OpenRaiser/Intern-Atlas huggingface dataset:
Culture is Everywhere: A Call for Intentionally Cultural Evaluation
Venue: arXiv cs.CL 2025
Year: 2025

The prevailing ``trivia-centered paradigm'' for evaluating the cultural alignment of large language models (LLMs) is increasingly inadequate as these models become more advanced and widely deployed. Existing approaches typically reduce culture to static facts or values, testing models via multiple-choice or short-answer questions that treat culture as isolated trivia. Such methods neglect the pluralistic and interactive realities of culture, and overlook how cultural assumptions permeate even ostensibly ``neutral'' evaluation settings. In this position paper, we argue for \textbf{intentionally cultural evaluation}: an approach that systematically examines the cultural assumptions embedded in all aspects of evaluation, not just in explicitly cultural tasks. We systematically characterize the what, how, and circumstances by which culturally contingent considerations arise in evaluation, and emphasize the importance of researcher positionality for fostering inclusive, culturally aligned NLP research. Finally, we discuss implications and future directions for moving beyond current benchmarking practices, discovering important applications that we don't know exist, and involving communities in evaluation design through HCI-inspired participatory methodologies.
GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Text-to-SQL, the task of translating natural language questions into SQL queries, has significantly advanced with the introduction of Large Language Models (LLMs), broadening database accessibility for a wide range of users. Despite substantial progress in generating valid SQL, current LLMs still struggle with complex queries. To address this limitation, test-time strategies such as Best-of-N (BoN) and Majority Voting (Maj) are often employed, based on the assumption that LLMs can produce correct answers after multiple attempts. However, these methods rely on surface-level heuristics, selecting the syntactically correct query through execution-based BoN (ex-BoN) or the most frequently generated one through Majority Voting. Recently, Outcome Reward Models (ORMs), which assign utility scores to generated outputs based on semantic correctness, have emerged as a promising reinforcement learning approach for improving model alignment. We argue that ORMs could serve as an effective new test-time heuristic, although their application in this context remains largely underexplored. In this work, we propose a unified framework for training ORMs tailored to the Text-to-SQL task and assess their effectiveness as a test-time heuristic within the BoN strategy. We benchmark ORMs against ex-BoN and Maj across the BIRD and Spider datasets, fine-tuning diverse open-source LLMs from the Qwen2, Granite3, and Llama3 families. Results show that ORMs outperform ex-BoN and Maj, achieving execution accuracy gains of +4.33% (BIRD) and +2.10% (Spider) over ex-BoN, and +2.91% (BIRD) and +0.93% (Spider) over Maj. We further demonstrate that finetuning models already aligned with SQL generation, such as OmniSQL, yields superior ORM performance. Additionally, we observe that ORMs achieve competitive results on simple queries and benefit more from an increased number of candidates compared to ex-BoN and Maj.
DeepResearch Arena: The First Exam of LLMs' Research Abilities via Seminar-Grounded Tasks
Venue: arXiv cs.AI 2025
Year: 2025

Deep research agents have attracted growing attention for their potential to orchestrate multi-stage research workflows, spanning literature synthesis, methodological design, and empirical verification. Despite these strides, evaluating their research capability faithfully is rather challenging due to the difficulty of collecting frontier research questions that genuinely capture researchers' attention and intellectual curiosity. To address this gap, we introduce DeepResearch Arena, a benchmark grounded in academic seminars that capture rich expert discourse and interaction, better reflecting real-world research environments and reducing the risk of data leakage. To automatically construct DeepResearch Arena, we propose a Multi-Agent Hierarchical Task Generation (MAHTG) system that extracts research-worthy inspirations from seminar transcripts. The MAHTG system further translates research-worthy inspirations into high-quality research tasks, ensuring the traceability of research task formulation while filtering noise. With the MAHTG system, we curate DeepResearch Arena with over 10,000 high-quality research tasks from over 200 academic seminars, spanning 12 disciplines, such as literature, history, and science. Our extensive evaluation shows that DeepResearch Arena presents substantial challenges for current state-of-the-art agents, with clear performance gaps observed across different models.
Distillation of a tractable model from the VQ-VAE
Venue: arXiv cs.LG 2025
Year: 2025

Deep generative models with discrete latent space, such as the Vector-Quantized Variational Autoencoder (VQ-VAE), offer excellent data generation capabilities, but, due to the large size of their latent space, their probabilistic inference is deemed intractable. We demonstrate that the VQ-VAE can be distilled into a tractable model by selecting a subset of latent variables with high probabilities. This simple strategy is particularly efficient, especially if the VQ-VAE underutilizes its latent space, which is, indeed, very often the case. We frame the distilled model as a probabilistic circuit, and show that it preserves expressiveness of the VQ-VAE while providing tractable probabilistic inference. Experiments illustrate competitive performance in density estimation and conditional generation tasks, challenging the view of the VQ-VAE as an inherently intractable model.
ArabEmoNet: A Lightweight Hybrid 2D CNN-BiLSTM Model with Attention for Robust Arabic Speech Emotion Recognition
Venue: arXiv cs.SD 2025
Year: 2025

Speech emotion recognition is vital for human-computer interaction, particularly for low-resource languages like Arabic, which face challenges due to limited data and research. We introduce ArabEmoNet, a lightweight architecture designed to overcome these limitations and deliver state-of-the-art performance. Unlike previous systems relying on discrete MFCC features and 1D convolutions, which miss nuanced spectro-temporal patterns, ArabEmoNet uses Mel spectrograms processed through 2D convolutions, preserving critical emotional cues often lost in traditional methods. While recent models favor large-scale architectures with millions of parameters, ArabEmoNet achieves superior results with just 1 million parameters, 90 times smaller than HuBERT base and 74 times smaller than Whisper. This efficiency makes it ideal for resource-constrained environments. ArabEmoNet advances Arabic speech emotion recognition, offering exceptional performance and accessibility for real-world applications.
Near-Optimal Dynamic Steiner Spanners for Constant-Curvature Spaces
Venue: arXiv cs.CG 2025
Year: 2025

We consider Steiner spanners in Euclidean and non-Euclidean geometries. In the Euclidean setting, a recent line of work initiated by Le and Solomon [FOCS'19] and further improved by Chang et al. [SoCG'24] obtained Steiner $(1+\varepsilon)$-spanners of size $O_d(\varepsilon^{(1-d)/2}\log(1/\varepsilon)n)$, nearly matching the lower bounds of Bhore and Tóth [SIDMA'22]. We obtain Steiner $(1+\varepsilon)$-spanners of size $O_d(\varepsilon^{(1-d)/2}\log(1/\varepsilon)n)$ not only in $d$-dimensional Euclidean space, but also in $d$-dimensional spherical and hyperbolic space. For any fixed dimension $d$, the obtained edge count is optimal up to an $O(\log(1/\varepsilon))$ factor in each of these spaces. Unlike earlier constructions, our Steiner spanners are based on simple quadtrees, and they can be dynamically maintained, leading to efficient data structures for dynamic approximate nearest neighbours and bichromatic closest pair. In the hyperbolic setting, we also show that $2$-spanners in the hyperbolic plane must have $Ω(n\log n)$ edges, and we obtain a $2$-spanner of size $O_d(n\log n)$ in $d$-dimensional hyperbolic space, matching our lower bound for any constant $d$. Finally, we give a Steiner spanner with additive error $\varepsilon$ in hyperbolic space with $O_d(\varepsilon^{(1-d)/2}\log(α(n)/\varepsilon)n)$ edges, where $α(n)$ is the inverse Ackermann function. Our techniques generalize to closed orientable surfaces of constant curvature as well as to some quotient spaces.
An Information-Flow Perspective on Explainability Requirements: Specification and Verification
Venue: arXiv cs.LO 2025
Year: 2025

Explainable systems expose information about why certain observed effects are happening to the agents interacting with them. We argue that this constitutes a positive flow of information that needs to be specified, verified, and balanced against negative information flow that may, e.g., violate privacy guarantees. Since both explainability and privacy require reasoning about knowledge, we tackle these tasks with epistemic temporal logic extended with quantification over counterfactual causes. This allows us to specify that a multi-agent system exposes enough information such that agents acquire knowledge on why some effect occurred. We show how this principle can be used to specify explainability as a system-level requirement and provide an algorithm for checking finite-state models against such specifications. We present a prototype implementation of the algorithm and evaluate it on several benchmarks, illustrating how our approach distinguishes between explainable and unexplainable systems, and how it allows to pose additional privacy requirements.
MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

The nnUNet segmentation framework adeptly adjusts most hyperparameters in training scripts automatically, but it overlooks the tuning of internal hyperparameters within the segmentation network itself, which constrains the model's ability to generalize. Addressing this limitation, this study presents a novel Self-Adaptive Convolution Module that dynamically adjusts the size of the convolution kernels depending on the unique fingerprints of different datasets. This adjustment enables the MSA2-Net, when equipped with this module, to proficiently capture both global and local features within the feature maps. Self-Adaptive Convolution Module is strategically integrated into two key components of the MSA2-Net: the Multi-Scale Convolution Bridge and the Multi-Scale Amalgamation Decoder. In the MSConvBridge, the module enhances the ability to refine outputs from various stages of the CSWin Transformer during the skip connections, effectively eliminating redundant data that could potentially impair the decoder's performance. Simultaneously, the MSADecoder, utilizing the module, excels in capturing detailed information of organs varying in size during the decoding phase. This capability ensures that the decoder's output closely reproduces the intricate details within the feature maps, thus yielding highly accurate segmentation images. MSA2-Net, bolstered by this advanced architecture, has demonstrated exceptional performance, achieving Dice coefficient scores of 86.49\%, 92.56\%, 93.37\%, and 92.98\% on the Synapse, ACDC, Kvasir, and Skin Lesion Segmentation (ISIC2017) datasets, respectively. This underscores MSA2-Net's robustness and precision in medical image segmentation tasks across various datasets.
MeVe: A Modular System for Memory Verification and Effective Context Control in Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-Augmented Generation (RAG) systems typically face constraints because of their inherent mechanism: a simple top-k semantic search [1]. The approach often leads to the incorporation of irrelevant or redundant information in the context, degrading performance and efficiency [10][11]. This paper presents MeVe, a novel modular architecture intended for Memory Verification and smart context composition. MeVe rethinks the RAG paradigm by proposing a five-phase modular design that distinctly breaks down the retrieval and context composition process into distinct, auditable, and independently tunable phases: initial retrieval, relevance verification, fallback retrieval, context prioritization, and token budgeting. This architecture enables fine-grained control of what knowledge is made available to an LLM, enabling task-dependent filtering and adaptation. We release a reference implementation of MeVe as a proof of concept and evaluate its performance on knowledge-heavy QA tasks over a subset of English Wikipedia [22]. Our results demonstrate that by actively verifying information before composition, MeVe significantly improves context efficiency, achieving a 57% reduction on the Wikipedia dataset and a 75% reduction on the more complex HotpotQA dataset compared to standard RAG implementations [25]. This work provides a framework for more scalable and reliable LLM applications. By refining and distilling contextual information, MeVe offers a path toward better grounding and more accurate factual support [16].
Ultra Fast Warm Start Solution for Graph Recommendations
Venue: arXiv cs.IR 2025
Year: 2025

In this work, we present a fast and effective Linear approach for updating recommendations in a scalable graph-based recommender system UltraGCN. Solving this task is extremely important to maintain the relevance of the recommendations under the conditions of a large amount of new data and changing user preferences. To address this issue, we adapt the simple yet effective low-rank approximation approach to the graph-based model. Our method delivers instantaneous recommendations that are up to 30 times faster than conventional methods, with gains in recommendation quality, and demonstrates high scalability even on the large catalogue datasets.
Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices
Venue: arXiv cs.CR 2025
Year: 2025

Radiation Detection Systems (RDSs) play a vital role in ensuring public safety across various settings, from nuclear facilities to medical environments. However, these systems are increasingly vulnerable to cyber-attacks such as data injection, man-in-the-middle (MITM) attacks, ICMP floods, botnet attacks, privilege escalation, and distributed denial-of-service (DDoS) attacks. Such threats could compromise the integrity and reliability of radiation measurements, posing significant public health and safety risks. This paper presents a new synthetic radiation dataset and an Intrusion Detection System (IDS) tailored for resource-constrained environments, bringing Machine Learning (ML) predictive capabilities closer to the sensing edge layer of critical infrastructure. Leveraging TinyML techniques, the proposed IDS employs an optimized XGBoost model enhanced with pruning, quantization, feature selection, and sampling. These TinyML techniques significantly reduce the size of the model and computational demands, enabling real-time intrusion detection on low-resource devices while maintaining a reasonable balance between efficiency and accuracy.
Statistics-Friendly Confidentiality Protection for Establishment Data, with Applications to the QCEW
Venue: arXiv cs.CR 2025
Year: 2025

Confidentiality for business data is an understudied area of disclosure avoidance, where legacy methods struggle to provide acceptable results. Modern formal privacy techniques designed for person-level data do not provide suitable confidentiality/utility trade-offs due to the highly skewed nature of business data and because extreme outlier records are often important contributors to query answers. In this paper, inspired by Gaussian Differential Privacy, we propose a novel confidentiality framework for business data with a focus on interpretability for policy makers. We propose two query-answering mechanisms and analyze new challenges that arise when noisy query answers are converted into confidentiality-preserving microdata. We evaluate our mechanisms on confidential Quarterly Census of Employment and Wages (QCEW) microdata and a public substitute dataset.
Testing the assumptions about the geometry of sentence embedding spaces: the cosine measure need not apply
Venue: arXiv cs.CL 2025
Year: 2025

Transformer models learn to encode and decode an input text, and produce contextual token embeddings as a side-effect. The mapping from language into the embedding space maps words expressing similar concepts onto points that are close in the space. In practice, the reverse implication is also assumed: words corresponding to close points in this space are similar or related, those that are further are not. Does closeness in the embedding space extend to shared properties for sentence embeddings? We present an investigation of sentence embeddings and show that the geometry of their embedding space is not predictive of their relative performances on a variety of tasks. We compute sentence embeddings in three ways: as averaged token embeddings, as the embedding of the special [CLS] token, and as the embedding of a random token from the sentence. We explore whether there is a correlation between the distance between sentence embedding variations and their performance on linguistic tasks, and whether despite their distances, they do encode the same information in the same manner. The results show that the cosine similarity -- which treats dimensions shallowly -- captures (shallow) commonalities or differences between sentence embeddings, which are not predictive of their performance on specific tasks. Linguistic information is rather encoded in weighted combinations of different dimensions, which are not reflected in the geometry of the sentence embedding space.
Quantifying the Effect of Thermal Illusions in Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

Thermal sensations are central to how we experience the world, yet most virtual and extended reality systems fail to simulate them effectively. While hardware-based thermal displays can provide accurate temperature changes, they are often bulky, power-intensive, and restrict user mobility. Consequently, recent works have explored thermal illusions, perceptual effects that rely on cross-modal interactions, to achieve thermal experiences without physical heating or cooling. While thermal illusions have been shown to consistently alter subjective ratings, the actual extent of their effect on the perceived temperature of interacted objects remains unexplored. To address this, we contribute the findings of two user studies following psychophysical procedures. We first ordered and scaled the effects of a variety of visual and auditory cues (N=20) and subsequently quantified their isolated and combined efficacy in offsetting physical temperature changes (N=24). We found that thermal illusions elicited robust changes in subjective judgments, and auditory cues showed potential as an alternative or complementary approach to established visual techniques. However, the actual effects induced by thermal illusions were relatively small (+-0.5°C) and did not consistently align with abstract ratings, suggesting a need to reconsider how future thermal illusions or experiences are designed and evaluated.
A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
Venue: arXiv cs.RO 2025
Year: 2025

Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change decisions and lane change actions for autonomous vehicles within traffic flow. Firstly, a surrounding vehicles trajectory prediction method is proposed to reduce the risk of future behavior of surrounding vehicles to ego vehicle, and the prediction results are input into the reinforcement learning model as additional information. Secondly, to comprehensively leverage environmental information, the model extracts feature from high-dimensional images and low-dimensional sensor data simultaneously. The fusion of surrounding vehicle trajectory prediction and multi-modal information are used as state space of reinforcement learning to improve the rationality of lane change decision. Finally, we integrate reinforcement learning macro decisions with end-to-end vehicle control to achieve a holistic lane change process. Experiments were conducted within the CARLA simulator, and the results demonstrated that the utilization of a hybrid state space significantly enhances the safety of vehicle lane change decisions.
STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data
Venue: arXiv cs.DC 2025
Year: 2025

Error-bounded lossy compression is one of the most efficient solutions to reduce the volume of scientific data. For lossy compression, progressive decompression and random-access decompression are critical features that enable on-demand data access and flexible analysis workflows. However, these features can severely degrade compression quality and speed. To address these limitations, we propose a novel streaming compression framework that supports both progressive decompression and random-access decompression while maintaining high compression quality and speed. Our contributions are three-fold: (1) we design the first compression framework that simultaneously enables both progressive decompression and random-access decompression; (2) we introduce a hierarchical partitioning strategy to enable both streaming features, along with a hierarchical prediction mechanism that mitigates the impact of partitioning and achieves high compression quality -- even comparable to state-of-the-art (SOTA) non-streaming compressor SZ3; and (3) our framework delivers high compression and decompression speed, up to 6.7$\times$ faster than SZ3.
An Interactive Google Earth Engine Application for Global Multi-Scale Vegetation Analysis Using NDVI Thresholding
Venue: arXiv cs.HC 2025
Year: 2025

Monitoring vegetation dynamics is crucial for addressing global environmental challenges like degradation and deforestation, but traditional remote sensing methods are often complex and resource-intensive. To overcome these barriers, we developed an interactive, cloud-based application on the Google Earth Engine (GEE) platform for few clicks on-demand global vegetation analysis without complex technical knowledge. The application automates the calculation of vegetated areas using the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 and Landsat imagery. It utilizes a median composite of images over a selected period to create a single, robust, cloud-free image, minimizing atmospheric noise and other artifacts. It offers a flexible, global multi-scale analytical platform, allowing users to define regions of interest based on administrative boundaries, protected areas, or custom-drawn polygons. The user-friendly interface enables the selection of specific time periods and NDVI thresholds to quantify vegetation cover in real time, eliminating the need for manual and time intensive data handling and processing. A validation of the platform was conducted for two protected areas in Bangladesh which demonstrated high accuracy, with area estimates showing over 97% agreement with published reference data. By simplifying access to powerful geospatial analytics to general people, this tool provides a scalable and practical solution for researchers, land managers, policymakers, and any interested person to monitor vegetation trends, support conservation efforts, to inform decision making in spatial context where policy maker need to use insights in few clicks and inform environmental policy.