OpenRaiser/Intern-Atlas huggingface dataset:
Scalable Thread-Safety Analysis of Java Classes with CodeQL
Venue: arXiv cs.SE 2025
Year: 2025

In object-oriented languages software developers rely on thread-safe classes to implement concurrent applications. However, determining whether a class is thread-safe is a challenging task. This paper presents a highly scalable method to analyze thread-safety in Java classes. We provide a definition of thread-safety for Java classes founded on the correctness principle of the Java memory model, data race freedom. We devise a set of properties for Java classes that are proven to ensure thread-safety. We encode these properties in the static analysis tool CodeQL to automatically analyze Java source code. We perform an evaluation on the top 1000 GitHub repositories. The evaluation comprises 3632865 Java classes; with 1992 classes annotated as @ThreadSafe from 71 repositories. These repositories include highly popular software such as Apache Flink (24.6k stars), Facebook Fresco (17.1k stars), PrestoDB (16.2k starts), and gRPC (11.6k starts). Our queries detected thousands of thread-safety errors. The running time of our queries is below 2 minutes for repositories up to 200k lines of code, 20k methods, 6000 fields, and 1200 classes. We have submitted a selection of detected concurrency errors as PRs, and developers positively reacted to these PRs. We have submitted our CodeQL queries to the main CodeQL repository, and they are currently in the process of becoming available as part of GitHub actions. The results demonstrate the applicability and scalability of our method to analyze thread-safety in real-world code bases.
ContextFusion and Bootstrap: An Effective Approach to Improve Slot Attention-Based Object-Centric Learning
Venue: arXiv cs.CV 2025
Year: 2025

A key human ability is to decompose a scene into distinct objects and use their relationships to understand the environment. Object-centric learning aims to mimic this process in an unsupervised manner. Recently, the slot attention-based framework has emerged as a leading approach in this area and has been widely used in various downstream tasks. However, existing slot attention methods face two key limitations: (1) a lack of high-level semantic information. In current methods, image areas are assigned to slots based on low-level features such as color and texture. This makes the model overly sensitive to low-level features and limits its understanding of object contours, shapes, or other semantic characteristics. (2) The inability to fine-tune the encoder. Current methods require a stable feature space throughout training to enable reconstruction from slots, which restricts the flexibility needed for effective object-centric learning. To address these limitations, we propose a novel ContextFusion stage and a Bootstrap Branch, both of which can be seamlessly integrated into existing slot attention models. In the ContextFusion stage, we exploit semantic information from the foreground and background, incorporating an auxiliary indicator that provides additional contextual cues about them to enrich the semantic content beyond low-level features. In the Bootstrap Branch, we decouple feature adaptation from the original reconstruction phase and introduce a bootstrap strategy to train a feature-adaptive mechanism, allowing for more flexible adaptation. Experimental results show that our method significantly improves the performance of different SOTA slot attention models on both simulated and real-world datasets.
NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task
Venue: arXiv cs.CL 2025
Year: 2025

We present the findings of the sixth Nuanced Arabic Dialect Identification (NADI 2025) Shared Task, which focused on Arabic speech dialect processing across three subtasks: spoken dialect identification (Subtask 1), speech recognition (Subtask 2), and diacritic restoration for spoken dialects (Subtask 3). A total of 44 teams registered, and during the testing phase, 100 valid submissions were received from eight unique teams. The distribution was as follows: 34 submissions for Subtask 1 "five teamsæ, 47 submissions for Subtask 2 "six teams", and 19 submissions for Subtask 3 "two teams". The best-performing systems achieved 79.8% accuracy on Subtask 1, 35.68/12.20 WER/CER (overall average) on Subtask 2, and 55/13 WER/CER on Subtask 3. These results highlight the ongoing challenges of Arabic dialect speech processing, particularly in dialect identification, recognition, and diacritic restoration. We also summarize the methods adopted by participating teams and briefly outline directions for future editions of NADI.
Targeted Physical Evasion Attacks in the Near-Infrared Domain
Venue: arXiv cs.CR 2025
Year: 2025

A number of attacks rely on infrared light sources or heat-absorbing material to imperceptibly fool systems into misinterpreting visual input in various image recognition applications. However, almost all existing approaches can only mount untargeted attacks and require heavy optimizations due to the use-case-specific constraints, such as location and shape. In this paper, we propose a novel, stealthy, and cost-effective attack to generate both targeted and untargeted adversarial infrared perturbations. By projecting perturbations from a transparent film onto the target object with an off-the-shelf infrared flashlight, our approach is the first to reliably mount laser-free targeted attacks in the infrared domain. Extensive experiments on traffic signs in the digital and physical domains show that our approach is robust and yields higher attack success rates in various attack scenarios across bright lighting conditions, distances, and angles compared to prior work. Equally important, our attack is highly cost-effective, requiring less than US\$50 and a few tens of seconds for deployment. Finally, we propose a novel segmentation-based detection that thwarts our attack with an F1-score of up to 99%.
NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking
Venue: arXiv cs.CV 2025
Year: 2025

The long-standing division between \textit{online} and \textit{offline} Multi-Object Tracking (MOT) has led to fragmented solutions that fail to address the flexible temporal requirements of real-world deployment scenarios. Current \textit{online} trackers rely on frame-by-frame hand-crafted association strategies and struggle with long-term occlusions, whereas \textit{offline} approaches can cover larger time gaps, but still rely on heuristic stitching for arbitrarily long sequences. In this paper, we introduce NOOUGAT, the first tracker designed to operate with arbitrary temporal horizons. NOOUGAT leverages a unified Graph Neural Network (GNN) framework that processes non-overlapping subclips, and fuses them through a novel Autoregressive Long-term Tracking (ALT) layer. The subclip size controls the trade-off between latency and temporal context, enabling a wide range of deployment scenarios, from frame-by-frame to batch processing. NOOUGAT achieves state-of-the-art performance across both tracking regimes, improving \textit{online} AssA by +2.3 on DanceTrack, +9.2 on SportsMOT, and +5.0 on MOT20, with even greater gains in \textit{offline} mode.
A substitution lemma for multiple context-free languages
Venue: arXiv cs.FL 2025
Year: 2025

We present a new criterion for proving that a language is not multiple context-free, which we call a Substitution Lemma. We apply it to show a sample selection of languages are not multiple context-free, including the word problem of $F_2\times F_2$. Our result is in contrast to Kanazawa et al. [2014, Theory Comput. Syst.] who proved that it was not possible to generalise the standard pumping lemma for context-free languages to multiple context-free languages, and Kanazawa [2019, Inform. and Comput.] who showed a weak variant of generalised Ogden's lemma does not apply to multiple context-free languages. We also record that groups with multiple context-free word problem have decidable rational subset membership problem.
AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) can inadvertently reflect societal biases present in their training data, leading to harmful or prejudiced outputs. In the Indian context, our empirical evaluations across a suite of models reveal that biases around caste and religion are particularly salient. Yet, most existing mitigation strategies are Western-centric and fail to address these local nuances. We propose AMBEDKAR, a framework inspired by the egalitarian vision of Dr B. R. Ambedkar, architect of the Indian Constitution, to guide LLM outputs toward fairness, neutrality, and inclusion in line with Articles 14 to 17. Our approach introduces a Constitution-Aware Decoding Layer, guided by the AI Constitution of India and applied only at inference time, without any parameter updates to the base model. We incorporate a speculative decoding algorithm that proactively reduces casteist and communal bias during generation. This mitigation layer operates directly within the decoding process, avoiding changes to model internals and lowering the computational and infrastructural costs associated with retraining. We reinterpret speculative decoding not merely as an efficiency tool but as a mechanism for fairness. In this framework, a Small Language Model (SLM) acts as a potentially biased generator, while a constitutionally guided Large Language Model (LLM) serves as the verifier. Rather than accelerating generation, the LLM enforces bias-robust trajectories in the SLM outputs. This inversion of roles gives rise to a fairness-by-speculation paradigm. Our approach yields an absolute reduction of bias up to 26.41 percent compared to baseline. Our source code, datasets, and results are available at https://anonymous.4open.science/r/AMBEDKAR-983B/
Learning Social Heuristics for Human-Aware Path Planning
Venue: arXiv cs.RO 2025
Year: 2025

Social robotic navigation has been at the center of numerous studies in recent years. Most of the research has focused on driving the robotic agent along obstacle-free trajectories, respecting social distances from humans, and predicting their movements to optimize navigation. However, in order to really be socially accepted, the robots must be able to attain certain social norms that cannot arise from conventional navigation, but require a dedicated learning process. We propose Heuristic Planning with Learned Social Value (HPLSV), a method to learn a value function encapsulating the cost of social navigation, and use it as an additional heuristic in heuristic-search path planning. In this preliminary work, we apply the methodology to the common social scenario of joining a queue of people, with the intention of generalizing to further human activities.
SegFormer Fine-Tuning with Dropout: Advancing Hair Artifact Removal in Skin Lesion Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Hair artifacts in dermoscopic images present significant challenges for accurate skin lesion analysis, potentially obscuring critical diagnostic features in dermatological assessments. This work introduces a fine-tuned SegFormer model augmented with dropout regularization to achieve precise hair mask segmentation. The proposed SegformerWithDropout architecture leverages the MiT-B2 encoder, pretrained on ImageNet, with an in-channel count of 3 and 2 output classes, incorporating a dropout probability of 0.3 in the segmentation head to prevent overfitting. Training is conducted on a specialized dataset of 500 dermoscopic skin lesion images with fine-grained hair mask annotations, employing 10-fold cross-validation, AdamW optimization with a learning rate of 0.001, and cross-entropy loss. Early stopping is applied based on validation loss, with a patience of 3 epochs and a maximum of 20 epochs per fold. Performance is evaluated using a comprehensive suite of metrics, including Intersection over Union (IoU), Dice coefficient, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). Experimental results from the cross-validation demonstrate robust performance, with average Dice coefficients reaching approximately 0.96 and IoU values of 0.93, alongside favorable PSNR (around 34 dB), SSIM (0.97), and low LPIPS (0.06), highlighting the model's effectiveness in accurate hair artifact segmentation and its potential to enhance preprocessing for downstream skin cancer detection tasks.
Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Pedestrian Attribute Recognition (PAR) involves identifying various human attributes from images with applications in intelligent monitoring systems. The scarcity of large-scale annotated datasets hinders the generalization of PAR models, specially in complex scenarios involving occlusions, varying poses, and diverse environments. Recent advances in diffusion models have shown promise for generating diverse and realistic synthetic images, allowing to expand the size and variability of training data. However, the potential of diffusion-based data expansion for generating PAR-like images remains underexplored. Such expansion may enhance the robustness and adaptability of PAR models in real-world scenarios. This paper investigates the effectiveness of diffusion models in generating synthetic pedestrian images tailored to PAR tasks. We identify key parameters of img2img diffusion-based data expansion; including text prompts, image properties, and the latest enhancements in diffusion-based data augmentation, and examine their impact on the quality of generated images for PAR. Furthermore, we employ the best-performing expansion approach to generate synthetic images for training PAR models, by enriching the zero-shot datasets. Experimental results show that prompt alignment and image properties are critical factors in image generation, with optimal selection leading to a 4.5% improvement in PAR recognition performance.
AudioRWKV: Efficient and Stable Bidirectional RWKV for Audio Pattern Recognition
Venue: arXiv cs.SD 2025
Year: 2025

Recently, Transformers (e.g., Audio Spectrogram Transformers, AST) and state-space models (e.g., Audio Mamba, AuM) have achieved remarkable progress in audio modeling. However, the O(L^2) computational complexity of the Transformer architecture hinders efficient long-sequence processing, while the Mamba architecture tends to become unstable when scaling parameters and data. To address these challenges, this paper proposes AudioRWKV (A-RWKV), a highly efficient and stable architecture for audio modeling. Specifically, we inherit the stable and efficient recurrent formulation of RWKV7 and replace its 1D token-shift operation with a 2D depthwise separable convolution to better capture local spectro-temporal patterns. Furthermore, we adapt the original causal WKV kernel into a bidirectional WKV kernel (Bi-WKV), enabling global context modeling over the entire audio sequence while maintaining linear computational complexity. Benefiting from the inherent stability of the RWKV7 foundation, A-RWKV scales seamlessly to larger model sizes. Experimental results demonstrate that, under the same linear-model regime, A-RWKV-S (22M) achieves performance parity with AuM-B (92M) while exhibiting more stable throughput than AST; for long-form audio (~5 minutes 28 seconds), WKV7 achieves up to a 13.3X speedup in processing.
FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models tend to struggle when dealing with specialized domains. While all aspects of evaluation hold importance, factuality is the most critical one. Similarly, reliable fact-checking tools and data sources are essential for hallucination mitigation. We address these issues by providing a comprehensive Fact-checking Benchmark FActBench covering four generation tasks and six state-of-the-art Large Language Models (LLMs) for the Medical domain. We use two state-of-the-art Fact-checking techniques: Chain-of-Thought (CoT) Prompting and Natural Language Inference (NLI). Our experiments show that the fact-checking scores acquired through the Unanimous Voting of both techniques correlate best with Domain Expert Evaluation.
Adaptive Navigation Strategy for Low-Thrust Proximity Operations in Circular Relative Orbit
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents an adaptive observer-based navigation strategy for spacecraft in Circular Relative Orbit (CRO) scenarios, addressing challenges in proximity operations like formation flight and uncooperative target inspection. The proposed method adjusts observer gains based on the estimated state to achieve fast convergence and low noise sensitivity in state estimation. A Lyapunov-based analysis ensures stability and accuracy, while simulations using vision-based sensor data validate the approach under realistic conditions. Compared to classical observers with time-invariant gains, the proposed method enhances trajectory tracking precision and reduces control input switching, making it a promising solution for autonomous spacecraft localization and control.
Baichuan-M2: Scaling Medical Capability with Large Verifier System
Venue: arXiv cs.LG 2025
Year: 2025

As large language models (LLMs) advance in conversational and reasoning capabilities, their practical application in healthcare has become a critical research focus. However, there is a notable gap between the performance of medical LLMs on static benchmarks such as USMLE and their utility in real-world clinical decision-making. This discrepancy arises because traditional exams fail to capture the dynamic, interactive nature of medical consultations. To address this challenge, we introduce a novel dynamic verification framework that moves beyond static answer verifier, establishing a large-scale, high-fidelity interactive reinforcement learning system. Our framework comprises two key components: a Patient Simulator that creates realistic clinical environments using de-identified medical records, and a Clinical Rubrics Generator that dynamically produces multi-dimensional evaluation metrics. Building on this foundation, we develop Baichuan-M2, a 32B-parameter medical augmented reasoning model trained through a multi-stage reinforcement learning strategy with an improved Group Relative Policy Optimization (GRPO) algorithm. Evaluated on HealthBench, Baichuan-M2 outperforms all other open-source models and most advanced closed-source counterparts, achieving a score above 32 on the challenging HealthBench Hard benchmark-previously exceeded only by GPT-5. Our work demonstrates that robust dynamic verifier system is essential for aligning LLM capabilities with practical clinical applications, establishing a new Pareto front in the performance-parameter trade-off for medical AI deployment.
Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit
Venue: arXiv cs.IR 2025
Year: 2025

News recommendations are complex, with diversity playing a vital role. So far, existing literature predominantly focuses on specific aspects of news diversity, such as viewpoints. In this paper, we introduce multi-aspect diversification in four distinct recommendation modes and outline the nuanced challenges in diversifying lists, sequences, summaries, and interactions. Our proposed research direction combines symbolic and subsymbolic artificial intelligence, leveraging both knowledge graphs and rule learning. We plan to evaluate our models using user studies to not only capture behavior but also their perceived experience. Our vision to balance news consumption points to other positive effects for users (e.g., increased serendipity) and society (e.g., decreased polarization).
Towards Fundamental Language Models: Does Linguistic Competence Scale with Model Size?
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models offer impressive language capabilities but suffer from well-known limitations, including hallucinations, biases, privacy concerns, and high computational costs. These issues are largely driven by the combination of linguistic competence and factual memorization within a single monolithic model. This paper introduces and empirically supports the Fundamental Language Model (FLM) paradigm, which advocates for smaller, linguistically competent models that offload factual retrieval to external tools. We evaluate models ranging from 135M to 32B parameters across three dimensions: linguistic competence, external factual knowledge, and internal factual knowledge. Our findings reveal that while both linguistic competence and factual knowledge improve with scale, internal factual knowledge grows significantly faster, suggesting that model size is more closely tied to memorization than to core language ability. These results support a modular approach to language modeling, where compact, linguistically proficient models serve as the foundation for tool-augmented systems. The FLM paradigm offers a path toward more efficient, interpretable, and sustainable NLP solutions.
A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients
Venue: arXiv cs.CV 2025
Year: 2025

Neck pain is the primary symptom of cervical spondylosis, yet its underlying mechanisms remain unclear, leading to uncertain treatment outcomes. To address the challenges of multimodal feature fusion caused by imaging differences and spatial mismatches, this paper proposes an Adaptive Bidirectional Pyramid Difference Convolution (ABPDC) module that facilitates multimodal integration by exploiting the advantages of difference convolution in texture extraction and grayscale invariance, and a Feature Pyramid Registration Auxiliary Network (FPRAN) to mitigate structural misalignment. Experiments on the MMCSD dataset demonstrate that the proposed model achieves superior prediction accuracy of postoperative neck pain recovery compared with existing methods, and ablation studies further confirm its effectiveness.
VariAntNet: Learning Decentralized Control of Multi-Agent Systems
Venue: arXiv cs.LG 2025
Year: 2025

A simple multi-agent system can be effectively utilized in disaster response applications, such as firefighting. Such a swarm is required to operate in complex environments with limited local sensing and no reliable inter-agent communication or centralized control. These simple robotic agents, also known as Ant Robots, are defined as anonymous agents that possess limited sensing capabilities, lack a shared coordinate system, and do not communicate explicitly with one another. A key challenge for simple swarms lies in maintaining cohesion and avoiding fragmentation despite limited-range sensing. Recent advances in machine learning offer effective solutions to some of the classical decentralized control challenges. We propose VariAntNet, a deep learning-based decentralized control model designed to facilitate agent swarming and collaborative task execution. VariAntNet includes geometric features extraction from unordered, variable-sized local observations. It incorporates a neural network architecture trained with a novel, differentiable, multi-objective, mathematically justified loss function that promotes swarm cohesiveness by utilizing the properties of the visibility graph Laplacian matrix. VariAntNet is demonstrated on the fundamental multi-agent gathering task, where agents with bearing-only and limited-range sensing must gather at some location. VariAntNet significantly outperforms an existing analytical solution, achieving more than double the convergence rate while maintaining high swarm connectivity across varying swarm sizes. While the analytical solution guarantees cohesion, it is often too slow in practice. In time-critical scenarios, such as emergency response operations where lives are at risk, slower analytical methods are impractical and justify the loss of some agents within the swarm. This paper presents and analyzes this trade-off in detail.