OpenRaiser/Intern-Atlas huggingface dataset:
Cloning a Conversational Voice AI Agent from Call\,Recording Datasets for Telesales
Venue: arXiv cs.AI 2025
Year: 2025

Recent advances in language and speech modelling have made it possible to build autonomous voice assistants that understand and generate human dialogue in real time. These systems are increasingly being deployed in domains such as customer service and healthcare care, where they can automate repetitive tasks, reduce operational costs, and provide constant support around the clock. In this paper, we present a general methodology for cloning a conversational voice AI agent from a corpus of call recordings. Although the case study described in this paper uses telesales data to illustrate the approach, the underlying process generalizes to any domain where call transcripts are available. Our system listens to customers over the telephone, responds with a synthetic voice, and follows a structured playbook learned from top performing human agents. We describe the domain selection, knowledge extraction, and prompt engineering used to construct the agent, integrating automatic speech recognition, a large language model based dialogue manager, and text to speech synthesis into a streaming inference pipeline. The cloned agent is evaluated against human agents on a rubric of 22 criteria covering introduction, product communication, sales drive, objection handling, and closing. Blind tests show that the AI agent approaches human performance in routine aspects of the call while underperforming in persuasion and objection handling. We analyze these shortcomings and refine the prompt accordingly. The paper concludes with design lessons and avenues for future research, including large scale simulation and automated evaluation.
Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning
Venue: arXiv cs.CV 2025
Year: 2025

Cryoablation is a minimally invasive localised treatment for prostate cancer that destroys malignant tissue during de-freezing, while sparing surrounding healthy structures. Its success depends on accurate preoperative planning of cryoprobe placements to fully cover the tumour and avoid critical anatomy. This planning is currently manual, expertise-dependent, and time-consuming, leading to variability in treatment quality and limited scalability. In this work, we introduce Cryo-RL, a reinforcement learning framework that models cryoablation planning as a Markov decision process and learns an optimal policy for cryoprobe placement. Within a simulated environment that models clinical constraints and stochastic intraoperative variability, an agent sequentially selects cryoprobe positions and ice sphere diameters. Guided by a reward function based on tumour coverage, this agent learns a cryoablation strategy that leads to optimal cryoprobe placements without the need for any manually-designed plans. Evaluated on 583 retrospective prostate cancer cases, Cryo-RL achieved over 8 percentage-point Dice improvements compared with the best automated baselines, based on geometric optimisation, and matched human expert performance while requiring substantially less planning time. These results highlight the potential of reinforcement learning to deliver clinically viable, reproducible, and efficient cryoablation plans.
On the Normalization of Confusion Matrices: Methods and Geometric Interpretations
Venue: arXiv cs.LG 2025
Year: 2025

The confusion matrix is a standard tool for evaluating classifiers by providing insights into class-level errors. In heterogeneous settings, its values are shaped by two main factors: class similarity -- how easily the model confuses two classes -- and distribution bias, arising from skewed distributions in the training and test sets. However, confusion matrix values reflect a mix of both factors, making it difficult to disentangle their individual contributions. To address this, we introduce bistochastic normalization using Iterative Proportional Fitting, a generalization of row and column normalization. Unlike standard normalizations, this method recovers the underlying structure of class similarity. By disentangling error sources, it enables more accurate diagnosis of model behavior and supports more targeted improvements. We also show a correspondence between confusion matrix normalizations and the model's internal class representations. Both standard and bistochastic normalizations can be interpreted geometrically in this space, offering a deeper understanding of what normalization reveals about a classifier.
Classification of kinetic-related injury in hospital triage data using NLP
Venue: arXiv cs.CL 2025
Year: 2025

Triage notes, created at the start of a patient's hospital visit, contain a wealth of information that can help medical staff and researchers understand Emergency Department patient epidemiology and the degree of time-dependent illness or injury. Unfortunately, applying modern Natural Language Processing and Machine Learning techniques to analyse triage data faces some challenges: Firstly, hospital data contains highly sensitive information that is subject to privacy regulation thus need to be analysed on site; Secondly, most hospitals and medical facilities lack the necessary hardware to fine-tune a Large Language Model (LLM), much less training one from scratch; Lastly, to identify the records of interest, expert inputs are needed to manually label the datasets, which can be time-consuming and costly. We present in this paper a pipeline that enables the classification of triage data using LLM and limited compute resources. We first fine-tuned a pre-trained LLM with a classifier using a small (2k) open sourced dataset on a GPU; and then further fine-tuned the model with a hospital specific dataset of 1000 samples on a CPU. We demonstrated that by carefully curating the datasets and leveraging existing models and open sourced data, we can successfully classify triage data with limited compute resources.
Directed Evolution of Proteins via Bayesian Optimization in Embedding Space
Venue: arXiv cs.LG 2025
Year: 2025

Directed evolution is an iterative laboratory process of designing proteins with improved function by iteratively synthesizing new protein variants and evaluating their desired property with expensive and time-consuming biochemical screening. Machine learning methods can help select informative or promising variants for screening to increase their quality and reduce the amount of necessary screening. In this paper, we present a novel method for machine-learning-assisted directed evolution of proteins which combines Bayesian optimization with informative representation of protein variants extracted from a pre-trained protein language model. We demonstrate that the new representation based on the sequence embeddings significantly improves the performance of Bayesian optimization yielding better results with the same number of conducted screening in total. At the same time, our method outperforms the state-of-the-art machine-learning-assisted directed evolution methods with regression objective.
Depth-Aware Initialization for Stable and Efficient Neural Network Training
Venue: arXiv cs.LG 2025
Year: 2025

In past few years, various initialization schemes have been proposed. These schemes are glorot initialization, He initialization, initialization using orthogonal matrix, random walk method for initialization. Some of these methods stress on keeping unit variance of activation and gradient propagation through the network layer. Few of these methods are independent of the depth information while some methods has considered the total network depth for better initialization. In this paper, comprehensive study has been done where depth information of each layer as well as total network is incorporated for better initialization scheme. It has also been studied that for deeper networks theoretical assumption of unit variance throughout the network does not perform well. It requires the need to increase the variance of the network from first layer activation to last layer activation. We proposed a novel way to increase the variance of the network in flexible manner, which incorporates the information of each layer depth. Experiments shows that proposed method performs better than the existing initialization scheme.
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Venue: arXiv cs.CV 2025
Year: 2025

Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during production, leveraging them usually requires pixel-level annotations, limiting scalability. To address this, we propose ADClick, an Interactive Image Segmentation (IIS) algorithm for industrial anomaly detection. ADClick generates pixel-wise anomaly annotations from only a few user clicks and a brief textual description, enabling precise and efficient labeling that significantly improves AD model performance (e.g., AP = 96.1\% on MVTec AD). We further introduce ADClick-Seg, a cross-modal framework that aligns visual features and textual prompts via a prototype-based approach for anomaly detection and localization. By combining pixel-level priors with language-guided cues, ADClick-Seg achieves state-of-the-art results on the challenging ``Multi-class'' AD task (AP = 80.0\%, PRO = 97.5\%, Pixel-AUROC = 99.1\% on MVTec AD).
GenAI-based test case generation and execution in SDV platform
Venue: arXiv cs.SE 2025
Year: 2025

This paper introduces a GenAI-driven approach for automated test case generation, leveraging Large Language Models and Vision-Language Models to translate natural language requirements and system diagrams into structured Gherkin test cases. The methodology integrates Vehicle Signal Specification modeling to standardize vehicle signal definitions, improve compatibility across automotive subsystems, and streamline integration with third-party testing tools. Generated test cases are executed within the digital.auto playground, an open and vendor-neutral environment designed to facilitate rapid validation of software-defined vehicle functionalities. We evaluate our approach using the Child Presence Detection System use case, demonstrating substantial reductions in manual test specification effort and rapid execution of generated tests. Despite significant automation, the generation of test cases and test scripts still requires manual intervention due to current limitations in the GenAI pipeline and constraints of the digital.auto platform.
A Scalable Attention-Based Approach for Image-to-3D Texture Mapping
Venue: arXiv cs.CV 2025
Year: 2025

High-quality textures are critical for realistic 3D content creation, yet existing generative methods are slow, rely on UV maps, and often fail to remain faithful to a reference image. To address these challenges, we propose a transformer-based framework that predicts a 3D texture field directly from a single image and a mesh, eliminating the need for UV mapping and differentiable rendering, and enabling faster texture generation. Our method integrates a triplane representation with depth-based backprojection losses, enabling efficient training and faster inference. Once trained, it generates high-fidelity textures in a single forward pass, requiring only 0.2s per shape. Extensive qualitative, quantitative, and user preference evaluations demonstrate that our method outperforms state-of-the-art baselines on single-image texture reconstruction in terms of both fidelity to the input image and perceptual quality, highlighting its practicality for scalable, high-quality, and controllable 3D content creation.
Transition of car-based human-mobility in the pandemic era: Data insight from a cross-border region in Europe
Venue: arXiv cs.HC 2025
Year: 2025

Many transport authorities are collecting and publishing almost real-time road traffic data to meet the growing trend of massive open data, a vital resource for foresight decision support systems considering deep data insights. We explored the spatio-temporal transitions in the cross-country road traffic volumes in the context of modelling behavioural transitions in car-based human mobility. This study reports on individual car-based daily travel behaviour detected, before (2018) and during the COVID pandemic (2020), between Germany and neighbouring countries. In the case of Luxembourg, the Bridges and Roads Authority has installed a large digital traffic observatory infrastructure through the adoption of sensor-based IoT technologies, like other European member states. Since 2016, they have provided high-performance data processing and published open data on the country's road traffic. The dataset contains an hourly traffic count for different vehicle types, daily for representative observation points, followed by a major road network. The original dataset contains significant missing entries, so comprehensive data harmonization was performed. We observed the decrease in traffic volumes during pandemic factors (e.g. lockdowns and remote work) period by following global trend of reduced personal mobility. The understanding the dynamic adaptive travel behaviours provide a potential opportunity to generate the actionable insight including temporal and spatial implications. This study demonstrates that the national open traffic data products can have adoption potential to address cross-border insights. In relevance to the net-zero carbon transition, further study should shed light on the interpolation and downscaling approaches at the comprehensive road-network level for identifying pollution hot spots, causal link to functional landuse patterns and calculation of spatial influence area.
Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet
Venue: arXiv cs.CV 2025
Year: 2025

The classification of 3D point clouds is crucial for applications such as autonomous driving, robotics, and augmented reality. However, the commonly used ModelNet40 dataset suffers from limitations such as inconsistent labeling, 2D data, size mismatches, and inadequate class differentiation, which hinder model performance. This paper introduces ModelNet-R, a meticulously refined version of ModelNet40 designed to address these issues and serve as a more reliable benchmark. Additionally, this paper proposes Point-SkipNet, a lightweight graph-based neural network that leverages efficient sampling, neighborhood grouping, and skip connections to achieve high classification accuracy with reduced computational overhead. Extensive experiments demonstrate that models trained in ModelNet-R exhibit significant performance improvements. Notably, Point-SkipNet achieves state-of-the-art accuracy on ModelNet-R with a substantially lower parameter count compared to contemporary models. This research highlights the crucial role of dataset quality in optimizing model efficiency for 3D point cloud classification. For more details, see the code at: https://github.com/m-saeid/ModeNetR_PointSkipNet.
List Decoding Expander-Based Codes via Fast Approximation of Expanding CSPs: I
Venue: arXiv cs.DS 2025
Year: 2025

We present near-linear time list decoding algorithms (in the block-length $n$) for expander-based code constructions. More precisely, we show that (i) For every $δ\in (0,1)$ and $ε> 0$, there is an explicit family of good Tanner LDPC codes of (design) distance $δ$ that is $(δ- ε, O_\varepsilon(1))$ list decodable in time $\widetilde{\mathcal{O}}_{\varepsilon}(n)$ with alphabet size $O_δ(1)$, (ii) For every $R \in (0,1)$ and $ε> 0$, there is an explicit family of AEL codes of rate $R$, distance $1-R -\varepsilon$ that is $(1-R-ε, O_\varepsilon(1))$ list decodable in time $\widetilde{\mathcal{O}}_{\varepsilon}(n)$ with alphabet size $\text{exp}(\text{poly}(1/ε))$, and (iii) For every $R \in (0,1)$ and $ε> 0$, there is an explicit family of AEL codes of rate $R$, distance $1-R-\varepsilon$ that is $(1-R-ε, O(1/ε))$ list decodable in time $\widetilde{\mathcal{O}}_{\varepsilon}(n)$ with alphabet size $\text{exp}(\text{exp}(\text{poly}(1/ε)))$ using recent near-optimal list size bounds from [JMST25]. Our results are obtained by phrasing the decoding task as an agreement CSP [RWZ20,DHKNT19] on expander graphs and using the fast approximation algorithm for $q$-ary expanding CSPs from [Jer23], which is based on weak regularity decomposition [JST21,FK96]. Similarly to list decoding $q$-ary Ta-Shma's codes in [Jer23], we show that it suffices to enumerate over assignments that are constant in each part (of the constantly many) of the decomposition in order to recover all codewords in the list.
Algorithmic Information Bounds for Distances and Orthogonal Projections
Venue: arXiv cs.CC 2025
Year: 2025

We develop quantitative algorithmic information bounds for orthogonal projections and distances in the plane. Under mild independence conditions, the distance $|x-y|$ and a projection coordinate $p_e x$ each retain at least half the algorithmic information content of $x$ in the sense of finite-precision Kolmogorov complexity, up to lower-order terms. Our bounds support conditioning on coarser approximations, enabling case analyses across precision scales. The proofs introduce a surrogate point selection step. Via the point-to-set principle we derive a new bound on the Hausdorff dimension of pinned distance sets, showing that every analytic set $E\subseteq\mathbb{R}^2$ with $\dim_H(E)\leq 1$ satisfies \[\sup_{x\in E}\dim_H(Δ_x E)\geq \frac{3}{4}\dim_H(E).\] We also extend Bourgain's theorem on exceptional sets for orthogonal projections to all sets that admit optimal Hausdorff oracles.
Toward Distributed 3D Gaussian Splatting for High-Resolution Isosurface Visualization
Venue: arXiv cs.DC 2025
Year: 2025

We present a multi-GPU extension of the 3D Gaussian Splatting (3D-GS) pipeline for scientific visualization. Building on previous work that demonstrated high-fidelity isosurface reconstruction using Gaussian primitives, we incorporate a multi-GPU training backend adapted from Grendel-GS to enable scalable processing of large datasets. By distributing optimization across GPUs, our method improves training throughput and supports high-resolution reconstructions that exceed single-GPU capacity. In our experiments, the system achieves a 5.6X speedup on the Kingsnake dataset (4M Gaussians) using four GPUs compared to a single-GPU baseline, and successfully trains the Miranda dataset (18M Gaussians) that is an infeasible task on a single A100 GPU. This work lays the groundwork for integrating 3D-GS into HPC-based scientific workflows, enabling real-time post hoc and in situ visualization of complex simulations.
HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Positional encoding mechanisms enable Transformers to model sequential structure and long-range dependencies in text. While absolute positional encodings struggle with extrapolation to longer sequences due to fixed positional representations, and relative approaches like Alibi exhibit performance degradation on extremely long contexts, the widely-used Rotary Positional Encoding (RoPE) introduces oscillatory attention patterns that hinder stable long-distance dependency modelling. We address these limitations through a geometric reformulation of positional encoding. Drawing inspiration from Lorentz transformations in hyperbolic geometry, we propose Hyperbolic Rotary Positional Encoding (HoPE), which leverages hyperbolic functions to implement Lorentz rotations on token representations. Theoretical analysis demonstrates that RoPE is a special case of our generalized formulation. HoPE fundamentally resolves RoPE's slation issues by enforcing monotonic decay of attention weights with increasing token distances. Extensive experimental results, including perplexity evaluations under several extended sequence benchmarks, show that HoPE consistently exceeds existing positional encoding methods. These findings underscore HoPE's enhanced capacity for representing and generalizing long-range dependencies. Data and code will be available.
Recomposer: Event-roll-guided generative audio editing
Venue: arXiv cs.SD 2025
Year: 2025

Editing complex real-world sound scenes is difficult because individual sound sources overlap in time. Generative models can fill-in missing or corrupted details based on their strong prior understanding of the data domain. We present a system for editing individual sound events within complex scenes able to delete, insert, and enhance individual sound events based on textual edit descriptions (e.g., ``enhance Door'') and a graphical representation of the event timing derived from an ``event roll'' transcription. We present an encoder-decoder transformer working on SoundStream representations, trained on synthetic (input, desired output) audio example pairs formed by adding isolated sound events to dense, real-world backgrounds. Evaluation reveals the importance of each part of the edit descriptions -- action, class, timing. Our work demonstrates ``recomposition'' is an important and practical application.
Dual-Branch Convolutional Framework for Spatial and Frequency-Based Image Forgery Detection
Venue: arXiv cs.LG 2025
Year: 2025

With a very rapid increase in deepfakes and digital image forgeries, ensuring the authenticity of images is becoming increasingly challenging. This report introduces a forgery detection framework that combines spatial and frequency-based features for detecting forgeries. We propose a dual branch convolution neural network that operates on features extracted from spatial and frequency domains. Features from both branches are fused and compared within a Siamese network, yielding 64 dimensional embeddings for classification. When benchmarked on CASIA 2.0 dataset, our method achieves an accuracy of 77.9%, outperforming traditional statistical methods. Despite its relatively weaker performance compared to larger, more complex forgery detection pipelines, our approach balances computational complexity and detection reliability, making it ready for practical deployment. It provides a strong methodology for forensic scrutiny of digital images. In a broader sense, it advances the state of the art in visual forensics, addressing an urgent requirement in media verification, law enforcement and digital content reliability.
Elucidating the Design Space of Decay in Linear Attention
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents a comprehensive investigation into the decay mechanisms inherent in linear complexity sequence models. We systematically delineate the design space of decay mechanisms across four pivotal dimensions: parameterization strategy, which refers to the computational methodology for decay; parameter sharing, which involves the utilization of supplementary parameters for decay computation; decay granularity, comparing scalar versus vector-based decay; and compatibility with relative positional encoding methods, such as Rotary Position Embedding (RoPE). Through an extensive series of experiments conducted on diverse language modeling tasks, we uncovered several critical insights. Firstly, the design of the parameterization strategy for decay requires meticulous consideration. Our findings indicate that effective configurations are typically confined to a specific range of parameters. Secondly, parameter sharing cannot be used arbitrarily, as it may cause decay values to be too large or too small, thereby significantly impacting performance. Thirdly, under identical parameterization strategies, scalar decay generally underperforms compared to its vector-based counterpart. However, in certain scenarios with alternative parameterization strategies, scalar decay may unexpectedly surpass vector decay in efficacy. Lastly, our analysis reveals that RoPE, a commonly employed relative positional encoding method, typically fails to provide tangible benefits to the majority of linear attention mechanisms.