OpenRaiser/Intern-Atlas huggingface dataset:
CSDformer: A Conversion Method for Fully Spike-Driven Transformer
Venue: arXiv cs.CV 2025
Year: 2025

Spike-based transformer is a novel architecture aiming to enhance the performance of spiking neural networks while mitigating the energy overhead inherent to transformers. However, methods for generating these models suffer from critical limitations: excessive training costs introduced by direct training methods, or unavoidably hardware-unfriendly operations in existing conversion methods. In this paper, we propose CSDformer, a novel conversion method for fully spike-driven transformers. We tailor a conversion-oriented transformer-based architecture and propose a new function NReLU to replace softmax in self-attention. Subsequently, this model is quantized and trained, and converted into a fully spike-driven model with temporal decomposition technique. Also, we propose delayed Integrate-andFire neurons to reduce conversion errors and improve the performance of spiking models. We evaluate CSDformer on ImageNet, CIFAR-10 and CIFAR-100 datasets and achieve 76.36% top-1 accuracy under 7 time-steps on ImageNet, demonstrating superiority over state-of-the-art models. Furthermore, CSDformer eliminates the need for training SNNs, thereby reducing training costs (reducing computational resource by 75% and accelerating training speed by 2-3$\times$). To the best of our knowledge, this is the first fully spike-driven transformer-based model developed via conversion method, achieving high performance under ultra-low latency, while dramatically reducing both computational complexity and training overhead.
CorefInst: Leveraging LLMs for Multilingual Coreference Resolution
Venue: arXiv cs.CL 2025
Year: 2025

Coreference Resolution (CR) is a crucial yet challenging task in natural language understanding, often constrained by task-specific architectures and encoder-based language models that demand extensive training and lack adaptability. This study introduces the first multilingual CR methodology which leverages decoder-only LLMs to handle both overt and zero mentions. The article explores how to model the CR task for LLMs via five different instruction sets using a controlled inference method. The approach is evaluated across three LLMs; Llama 3.1, Gemma 2, and Mistral 0.3. The results indicate that LLMs, when instruction-tuned with a suitable instruction set, can surpass state-of-the-art task-specific architectures. Specifically, our best model, a fully fine-tuned Llama 3.1 for multilingual CR, outperforms the leading multilingual CR model (i.e., Corpipe 24 single stage variant) by 2 pp on average across all languages in the CorefUD v1.2 dataset collection.
An Unlearning Framework for Continual Learning
Venue: arXiv cs.LG 2025
Year: 2025

Growing concerns surrounding AI safety and data privacy have driven the development of Machine Unlearning as a potential solution. However, current machine unlearning algorithms are designed to complement the offline training paradigm. The emergence of the Continual Learning (CL) paradigm promises incremental model updates, enabling models to learn new tasks sequentially. Naturally, some of those tasks may need to be unlearned to address safety or privacy concerns that might arise. We find that applying conventional unlearning algorithms in continual learning environments creates two critical problems: performance degradation on retained tasks and task relapse, where previously unlearned tasks resurface during subsequent learning. Furthermore, most unlearning algorithms require data to operate, which conflicts with CL's philosophy of discarding past data. A clear need arises for unlearning algorithms that are data-free and mindful of future learning. To that end, we propose UnCLe, an Unlearning framework for Continual Learning. UnCLe employs a hypernetwork that learns to generate task-specific network parameters, using task embeddings. Tasks are unlearned by aligning the corresponding generated network parameters with noise, without requiring any data. Empirical evaluations on several vision data sets demonstrate UnCLe's ability to sequentially perform multiple learning and unlearning operations with minimal disruption to previously acquired knowledge.
Attention-based Mixture of Experts for Robust Speech Deepfake Detection
Venue: arXiv cs.SD 2025
Year: 2025

AI-generated speech is becoming increasingly used in everyday life, powering virtual assistants, accessibility tools, and other applications. However, it is also being exploited for malicious purposes such as impersonation, misinformation, and biometric spoofing. As speech deepfakes become nearly indistinguishable from real human speech, the need for robust detection methods and effective countermeasures has become critically urgent. In this paper, we present the ISPL's submission to the SAFE challenge at IH&MMSec 2025, where our system ranked first across all tasks. Our solution introduces a novel approach to audio deepfake detection based on a Mixture of Experts architecture. The proposed system leverages multiple state-of-the-art detectors, combining their outputs through an attention-based gating network that dynamically weights each expert based on the input speech signal. In this design, each expert develops a specialized understanding of the shared training data by learning to capture different complementary aspects of the same input through inductive biases. Experimental results indicate that our method outperforms existing approaches across multiple datasets. We further evaluate and analyze the performance of our system in the SAFE challenge.
From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge
Venue: arXiv cs.CV 2025
Year: 2025

Visual anomaly detection is a strongly application-driven field of research. Consequently, the connection between academia and industry is of paramount importance. In this regard, we present the VAND 3.0 Challenge to showcase current progress in anomaly detection across different practical settings whilst addressing critical issues in the field. The challenge hosted two tracks, fostering the development of anomaly detection methods robust against real-world distribution shifts (Category 1) and exploring the capabilities of Vision Language Models within the few-shot regime (Category 2), respectively. The participants' solutions reached significant improvements over previous baselines by combining or adapting existing approaches and fusing them with novel pipelines. While for both tracks the progress in large pre-trained vision (language) backbones played a pivotal role for the performance increase, scaling up anomaly detection methods more efficiently needs to be addressed by future research to meet real-time and computational constraints on-site.
Tensor-Based Self-Calibration of Cameras via the TrifocalCalib Method
Venue: arXiv cs.CV 2025
Year: 2025

Estimating camera intrinsic parameters without prior scene knowledge is a fundamental challenge in computer vision. This capability is particularly important for applications such as autonomous driving and vehicle platooning, where precalibrated setups are impractical and real-time adaptability is necessary. To advance the state-of-the-art, we present a set of equations based on the calibrated trifocal tensor, enabling projective camera self-calibration from minimal image data. Our method, termed TrifocalCalib, significantly improves accuracy and robustness compared to both recent learning-based and classical approaches. Unlike many existing techniques, our approach requires no calibration target, imposes no constraints on camera motion, and simultaneously estimates both focal length and principal point. Evaluations in both procedurally generated synthetic environments and structured dataset-based scenarios demonstrate the effectiveness of our approach. To support reproducibility, we make the code publicly available.
Propuesta de implementación de catálogos federados para espacios de datos sobre DataHub
Venue: arXiv cs.DB 2025
Year: 2025

In the digital era, data spaces are emerging as key ecosystems for the secure and controlled exchange of information among participants. To achieve this, components such as metadata catalogs and data space connectors are essential. This document proposes an implementation and integration solution for both elements, considering standardization guidelines for data formats, metadata, and protocols, which ensures interoperability. A hybrid solution is presented: DataHub is used as a federated catalog for robust metadata management, leveraging its advanced ingestion, governance, and lineage capabilities. On the other hand, a custom implementation, Rainbow Catalog, manages ODRL policies for access and usage. This integration makes it possible to query datasets from DataHub and associate them with ODRL policies, facilitating negotiation and transfer flows defined by the Dataspace Protocol. The result is a system that combines the power of DataHub for large-scale cataloging with the policy management of the connector crucial for sovereignty and trust in data spaces.
Crosslingual Optimized Metric for Translation Assessment of Indian Languages
Venue: arXiv cs.CL 2025
Year: 2025

Automatic evaluation of translation remains a challenging task owing to the orthographic, morphological, syntactic and semantic richness and divergence observed across languages. String-based metrics such as BLEU have previously been extensively used for automatic evaluation tasks, but their limitations are now increasingly recognized. Although learned neural metrics have helped mitigate some of the limitations of string-based approaches, they remain constrained by a paucity of gold evaluation data in most languages beyond the usual high-resource pairs. In this present work we address some of these gaps. We create a large human evaluation ratings dataset for 13 Indian languages covering 21 translation directions and then train a neural translation evaluation metric named Cross-lingual Optimized Metric for Translation Assessment of Indian Languages (COMTAIL) on this dataset. The best performing metric variants show significant performance gains over previous state-of-the-art when adjudging translation pairs with at least one Indian language. Furthermore, we conduct a series of ablation studies to highlight the sensitivities of such a metric to changes in domain, translation quality, and language groupings. We release both the COMTAIL dataset and the accompanying metric models.
Turk-LettuceDetect: A Hallucination Detection Models for Turkish RAG Applications
Venue: arXiv cs.CL 2025
Year: 2025

The widespread adoption of Large Language Models (LLMs) has been hindered by their tendency to hallucinate, generating plausible but factually incorrect information. While Retrieval-Augmented Generation (RAG) systems attempt to address this issue by grounding responses in external knowledge, hallucination remains a persistent challenge, particularly for morphologically complex, low-resource languages like Turkish. This paper introduces Turk-LettuceDetect, the first suite of hallucination detection models specifically designed for Turkish RAG applications. Building on the LettuceDetect framework, we formulate hallucination detection as a token-level classification task and fine-tune three distinct encoder architectures: a Turkish-specific ModernBERT, TurkEmbed4STS, and multilingual EuroBERT. These models were trained on a machine-translated version of the RAGTruth benchmark dataset containing 17,790 instances across question answering, data-to-text generation, and summarization tasks. Our experimental results show that the ModernBERT-based model achieves an F1-score of 0.7266 on the complete test set, with particularly strong performance on structured tasks. The models maintain computational efficiency while supporting long contexts up to 8,192 tokens, making them suitable for real-time deployment. Comparative analysis reveals that while state-of-the-art LLMs demonstrate high recall, they suffer from low precision due to over-generation of hallucinated content, underscoring the necessity of specialized detection mechanisms. By releasing our models and translated dataset, this work addresses a critical gap in multilingual NLP and establishes a foundation for developing more reliable and trustworthy AI applications for Turkish and other languages.
Overcoming challenges in bamboo connections: A review of mechanical properties and structural considerations
Venue: arXiv cs.AR 2025
Year: 2025

Over the past decades, bamboo has increasingly gained attention as a sustainable construction material, through its rapid growth, naturally optimized shape, high mechanical properties, and significant environmental benefits. However, despite these advantages, the use of bamboo in its natural form for structural applications remains limited, partly due to insufficient knowledge of connection behavior, which is crucial for ensuring the long-term reliability and performance of bamboo structures. This article provides a comprehensive review of the key factors to consider in the design of structural bamboo connections and discusses the existing connection classification methods used as guidelines by designers. By synthesizing findings from the literature, our research aims to identify the key parameters interacting with the connection design process, focusing on the anatomical, geometric, and mechanical properties of bamboo, the mechanical requirements of the structure design, and the building methods. A critical analysis of Janssen's classification of bamboo connections, based on force transfer modes and later refined by Widyowijatnoko, is presented. Finally, we discuss the identified research gaps and emphasize the need for integrated design approaches supported by guidelines to support the broader adoption of bamboo in construction.
ConfClip: Confidence-Weighted and Clipped Reward for Reinforcement Learning in LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) has become a standard paradigm for refining large language models (LLMs) beyond pre-training and instruction tuning. A prominent line of work is RL with verifiable rewards (RLVR), which leverages automatically verifiable outcomes (e.g., correctness or executability) to generate reward signals. While efficient, this framework faces two key limitations: First, its binary feedback is too sparse to capture the quality of the reasoning process. Second, its coarse-grained rewards potentially lead to vanishing gradients. Inspired by observations from human learning, we introduce a RL technique that integrates verifiable outcomes with the model's own confidence estimates. This joint design enriches the reward signal, providing finer-grained feedback and implicitly supervising the reasoning process. Experimental results demonstrate that our proposed method enhances RL performance across multiple datasets and reduces token consumption during inference, while incurring negligible additional training cost. Moreover, it can be used as a plug-in module to enhance other state-of-the-art RL methods.
Symbol Detection in Inter-Symbol Interference Channels using Expectation Propagation with Channel Shortening
Venue: arXiv cs.IT 2025
Year: 2025

Iterative message passing detection based on expectation propagation(EP) has demonstrated near-optimum performance in many signal processing and communication scenarios. The method remains feasible even for channel impulse responses (CIRs), where the optimal Bahl-Cocke-Jelinek-Raviv (BCJR) detector is infeasible. However, significant performance degradation occurs for channels with strong inter-symbol interference (ISI), where the initial linear minimum mean square error (LMMSE) estimate is inaccurate. We propose an EP-based detector that operates in a transformed signal space obtained by channel shortening. Specifically, instead of the conventional approach that iterates between an LMMSE estimator and a non-linear symbol-wise demapper, the proposed method iterates between a linear channel shortening filter-based estimator and a nonlinear BCJR detector with reduced memory compared to the actual channel. Additionally, we propose a deliberate mismatch between the initialized messages and the initialized covariance used in the linear estimator in the first iteration for faster convergence. The proposed approach is evaluated for the well-known Proakis-C ISI channel and for CIRs from a wireless measurement campaign. We demonstrate improvements of up to 6dB at 2 bits per channel use and an improved performance-complexity trade-off over conventional EP-based detection.
WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) have shown promise in visual-textual reasoning, with Multimodal Chain-of-Thought (MCoT) prompting significantly enhancing interpretability. However, existing MCoT methods rely on rationale-rich datasets and largely focus on inter-object reasoning, overlooking the intra-object understanding crucial for image classification. To address this gap, we propose WISE, a Weak-supervision-guided Step-by-step Explanation method that augments any image classification dataset with MCoTs by reformulating the concept-based representations from Concept Bottleneck Models (CBMs) into concise, interpretable reasoning chains under weak supervision. Experiments across ten datasets show that our generated MCoTs not only improve interpretability by 37% but also lead to gains in classification accuracy when used to fine-tune MLLMs. Our work bridges concept-based interpretability and generative MCoT reasoning, providing a generalizable framework for enhancing MLLMs in fine-grained visual understanding.
Learning Neural Antiderivatives
Venue: arXiv cs.LG 2025
Year: 2025

Neural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representations of repeated antiderivatives directly from a function, a continuous analogue of summed-area tables. Although widely used in discrete domains, such cumulative schemes rely on grids, which prevents their applicability in continuous neural contexts. We introduce and analyze a range of neural methods for repeated integration, including both adaptations of prior work and novel designs. Our evaluation spans multiple input dimensionalities and integration orders, assessing both reconstruction quality and performance in downstream tasks such as filtering and rendering. These results enable integrating classical cumulative operators into modern neural systems and offer insights into learning tasks involving differential and integral operators.
Quickest Change Detection in Continuous-Time in Presence of a Covert Adversary
Venue: arXiv cs.IT 2025
Year: 2025

We investigate the problem of covert quickest change detection in a continuous-time setting, where a Brownian motion experiences a drift change at an unknown time. Unlike classical formulations, we consider a covert adversary who adjusts the post-change drift $μ= μ(γ)$ as a function of the false alarm constraint parameter $γ$, with the goal of remaining undetected for as long as possible. Leveraging the exact expressions for the average detection delay (ADD) and average time to false alarm (AT2FA) known for the continuous-time CuSum procedure, we rigorously analyze how the asymptotic behavior of ADD evolves as $μ(γ) \to 0$ with increasing $γ$. Our results reveal that classical detection delay characterizations no longer hold in this regime. We derive sharp asymptotic expressions for the ADD under various convergence rates of $μ(γ)$, identify precise conditions for maintaining covertness, and characterize the total damage inflicted by the adversary. We show that the adversary achieves maximal damage when the drift scales as $μ(γ) = Θ(1/\sqrtγ)$, marking a fundamental trade-off between stealth and impact in continuous-time detection systems.
Efficient Linearizability Monitoring
Venue: arXiv cs.PL 2025
Year: 2025

This paper revisits the fundamental problem of monitoring the linearizability of concurrent stacks, queues, sets, and multisets. Given a history of a library implementing one of these abstract data types, the monitoring problem is to answer whether the given history is linearizable. For stacks, queues, and (multi)sets, we present monitoring algorithms with complexities $\mathcal{O}(n^2)$, $\mathcal{O}(n\; log\, n)$, and $\mathcal{O}{(n)}$, respectively, where $n$ is the number of operations in the input history. For stacks and queues, our results hold under the standard assumption of {\it data-independence}, i.e., the behavior of the library is not sensitive to the actual values stored in the data structure. Past works to solve the same problems have cubic time complexity and (more seriously) have correctness issues: they either (i) lack correctness proofs or (ii) the suggested correctness proofs are erroneous (we present counter-examples), or (iii) have incorrect algorithms. Our improved complexity results rely on substantially different algorithms for which we provide detailed proofs of correctness. We have implemented our stack and queue algorithms in LiMo (Linearizability Monitor). We evaluate LiMo and compare it with the state-of-the-art tool Violin -- whose correctness proofs we have found errors in -- which checks for linearizability violations. Our experimental evaluation confirms that LiMo outperforms Violin regarding both efficiency and scalability.
Theory Meets Practice for Bit Vectors Supporting Rank and Select
Venue: arXiv cs.DS 2025
Year: 2025

Bit vectors with support for fast rank and select are a fundamental building block for compressed data structures. We close a gap between theory and practice by analyzing an important part of the design space and experimentally evaluating a sweet spot. The result is the first implementation of a rank and select data structure for bit vectors with worst-case constant query time, good practical performance, and a space-overhead of just 0.78%, i.e., between $4.5\times$ and $64.1\times$ less than previous implementations.
B-Privacy: Defining and Enforcing Privacy in Weighted Voting
Venue: arXiv cs.CR 2025
Year: 2025

In traditional, one-vote-per-person voting systems, privacy equates with ballot secrecy: voting tallies are published, but individual voters' choices are concealed. Voting systems that weight votes in proportion to token holdings, though, are now prevalent in cryptocurrency and web3 systems. We show that these weighted-voting systems overturn existing notions of voter privacy. Our experiments demonstrate that even with secret ballots, publishing raw tallies often reveals voters' choices. Weighted voting thus requires a new framework for privacy. We introduce a notion called B-privacy whose basis is bribery, a key problem in voting systems today. B-privacy captures the economic cost to an adversary of bribing voters based on revealed voting tallies. We propose a mechanism to boost B-privacy by noising voting tallies. We prove bounds on its tradeoff between B-privacy and transparency, meaning reported-tally accuracy. Analyzing 3,582 proposals across 30 Decentralized Autonomous Organizations (DAOs), we find that the prevalence of large voters ("whales") limits the effectiveness of any B-Privacy-enhancing technique. However, our mechanism proves to be effective in cases without extreme voting weight concentration: among proposals requiring coalitions of $\geq5$ voters to flip outcomes, our mechanism raises B-privacy by a geometric mean factor of $4.1\times$. Our work offers the first principled guidance on transparency-privacy tradeoffs in weighted-voting systems, complementing existing approaches that focus on ballot secrecy and revealing fundamental constraints that voting weight concentration imposes on privacy mechanisms.