diagnostic tool in medical imaging, particularly in the analysis of retinal fundus images. However, this approach is not yet clinically applicable, as it still depends on human validation from a professional. Therefore, we present the design for a mobile application that monitors metrics related to retinal fundus images correlating to age-related conditions. The purpose of this platform is to observe for a change in these metrics over time, offering early insights into potential ocular diseases without explicitly delivering diagnostics. Metrics analysed include vessel tortuosity, as well as signs of glaucoma, retinopathy and macular edema. To evaluate retinopathy grade and risk of macular edema, a model was trained on the Messidor dataset and compared to a similar model trained on the MAPLES-DR dataset. Information from the DeepSeeNet glaucoma detection model, as well as tortuosity calculations, is additionally incorporated to ultimately present a retinal fundus image monitoring platform. As a result, the mobile application permits monitoring of trends or changes in ocular metrics correlated to age-related conditions with regularly uploaded photographs.
Implementation of the Collision Avoidance System for DO-178C Compliance
Venue: arXiv cs.SE 2025
Year: 2025

This technical report presents the detailed implementation of a Collision Avoidance System (CAS) for Unmanned Aerial Vehicles (UAVs), developed as a case study to demonstrate a rigorous methodology for achieving DO-178C compliance in safety-critical software. The CAS is based on functional requirements inspired by NASA's Access 5 project and is designed to autonomously detect, evaluate, and avoid potential collision threats in real-time, supporting the safe integration of UAVs into civil airspace. The implementation environment combines formal methods, model-based development, and automated verification tools, including Alloy, SPIN, Simulink Embedded Coder, and the LDRA tool suite. The report documents each phase of the software lifecycle: requirements specification and validation, architectural and detailed design, coding, verification, and traceability, with a strong focus on compliance with DO-178C Design Assurance Level B objectives. Results demonstrate that formal modelling and automated toolchains enabled early detection and correction of specification defects, robust traceability, and strong evidence of verification and validation across all development stages. Static and dynamic analyses confirmed code quality and coverage, while formal verification methods provided mathematical assurance of correctness for critical components. Although the integration phase was not fully implemented, the approach proved effective in addressing certification challenges for UAV safety-critical systems. \keywords Collision Avoidance System (CAS), Unmanned Aerial Vehicles (UAVs), DO-178C compliance, Safety-critical software, Formal methods, Model-based development, Alloy, SPIN model checker, Simulink Embedded Coder, LDRA tool suite, Software verification and validation, Traceability, Certification.
ShadowServe: Interference-Free KV Cache Fetching for Distributed Prefix Caching
Venue: arXiv cs.DC 2025
Year: 2025

Distributed prefix caching accelerates long-context LLM serving by reusing KV cache entries for common context prefixes. However, KV cache fetches can become a bottleneck when network bandwidth is limited. Compression mitigates the bandwidth issue, but can degrade overall performance when decompression interferes with model computation. We present ShadowServe, the first SmartNIC-accelerated, interference-free prefix caching system for LLM serving. ShadowServe separates a control plane on the host and a data plane fully offloaded to the SmartNIC, which eliminates interference to both host GPU and CPU. To overcome the SmartNIC's limited compute and memory resources, we design a chunked pipeline that parallelizes data plane operations across the SmartNIC's compute resources, and a minimal-copy memory management scheme that reduces memory pressure on the SmartNIC. Compared to state-of-the-art solutions, ShadowServe achieves up to 2.2x lower loaded time-per-output-token (TPOT), and reduces time-to-first-token (TTFT) by up to 1.38x in low-bandwidth scenarios (<= 20 Gbps), translating to up to 1.35x higher throughput.
MobileUPReg: Identifying User-Perceived Performance Regressions in Mobile OS Versions
Venue: arXiv cs.SE 2025
Year: 2025

Mobile operating systems (OS) are frequently updated, but such updates can unintentionally degrade user experience by introducing performance regressions. Existing detection techniques often rely on system-level metrics (e.g., CPU or memory usage) or focus on specific OS components, which may miss regressions actually perceived by users -- such as slower responses or UI stutters. To address this gap, we present MobileUPReg, a black-box framework for detecting user-perceived performance regressions across OS versions. MobileUPReg runs the same apps under different OS versions and compares user-perceived performance metrics -- response time, finish time, launch time, and dropped frames -- to identify regressions that are truly perceptible to users. In a large-scale study, MobileUPReg achieves high accuracy in extracting user-perceived metrics and detects user-perceived regressions with 0.96 precision, 0.91 recall, and 0.93 F1-score -- significantly outperforming a statistical baseline using the Wilcoxon rank-sum test and Cliff's Delta. MobileUPReg has been deployed in an industrial CI pipeline, where it analyzes thousands of screencasts across hundreds of apps daily and has uncovered regressions missed by traditional tools. These results demonstrate that MobileUPReg enables accurate, scalable, and perceptually aligned regression detection for mobile OS validation.
PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction
Venue: arXiv cs.GR 2025
Year: 2025

Low Dynamic Range (LDR) to High Dynamic Range (HDR) image translation is a fundamental task in many computational vision problems. Numerous data-driven methods have been proposed to address this problem; however, they lack explicit modeling of illumination, lighting, and scene geometry in images. This limits the quality of the reconstructed HDR images. Since lighting and shadows interact differently with different materials, (e.g., specular surfaces such as glass and metal, and lambertian or diffuse surfaces such as wood and stone), modeling material-specific properties (e.g., specular and diffuse reflectance) has the potential to improve the quality of HDR image reconstruction. This paper presents PhysHDR, a simple yet powerful latent diffusion-based generative model for HDR image reconstruction. The denoising process is conditioned on lighting and depth information and guided by a novel loss to incorporate material properties of surfaces in the scene. The experimental results establish the efficacy of PhysHDR in comparison to a number of recent state-of-the-art methods.
PRISM: Precision-Recall Informed Data-Free Knowledge Distillation via Generative Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access to the real in-distribution (ID) data. While existing methods perform well on small-scale images, they suffer from mode collapse when synthesizing large-scale images, resulting in limited knowledge transfer. Recently, leveraging advanced generative models to synthesize photorealistic images has emerged as a promising alternative. Nevertheless, directly using off-the-shelf diffusion to generate datasets faces the precision-recall challenges: 1) ensuring synthetic data aligns with the real distribution, and 2) ensuring coverage of the real ID manifold. In response, we propose PRISM, a precision-recall informed synthesis method. Specifically, we introduce Energy-guided Distribution Alignment to avoid the generation of out-of-distribution samples, and design the Diversified Prompt Engineering to enhance coverage of the real ID manifold. Extensive experiments on various large-scale image datasets demonstrate the superiority of PRISM. Moreover, we demonstrate that models trained with PRISM exhibit strong domain generalization.
Further results on bent partitions
Venue: arXiv cs.IT 2025
Year: 2025

Bent partitions of $V_{n}^{(p)}$ play an important role in constructing (vectorial) bent functions, partial difference sets, and association schemes, where $V_{n}^{(p)}$ denotes an $n$-dimensional vector space over the finite field $\mathbb{F}_{p}$, $n$ is an even positive integer, and $p$ is a prime. For bent partitions, there remains a challenging open problem: Whether the depth of any bent partition of $V_{n}^{(p)}$ is always a power of $p$. Notably, the depths of all current known bent partitions of $V_{n}^{(p)}$ are powers of $p$. In this paper, we prove that for a bent partition $Γ$ of $V_{n}^{(p)}$ for which all the $p$-ary bent functions generated by $Γ$ are regular or all are weakly regular but not regular, the depth of $Γ$ must be a power of $p$. We present new constructions of bent partitions that (do not) correspond to vectorial dual-bent functions. In particular, a new construction of vectorial dual-bent functions is provided. Additionally, for general bent partitions of $V_{n}^{(2)}$, we establish a characterization in terms of Hadamard matrices.
Cross-Attention with Confidence Weighting for Multi-Channel Audio Alignment
Venue: arXiv cs.SD 2025
Year: 2025

Multi-channel audio alignment is a key requirement in bioacoustic monitoring, spatial audio systems, and acoustic localization. However, existing methods often struggle to address nonlinear clock drift and lack mechanisms for quantifying uncertainty. Traditional methods like Cross-correlation and Dynamic Time Warping assume simple drift patterns and provide no reliability measures. Meanwhile, recent deep learning models typically treat alignment as a binary classification task, overlooking inter-channel dependencies and uncertainty estimation. We introduce a method that combines cross-attention mechanisms with confidence-weighted scoring to improve multi-channel audio synchronization. We extend BEATs encoders with cross-attention layers to model temporal relationships between channels. We also develop a confidence-weighted scoring function that uses the full prediction distribution instead of binary thresholding. Our method achieved first place in the BioDCASE 2025 Task 1 challenge with 0.30 MSE average across test datasets, compared to 0.58 for the deep learning baseline. On individual datasets, we achieved 0.14 MSE on ARU data (77% reduction) and 0.45 MSE on zebra finch data (18% reduction). The framework supports probabilistic temporal alignment, moving beyond point estimates. While validated in a bioacoustic context, the approach is applicable to a broader range of multi-channel audio tasks where alignment confidence is critical. Code available on: https://github.com/Ragib-Amin-Nihal/BEATsCA
Geometric Interpolation of Rigid Body Motions
Venue: arXiv cs.RO 2025
Year: 2025

The problem of interpolating a rigid body motion is to find a spatial trajectory between a prescribed initial and terminal pose. Two variants of this interpolation problem are addressed. The first is to find a solution that satisfies initial conditions on the k-1 derivatives of the rigid body twist. This is called the kth-order initial value trajectory interpolation problem (k-IV-TIP). The second is to find a solution that satisfies conditions on the rigid body twist and its k-1 derivatives at the initial and terminal pose. This is called the kth-order boundary value trajectory interpolation problem (k-BV-TIP). Solutions to the k-IV-TIP for k=1,...,4, i.e. the initial twist and up to the 4th time derivative are prescribed. Further, a solution to the 1-IV-TBP is presented, i.e. the initial and terminal twist are prescribed. The latter is a novel cubic interpolation between two spatial configurations with given initial and terminal twist. This interpolation is automatically identical to the minimum acceleration curve when the twists are set to zero. The general approach to derive higher-order solutions is presented. Numerical results are shown for two examples.
Interpretable Audio Editing Evaluation via Chain-of-Thought Difference-Commonality Reasoning with Multimodal LLMs
Venue: arXiv cs.SD 2025
Year: 2025

Automatic mean opinion score (MOS) prediction provides a more perceptual alternative to objective metrics, offering deeper insights into the evaluated models. With the rapid progress of multimodal large language models (MLLMs), their enhanced perceptual and reasoning abilities enable more comprehensive and interpretable audio quality assessment. In this work, we tackle the challenging task of audio editing evaluation and propose the first natural language-based automated evaluation framework built on MLLMs. Our approach introduces two fine-tuning tasks to boost multi-audio understanding, combined with Chain-of-Thought prompting, and lightweight instruction tuning, to enhance step-by-step reasoning. Experiment demonstrate that our framework delivers accurate, interpretable, and text-based editing evaluation, closely aligning with human judgments and objective metrics while substantially improving over baselines. The code and demo are available at https://github.com/NKU-HLT/Eval_Reasoning.
Advancing Speech Understanding in Speech-Aware Language Models with GRPO
Venue: arXiv cs.CL 2025
Year: 2025

In this paper, we introduce a Group Relative Policy Optimization (GRPO)-based method for training Speech-Aware Large Language Models (SALLMs) on open-format speech understanding tasks, such as Spoken Question Answering and Automatic Speech Translation. SALLMs have proven highly effective for speech understanding tasks. GRPO has recently gained traction for its efficiency in training LLMs, and prior work has explored its application to SALLMs, primarily in multiple-choice tasks. Building on this, we focus on open-format tasks that better reflect the generative abilities of the models. Our approach leverages GRPO with BLEU as the reward signal to optimize SALLMs, and we demonstrate empirically that it surpasses standard SFT across several key metrics. Finally, we explore the potential of incorporating off-policy samples within GRPO for these tasks, highlighting avenues for further improvement and further research.
VAInpaint: Zero-Shot Video-Audio inpainting framework with LLMs-driven Module
Venue: arXiv cs.MM 2025
Year: 2025

Video and audio inpainting for mixed audio-visual content has become a crucial task in multimedia editing recently. However, precisely removing an object and its corresponding audio from a video without affecting the rest of the scene remains a significant challenge. To address this, we propose VAInpaint, a novel pipeline that first utilizes a segmentation model to generate masks and guide a video inpainting model in removing objects. At the same time, an LLM then analyzes the scene globally, while a region-specific model provides localized descriptions. Both the overall and regional descriptions will be inputted into an LLM, which will refine the content and turn it into text queries for our text-driven audio separation model. Our audio separation model is fine-tuned on a customized dataset comprising segmented MUSIC instrument images and VGGSound backgrounds to enhance its generalization performance. Experiments show that our method achieves performance comparable to current benchmarks in both audio and video inpainting.
On the Limits of Tabular Hardness Metrics for Deep RL: A Study with the Pharos Benchmark
Venue: arXiv cs.LG 2025
Year: 2025

Principled evaluation is critical for progress in deep reinforcement learning (RL), yet it lags behind the theory-driven benchmarks of tabular RL. While tabular settings benefit from well-understood hardness measures like MDP diameter and suboptimality gaps, deep RL benchmarks are often chosen based on intuition and popularity. This raises a critical question: can tabular hardness metrics be adapted to guide non-tabular benchmarking? We investigate this question and reveal a fundamental gap. Our primary contribution is demonstrating that the difficulty of non-tabular environments is dominated by a factor that tabular metrics ignore: representation hardness. The same underlying MDP can pose vastly different challenges depending on whether the agent receives state vectors or pixel-based observations. To enable this analysis, we introduce \texttt{pharos}, a new open-source library for principled RL benchmarking that allows for systematic control over both environment structure and agent representations. Our extensive case study using \texttt{pharos} shows that while tabular metrics offer some insight, they are poor predictors of deep RL agent performance on their own. This work highlights the urgent need for new, representation-aware hardness measures and positions \texttt{pharos} as a key tool for developing them.
Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.