OpenRaiser/Intern-Atlas huggingface dataset:
Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit Poetry
Venue: arXiv cs.CL 2025
Year: 2025

Sanskrit, an ancient language with a rich linguistic heritage, presents unique challenges for automatic speech recognition (ASR) due to its phonemic complexity and the phonetic transformations that occur at word junctures, similar to the connected speech found in natural conversations. Due to these complexities, there has been limited exploration of ASR in Sanskrit, particularly in the context of its poetic verses, which are characterized by intricate prosodic and rhythmic patterns. This gap in research raises the question: How can we develop an effective ASR system for Sanskrit, particularly one that captures the nuanced features of its poetic form? In this study, we introduce Vedavani, the first comprehensive ASR study focused on Sanskrit Vedic poetry. We present a 54-hour Sanskrit ASR dataset, consisting of 30,779 labelled audio samples from the Rig Veda and Atharva Veda. This dataset captures the precise prosodic and rhythmic features that define the language. We also benchmark the dataset on various state-of-the-art multilingual speech models.$^{1}$ Experimentation revealed that IndicWhisper performed the best among the SOTA models.
MotionPersona: Characteristics-aware Locomotion Control
Venue: arXiv cs.GR 2025
Year: 2025

We present MotionPersona, a novel real-time character controller that allows users to characterize a character by specifying attributes such as physical traits, mental states, and demographics, and projects these properties into the generated motions for animating the character. In contrast to existing deep learning-based controllers, which typically produce homogeneous animations tailored to a single, predefined character, MotionPersona accounts for the impact of various traits on human motion as observed in the real world. To achieve this, we develop a block autoregressive motion diffusion model conditioned on SMPLX parameters, textual prompts, and user-defined locomotion control signals. We also curate a comprehensive dataset featuring a wide range of locomotion types and actor traits to enable the training of this characteristic-aware controller. Unlike prior work, MotionPersona is the first method capable of generating motion that faithfully reflects user-specified characteristics (e.g., an elderly person's shuffling gait) while responding in real time to dynamic control inputs. Additionally, we introduce a few-shot characterization technique as a complementary conditioning mechanism, enabling customization via short motion clips when language prompts fall short. Through extensive experiments, we demonstrate that MotionPersona outperforms existing methods in characteristics-aware locomotion control, achieving superior motion quality and diversity. Results, code, and demo can be found at: https://motionpersona25.github.io/.
Empirical Validation of the Independent Chip Model
Venue: arXiv cs.GT 2025
Year: 2025

The independent chip model (ICM) forms a cornerstone of all modern poker tournament strategy. However, despite its prominence, the ICM's performance in the real world has not been sufficiently scrutinized, especially at a large scale. In this paper, we introduce our new dataset of poker tournaments, consisting of results of over ten thousand events. Then, using this dataset, we perform two experiments as part of a large-scale empirical validation of the ICM. First, we verify that the ICM performs more accurately than a baseline we propose. Second, we obtain empirical evidence of the ICM underestimating the performances of players with larger stacks while overestimating those who are short-stacked. Our contributions may be useful to future researchers developing new algorithms for estimating a player's value in poker tournaments.
On the Interaction of Noise, Compression Role, and Adaptivity under $(L_0, L_1)$-Smoothness: An SDE-based Approach
Venue: arXiv cs.LG 2025
Year: 2025

Using stochastic differential equation (SDE) approximations, we study the dynamics of Distributed SGD, Distributed Compressed SGD, and Distributed SignSGD under $(L_0,L_1)$-smoothness and flexible noise assumptions. Our analysis provides insights -- which we validate through simulation -- into the intricate interactions between batch noise, stochastic gradient compression, and adaptivity in this modern theoretical setup. For instance, we show that \textit{adaptive} methods such as Distributed SignSGD can successfully converge under standard assumptions on the learning rate scheduler, even under heavy-tailed noise. On the contrary, Distributed (Compressed) SGD with pre-scheduled decaying learning rate fails to achieve convergence, unless such a schedule also accounts for an inverse dependency on the gradient norm -- de facto falling back into an adaptive method.
What do professional software developers need to know to succeed in an age of Artificial Intelligence?
Venue: arXiv cs.AI 2025
Year: 2025

Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each, illustrating how developers use AI at work. From all of these, we distilled our findings in the form of 5 insights. We found that the skills & knowledge to be a successful AI-enhanced developer are organized into four domains (using Generative AI effectively, core software engineering, adjacent engineering, and adjacent non-engineering) deployed at critical junctures throughout a 6-step task workflow. In order to "future proof" developers for this age of AI, on-the-job learning initiatives and computer science degree programs will need to target both "soft" skills and the technical skills & knowledge in all four domains to reskill, upskill and safeguard against deskilling.
FastCAR: Fast Classification And Regression for Task Consolidation in Multi-Task Learning to Model a Continuous Property Variable of Detected Object Class
Venue: arXiv cs.CV 2025
Year: 2025

FastCAR is a novel task consolidation approach in Multi-Task Learning (MTL) for a classification and a regression task, despite the non-triviality of task heterogeneity with only a subtle correlation. The approach addresses the classification of a detected object (occupying the entire image frame) and regression for modeling a continuous property variable (for instances of an object class), a crucial use case in science and engineering. FastCAR involves a label transformation approach that is amenable for use with only a single-task regression network architecture. FastCAR outperforms traditional MTL model families, parametrized in the landscape of architecture and loss weighting schemes, when learning both tasks are collectively considered (classification accuracy of 99.54%, regression mean absolute percentage error of 2.4%). The experiments performed used "Advanced Steel Property Dataset" contributed by us https://github.com/fastcandr/AdvancedSteel-Property-Dataset. The dataset comprises 4536 images of 224x224 pixels, annotated with discrete object classes and its hardness property that can take continuous values. Our proposed FastCAR approach for task consolidation achieves training time efficiency (2.52x quicker) and reduced inference latency (55% faster) than benchmark MTL networks.
AniTrack: A Power-Efficient, Time-Slotted and Robust UWB Localization System for Animal Tracking in a Controlled Setting
Venue: arXiv cs.RO 2025
Year: 2025

Accurate localization is essential for a wide range of applications, including asset tracking, smart agriculture, and animal monitoring. While traditional localization methods, such as Global Navigation Satellite System (GNSS), Wi-Fi, and Bluetooth Low Energy (BLE), offer varying levels of accuracy and coverage, they have drawbacks regarding power consumption, infrastructure requirements, and deployment flexibility. Ultra-Wideband (UWB) is emerging as an alternative, offering centimeter-level accuracy and energy efficiency, especially suitable for medium to large field monitoring with capabilities to work indoors and outdoors. However, existing UWB localization systems require infrastructure with mains power to supply the anchors, which impedes their scalability and ease of deployment. This underscores the need for a fully battery-powered and energy-efficient localization system. This paper presents an energy-optimized, battery-operated UWB localization system that leverages Long Range Wide Area Network (LoRaWAN) for data transmission to a server backend. By employing single-sided two-way ranging (SS-TWR) in a time-slotted localization approach, the power consumption both on the anchor and the tag is reduced, while maintaining high accuracy. With a low average power consumption of 20.44 mW per anchor and 7.19 mW per tag, the system allows fully battery-powered operation for up to 25 days, achieving average accuracy of 13.96 cm with self-localizing anchors on a 600 m2 testing ground. To validate its effectiveness and ease of installation in a challenging application scenario, ten anchors and two tags were successfully deployed in a tropical zoological biome where they could be used to track Aldabra Giant Tortoises (Aldabrachelys gigantea).
Evaluating the Contextual Integrity of False Positives in Algorithmic Travel Surveillance
Venue: arXiv cs.CY 2025
Year: 2025

International air travel is highly surveilled. While surveillance is deemed necessary for law enforcement to prevent and detect terrorism and other serious crimes, even the most accurate algorithmic mass surveillance systems produce high numbers of false positives. Despite the potential impact of false positives on the fundamental rights of millions of passengers, algorithmic travel surveillance is lawful in the EU. However, as the system's processing practices and accuracy are kept secret by law, it is unknown to what degree passengers are accepting of the system's interference with their rights to privacy and data protection. We conducted a nationally representative survey of the adult population of Finland (N=1550) to assess their attitudes towards algorithmic mass surveillance in air travel and its potential expansion to other travel contexts. Furthermore, we developed a novel approach for estimating the threshold, beyond which, the number of false positives breaches individuals' perception of contextual integrity. Surprisingly, when faced with a trade-off between privacy and security, even very high false positive counts were perceived as legitimate. This result could be attributed to Finland's high-trust cultural context, but also raises questions about people's capacity to account for privacy harms that happen to other people. We conclude by discussing how legal and ethical approaches to legitimising algorithmic surveillance based on individual rights may overlook the statistical or systemic properties of mass surveillance.
Ethical AI: Towards Defining a Collective Evaluation Framework
Venue: arXiv cs.AI 2025
Year: 2025

Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems. This article addresses these challenges by proposing a modular ethical assessment framework built on ontological blocks of meaning-discrete, interpretable units that encode ethical principles such as fairness, accountability, and ownership. By integrating these blocks with FAIR (Findable, Accessible, Interoperable, Reusable) principles, the framework supports scalable, transparent, and legally aligned ethical evaluations, including compliance with the EU AI Act. Using a real-world use case in AI-powered investor profiling, the paper demonstrates how the framework enables dynamic, behavior-informed risk classification. The findings suggest that ontological blocks offer a promising path toward explainable and auditable AI ethics, though challenges remain in automation and probabilistic reasoning.
Whispers of Many Shores: Cultural Alignment through Collaborative Cultural Expertise
Venue: arXiv cs.AI 2025
Year: 2025

The integration of large language models (LLMs) into global applications necessitates effective cultural alignment for meaningful and culturally-sensitive interactions. Current LLMs often lack the nuanced understanding required for diverse cultural contexts, and adapting them typically involves costly full fine-tuning. To address this, we introduce a novel soft prompt fine-tuning framework that enables efficient and modular cultural alignment. Our method utilizes vectorized prompt tuning to dynamically route queries to a committee of culturally specialized 'expert' LLM configurations, created by optimizing soft prompt embeddings without altering the base model's parameters. Extensive experiments demonstrate that our framework significantly enhances cultural sensitivity and adaptability, improving alignment scores from 0.208 to 0.820, offering a robust solution for culturally-aware LLM deployment. This research paves the way for subsequent investigations into enhanced cultural coverage and dynamic expert adaptation, crucial for realizing autonomous AI with deeply nuanced understanding in a globally interconnected world.
CASPER: A Large Scale Spontaneous Speech Dataset
Venue: arXiv cs.CL 2025
Year: 2025

The success of large language models has driven interest in developing similar speech processing capabilities. However, a key challenge is the scarcity of high-quality spontaneous speech data, as most existing datasets contain scripted dialogues. To address this, we present a novel pipeline for eliciting and recording natural dialogues and release our dataset with 100+ hours of spontaneous speech. Our approach fosters fluid, natural conversations while encouraging a diverse range of topics and interactive exchanges. Unlike traditional methods, it facilitates genuine interactions, providing a reproducible framework for future data collection. This paper introduces our dataset and methodology, laying the groundwork for addressing the shortage of spontaneous speech data. We plan to expand this dataset in future stages, offering a growing resource for the research community.
3D Gaussian Splat Vulnerabilities
Venue: arXiv cs.CR 2025
Year: 2025

With 3D Gaussian Splatting (3DGS) being increasingly used in safety-critical applications, how can an adversary manipulate the scene to cause harm? We introduce CLOAK, the first attack that leverages view-dependent Gaussian appearances - colors and textures that change with viewing angle - to embed adversarial content visible only from specific viewpoints. We further demonstrate DAGGER, a targeted adversarial attack directly perturbing 3D Gaussians without access to underlying training data, deceiving multi-stage object detectors e.g., Faster R-CNN, through established methods such as projected gradient descent. These attacks highlight underexplored vulnerabilities in 3DGS, introducing a new potential threat to robotic learning for autonomous navigation and other safety-critical 3DGS applications.
Lazy Heuristic Search for Solving POMDPs with Expensive-to-Compute Belief Transitions
Venue: arXiv cs.RO 2025
Year: 2025

Heuristic search solvers like RTDP-Bel and LAO* have proven effective for computing optimal and bounded sub-optimal solutions for Partially Observable Markov Decision Processes (POMDPs), which are typically formulated as belief MDPs. A belief represents a probability distribution over possible system states. Given a parent belief and an action, computing belief state transitions involves Bayesian updates that combine the transition and observation models of the POMDP to determine successor beliefs and their transition probabilities. However, there is a class of problems, specifically in robotics, where computing these transitions can be prohibitively expensive due to costly physics simulations, raycasting, or expensive collision checks required by the underlying transition and observation models, leading to long planning times. To address this challenge, we propose Lazy RTDP-Bel and Lazy LAO*, which defer computing expensive belief state transitions by leveraging Q-value estimation, significantly reducing planning time. We demonstrate the superior performance of the proposed lazy planners in domains such as contact-rich manipulation for pose estimation, outdoor navigation in rough terrain, and indoor navigation with a 1-D LiDAR sensor. Additionally, we discuss practical Q-value estimation techniques for commonly encountered problem classes that our lazy planners can leverage. Our results show that lazy heuristic search methods dramatically improve planning speed by postponing expensive belief transition evaluations while maintaining solution quality.
DLM-One: Diffusion Language Models for One-Step Sequence Generation
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces DLM-One, a score-distillation-based framework for one-step sequence generation with continuous diffusion language models (DLMs). DLM-One eliminates the need for iterative refinement by aligning the scores of a student model's outputs in the continuous token embedding space with the score function of a pretrained teacher DLM. We investigate whether DLM-One can achieve substantial gains in sampling efficiency for language modeling. Through comprehensive experiments on DiffuSeq -- a representative continuous DLM -- we show that DLM-One achieves up to ~500x speedup in inference time while maintaining competitive performance on benchmark text generation tasks used to evaluate the teacher models. We further analyze the method's empirical behavior across multiple datasets, providing initial insights into its generality and practical applicability. Our findings position one-step diffusion as a promising direction for efficient, high-quality language generation and broader adoption of continuous diffusion models operating in embedding space for natural language processing.
CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review
Venue: arXiv cs.SE 2025
Year: 2025

Reinforcement learning (RL) to improve code review comment generation requires handling unstructured outputs, making reinforcement learning (RL) feedback challenging. The two main RL approaches, namely RL with Verifiable Feedback (RLVR) and RL with AI Feedback (RLAIF), offer trade-offs: RLVR provides reliable feedback for structured tasks like code generation, while RLAIF works for unstructured outputs but is subjective. We bridge this gap with CRScore++, an RL framework that leverages both LLM-based subjective feedback and verifiable signals for training. Extending CRScore, a code review evaluation metric integrating LLMs with verifiers like linters and code smell detectors, CRScore++ transforms these signals into training rewards. We show that CRScore++ improves a weaker student model through a combination of supervised fine-tuning and RL critique from a stronger teacher model, thus enabling generalization to novel programming languages.
Improving Protein Sequence Design through Designability Preference Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Protein sequence design methods have demonstrated strong performance in sequence generation for de novo protein design. However, as the training objective was sequence recovery, it does not guarantee designability--the likelihood that a designed sequence folds into the desired structure. To bridge this gap, we redefine the training objective by steering sequence generation toward high designability. To do this, we integrate Direct Preference Optimization (DPO), using AlphaFold pLDDT scores as the preference signal, which significantly improves the in silico design success rate. To further refine sequence generation at a finer, residue-level granularity, we introduce Residue-level Designability Preference Optimization (ResiDPO), which applies residue-level structural rewards and decouples optimization across residues. This enables direct improvement in designability while preserving regions that already perform well. Using a curated dataset with residue-level annotations, we fine-tune LigandMPNN with ResiDPO to obtain EnhancedMPNN, which achieves a nearly 3-fold increase in in silico design success rate (from 6.56% to 17.57%) on a challenging enzyme design benchmark.
Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework
Venue: arXiv cs.LG 2025
Year: 2025

Most materials science datasets are limited to atomic geometries (e.g., XYZ files), restricting their utility for multimodal learning and comprehensive data-centric analysis. These constraints have historically impeded the adoption of advanced machine learning techniques in the field. This work introduces MultiCrystalSpectrumSet (MCS-Set), a curated framework that expands materials datasets by integrating atomic structures with 2D projections and structured textual annotations, including lattice parameters and coordination metrics. MCS-Set enables two key tasks: (1) multimodal property and summary prediction, and (2) constrained crystal generation with partial cluster supervision. Leveraging a human-in-the-loop pipeline, MCS-Set combines domain expertise with standardized descriptors for high-quality annotation. Evaluations using state-of-the-art language and vision-language models reveal substantial modality-specific performance gaps and highlight the importance of annotation quality for generalization. MCS-Set offers a foundation for benchmarking multimodal models, advancing annotation practices, and promoting accessible, versatile materials science datasets. The dataset and implementations are available at https://github.com/KurbanIntelligenceLab/MultiCrystalSpectrumSet.
Learning Aerodynamics for the Control of Flying Humanoid Robots
Venue: arXiv cs.RO 2025
Year: 2025

Robots with multi-modal locomotion are an active research field due to their versatility in diverse environments. In this context, additional actuation can provide humanoid robots with aerial capabilities. Flying humanoid robots face challenges in modeling and control, particularly with aerodynamic forces. This paper addresses these challenges from a technological and scientific standpoint. The technological contribution includes the mechanical design of iRonCub-Mk1, a jet-powered humanoid robot, optimized for jet engine integration, and hardware modifications for wind tunnel experiments on humanoid robots for precise aerodynamic forces and surface pressure measurements. The scientific contribution offers a comprehensive approach to model and control aerodynamic forces using classical and learning techniques. Computational Fluid Dynamics (CFD) simulations calculate aerodynamic forces, validated through wind tunnel experiments on iRonCub-Mk1. An automated CFD framework expands the aerodynamic dataset, enabling the training of a Deep Neural Network and a linear regression model. These models are integrated into a simulator for designing aerodynamic-aware controllers, validated through flight simulations and balancing experiments on the iRonCub-Mk1 physical prototype.