OpenRaiser/Intern-Atlas huggingface dataset:
The Trip to ZigBee Backscatter across a Decade, a Systematic Review
Venue: arXiv cs.NI 2025
Year: 2025

The field of backscatter communication has undergone a profound transformation, evolving from a niche technology for radio-frequency identification (RFID) into a sophisticated paradigm poised to enable a truly battery-free Internet of Things (IoT). This evolution is built upon a deepening understanding of the fundamental principles governing these ultra-low-power links. Modern backscatter systems are no longer simple reflectors of continuous waves but are increasingly designed to interact with complex, data-carrying ambient signals from ubiquitous sources like WiFi, ZigBee, and cellular networks. This review systematically charts the journey of ambient backscatter, particularly focusing on its interaction with ZigBee and other commodity wireless protocols over the last decade. We analyze the progression from foundational proof-of-concept systems that established productive backscatter to modern high-throughput, concurrent, and cross-technology communication architectures. Key advancements in fine-grained modulation, robust synchronization, cross-technology physical layer emulation, and multi-tag coordination are detailed. A comparative analysis of state-of-the-art systems highlights the core trade-offs between performance metrics like data rate and range, power consumption, and compatibility with commodity hardware. Finally, we synthesize the primary challenges, including networking scalability, security vulnerabilities, the near-far problem, and practical deployment hurdles, and outline future research directions, such as integration with Reconfigurable Intelligent Surfaces (RIS) and 6G networks, that promise to further expand the capabilities of this transformative technology.
MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios
Venue: arXiv cs.SE 2025
Year: 2025

Code debugging is a crucial task in software engineering, which attracts increasing attention. While remarkable success has been made in the era of large language models (LLMs), current research still focuses on the simple no-library or single-library setting, ignoring the complex multi-library scenario in real-world applications. To address this limitation, we make the first attempt to introduce MLDebugging (Multi-Library Debugging), a comprehensive benchmark designed to assess debugging challenges within multi-library Python code. Specifically, MLDebugging encompasses 126 distinct Python libraries, covering a wide range of multi-library code issues, categorized into seven distinct types. Furthermore, we conduct a thorough evaluation of MLDebugging using both mainstream open-source and closed-source LLMs and highlight that current LLMs still struggle to correctly perform code debugging across multi-library scenarios. We hope this work can uncover the potential of LLMs in multi-library debugging scenario and offer insights for future research.
FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) have made significant strides in front-end code generation. However, existing benchmarks exhibit several critical limitations: many tasks are overly simplistic, test cases often lack rigor, and end-to-end validation is absent. These issues hinder the accurate assessment of model performance. To address these challenges, we present FrontendBench, a benchmark co-developed by humans and LLMs. FrontendBench categorizes tasks based on code functionality and incorporates interactive test scenarios, enabling a more comprehensive and practical evaluation of front-end code generation capabilities. The benchmark comprises 148 meticulously crafted prompt-test case pairs spanning five levels of web components, from basic UI elements to complex interactive features. Each task reflects realistic front-end development challenges. Furthermore, we introduce an automatic evaluation framework that executes generated code within a sandbox environment and assesses outcomes using predefined test scripts. This framework achieves a 90.54% agreement rate with expert human evaluations, demonstrating high reliability. We benchmark several state-of-the-art LLMs on FrontendBench and observe substantial performance disparities in handling real-world front-end tasks. These results highlight FrontendBench as a reliable and scalable benchmark, supporting consistent multimodal evaluation and providing a robust foundation for future research in front-end code generation. Our data and code will be released soon.
Towards an Approach for Evaluating the Impact of AI Standards The use case of entity resolution
Venue: arXiv cs.CY 2025
Year: 2025

This paper is intended to provide an overview of how the evaluation of standards could be applied to entity resolution, or record linkage. Data quality is of critical importance for many AI applications, and the quality of data, particularly on individuals and businesses, depends critically, in turn, on the quality of the match of entities across different files. Getting entity resolution right is important, because high quality data on entities like people or organization are essential to many AI systems; creating high quality data increasingly requires correctly classifying information that comes from different sources as generated by the same entity. But it is also very difficult because data on the same entity that are acquired from different sources are often inconsistent and have to be carefully reconciled. The use of AI, in the form of machine learning methods, is becoming increasingly important because other approaches are less applicable for modern needs. In particular, manual methods to link data are too costly and slow to scale, and probabilistic methods are inappropriate in the increasingly frequent cases where unique identifiers are not available. The particular focus is on Learning Employment Records, which is a high profile example of the value of entity resolution
Toward Multimodal Privacy in XR: Design and Evaluation of Composite Privatization Methods for Gaze and Body Tracking Data
Venue: arXiv cs.HC 2025
Year: 2025

As extended reality (XR) systems become increasingly immersive and sensor-rich, they enable the collection of behavioral signals such as eye and body telemetry. These signals support personalized and responsive experiences and may also contain unique patterns that can be linked back to individuals. However, privacy mechanisms that naively pair unimodal mechanisms (e.g., independently apply privacy mechanisms for eye and body privatization) are often ineffective at preventing re-identification in practice. In this work, we systematically evaluate real-time privacy mechanisms for XR, both individually and in pair, across eye and body modalities. We assess privacy through re-identification rates and evaluate utility using numerical performance thresholds derived from existing literature to ensure real-time interaction requirements are met. We evaluated four eye and ten body mechanisms across multiple datasets, comprising up to 407 participants. Our results show that when carefully paired, multimodal mechanisms reduce re-identification rate from 80.3% to 26.3% in casual XR applications (e.g., VRChat and Job Simulator) and from 84.8% to 26.1% in competitive XR applications (e.g., Beat Saber and Synth Riders), all while maintaining acceptable performance based on established thresholds. To facilitate adoption, we additionally release XR Privacy SDK, an open-source toolkit enabling developers to integrate the privacy mechanisms into XR applications for real-time use. These findings underscore the potential of modality-specific and context-aware privacy strategies for protecting behavioral data in XR environments.
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture
Venue: arXiv cs.LG 2025
Year: 2025

To empower precision agriculture through distributed machine learning (DML), split learning (SL) has emerged as a promising paradigm, partitioning deep neural networks (DNNs) between edge devices and servers to reduce computational burdens and preserve data privacy. However, conventional SL frameworks' one-split-fits-all strategy is a critical limitation in agricultural ecosystems where edge insect monitoring devices exhibit vast heterogeneity in computational power, energy constraints, and connectivity. This leads to straggler bottlenecks, inefficient resource utilization, and compromised model performance. Bridging this gap, we introduce ReinDSplit, a novel reinforcement learning (RL)-driven framework that dynamically tailors DNN split points for each device, optimizing efficiency without sacrificing accuracy. Specifically, a Q-learning agent acts as an adaptive orchestrator, balancing workloads and latency thresholds across devices to mitigate computational starvation or overload. By framing split layer selection as a finite-state Markov decision process, ReinDSplit convergence ensures that highly constrained devices contribute meaningfully to model training over time. Evaluated on three insect classification datasets using ResNet18, GoogleNet, and MobileNetV2, ReinDSplit achieves 94.31% accuracy with MobileNetV2. Beyond agriculture, ReinDSplit pioneers a paradigm shift in SL by harmonizing RL for resource efficiency, privacy, and scalability in heterogeneous environments.
AI shares emotion with humans across languages and cultures
Venue: arXiv cs.CL 2025
Year: 2025

Effective and safe human-machine collaboration requires the regulated and meaningful exchange of emotions between humans and artificial intelligence (AI). Current AI systems based on large language models (LLMs) can provide feedback that makes people feel heard. Yet it remains unclear whether LLMs represent emotion in language as humans do, or whether and how the emotional tone of their output can be controlled. We assess human-AI emotional alignment across linguistic-cultural groups and model-families, using interpretable LLM features translated from concept-sets for over twenty nuanced emotion categories (including six basic emotions). Our analyses reveal that LLM-derived emotion spaces are structurally congruent with human perception, underpinned by the fundamental affective dimensions of valence and arousal. Furthermore, these emotion-related features also accurately predict large-scale behavioural data on word ratings along these two core dimensions, reflecting both universal and language-specific patterns. Finally, by leveraging steering vectors derived solely from human-centric emotion concepts, we show that model expressions can be stably and naturally modulated across distinct emotion categories, which provides causal evidence that human emotion concepts can be used to systematically induce LLMs to produce corresponding affective states when conveying content. These findings suggest AI not only shares emotional representations with humans but its affective outputs can be precisely guided using psychologically grounded emotion concepts.
HAELT: A Hybrid Attentive Ensemble Learning Transformer Framework for High-Frequency Stock Price Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

High-frequency stock price prediction is challenging due to non-stationarity, noise, and volatility. To tackle these issues, we propose the Hybrid Attentive Ensemble Learning Transformer (HAELT), a deep learning framework combining a ResNet-based noise-mitigation module, temporal self-attention for dynamic focus on relevant history, and a hybrid LSTM-Transformer core that captures both local and long-range dependencies. These components are adaptively ensembled based on recent performance. Evaluated on hourly Apple Inc. (AAPL) data from Jan 2024 to May 2025, HAELT achieves the highest F1-Score on the test set, effectively identifying both upward and downward price movements. This demonstrates HAELT's potential for robust, practical financial forecasting and algorithmic trading.
Spectral partitioning of graphs into compact, connected regions
Venue: arXiv cs.DS 2025
Year: 2025

We define and study a spectral recombination algorithm, SpecReCom, for partitioning a graph into a given number of connected parts. It is straightforward to introduce additional constraints such as the requirement that the weight (or number of vertices) in each part is approximately balanced, and we exemplify this by stating a variant, BalSpecReCom, of the SpecReCom algorithm. We provide empirical evidence that the algorithm achieves more compact partitions than alternatives such as RevReCom by studying a $56\times 56$ grid graph and a planar graph obtained from the state of Colorado.
Diffusion-based Inverse Observation Model for Artificial Skin
Venue: arXiv cs.RO 2025
Year: 2025

Contact-based estimation of object pose is challenging due to discontinuities and ambiguous observations that can correspond to multiple possible system states. This multimodality makes it difficult to efficiently sample valid hypotheses while respecting contact constraints. Diffusion models can learn to generate samples from such multimodal probability distributions through denoising algorithms. We leverage these probabilistic modeling capabilities to learn an inverse observation model conditioned on tactile measurements acquired from a distributed artificial skin. We present simulated experiments demonstrating efficient sampling of contact hypotheses for object pose estimation through touch.
glass: ordered set data structure for client-side order books
Venue: arXiv cs.DS 2025
Year: 2025

The "ordered set" abstract data type with operations "insert", "erase", "find", "min", "max", "next" and "prev" is ubiquitous in computer science. It is usually implemented with red-black trees, $B$-trees, or $B^+$-trees. We present our implementation of ordered set based on a trie. It only supports integer keys (as opposed to keys of any strict weakly ordered type) and is optimized for market data, namely for what we call sequential locality. The following is the list of what we believe to be novelties: * Cached path to exploit sequential locality, and fast truncation thereof on erase operation; * A hash table (or, rather, a cache table) with hard O(1) time guarantees on any operation to speed up key lookup (up to a pre-leaf node); * Hardware-accelerated "find next/previous set bit" operations with BMI2 instruction set extension on x86-64; * Order book-specific features: the preemption principle and the tree restructure operation that prevent the tree from consuming too much memory. We achieve the following speedups over C++'s standard std::map container: 6x-20x on modifying operations, 30x on lookup operations, 9x-15x on real market data, and a more modest 2x-3x speedup on iteration. In this paper, we discuss our implementation.
Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation
Venue: arXiv cs.CV 2025
Year: 2025

We consider the problem of disentangling 3D from large vision-language models, which we show on generative 3D portraits. This allows free-form text control of appearance attributes like age, hair style, and glasses, and 3D geometry control of face expression and camera pose. In this setting, we assume we use a pre-trained large vision-language model (LVLM; CLIP) to generate from a smaller 2D dataset with no additional paired labels and with a pre-defined 3D morphable model (FLAME). First, we disentangle using canonicalization to a 2D reference frame from a deformable neural 3D triplane representation. But another form of entanglement arises from the significant noise in the LVLM's embedding space that describes irrelevant features. This damages output quality and diversity, but we overcome this with a Jacobian regularization that can be computed efficiently with a stochastic approximator. Compared to existing methods, our approach produces portraits with added text and 3D control, where portraits remain consistent when either control is changed. Broadly, this approach lets creators control 3D generators on their own 2D face data without needing resources to label large data or train large models.
Ace-CEFR -- A Dataset for Automated Evaluation of the Linguistic Difficulty of Conversational Texts for LLM Applications
Venue: arXiv cs.CL 2025
Year: 2025

There is an unmet need to evaluate the language difficulty of short, conversational passages of text, particularly for training and filtering Large Language Models (LLMs). We introduce Ace-CEFR, a dataset of English conversational text passages expert-annotated with their corresponding level of text difficulty. We experiment with several models on Ace-CEFR, including Transformer-based models and LLMs. We show that models trained on Ace-CEFR can measure text difficulty more accurately than human experts and have latency appropriate to production environments. Finally, we release the Ace-CEFR dataset to the public for research and development.
Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Venue: arXiv cs.AI 2025
Year: 2025

Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised mobility services. Traditional approaches largely depend on location embeddings learned from historical mobility patterns, limiting their ability to encode explicit spatial information, integrate rich urban semantic context, and accommodate previously unseen locations. To address these challenges, we explore the application of CaLLiPer -- a multimodal representation learning framework that fuses spatial coordinates and semantic features of points of interest through contrastive learning -- for location embedding in individual mobility prediction. CaLLiPer's embeddings are spatially explicit, semantically enriched, and inductive by design, enabling robust prediction performance even in scenarios involving emerging locations. Through extensive experiments on four public mobility datasets under both conventional and inductive settings, we demonstrate that CaLLiPer consistently outperforms strong baselines, particularly excelling in inductive scenarios. Our findings highlight the potential of multimodal, inductive location embeddings to advance the capabilities of human mobility prediction systems. We also release the code and data (https://github.com/xlwang233/Into-the-Unknown) to foster reproducibility and future research.
The Teacher's Dilemma: Balancing Trade-Offs in Programming Education for Emergent Bilingual Students
Venue: arXiv cs.HC 2025
Year: 2025

K-12 computing teachers must navigate complex trade-offs when selecting programming languages and instructional materials for classrooms with emergent bilingual students. While they aim to foster an inclusive learning environment by addressing language barriers that impact student engagement, they must also align with K-12 computer science curricular guidelines and prepare students for industry-standard programming tools. Because programming languages predominantly use English keywords and most instructional materials are written in English, these linguistic barriers introduce cognitive load and accessibility challenges. This paper examines teachers' decisions in balancing these competing priorities, highlighting the tensions between accessibility, curriculum alignment, and workforce preparation. The findings shed light on how our teacher participants negotiate these trade-offs and what factors influence their selection of programming tools to best support EB students while meeting broader educational and professional goals.
Affective-CARA: A Knowledge Graph Driven Framework for Culturally Adaptive Emotional Intelligence in HCI
Venue: arXiv cs.HC 2025
Year: 2025

Culturally adaptive emotional responses remain a critical challenge in affective computing. This paper introduces Affective-CARA, an agentic framework designed to enhance user-agent interactions by integrating a Cultural Emotion Knowledge Graph (derived from StereoKG) with Valence, Arousal, and Dominance annotations, culture-specific data, and cross-cultural checks to minimize bias. A Gradient-Based Reward Policy Optimization mechanism further refines responses according to cultural alignment, affective appropriateness, and iterative user feedback. A Cultural-Aware Response Mediator coordinates knowledge retrieval, reinforcement learning updates, and historical data fusion. By merging real-time user input with past emotional states and cultural insights, Affective-CARA delivers narratives that are deeply personalized and sensitive to diverse cultural norms. Evaluations on AffectNet, SEMAINE DB, and MERD confirm that the framework consistently outperforms baseline models in sentiment alignment, cultural adaptation, and narrative quality. Affective-CARA achieved a Cultural Semantic Density of 9.32 out of 10 and lowered cultural representation bias by 61% (KL-Divergence: 0.28), demonstrating robust performance in generating ethical, adaptive responses. These findings suggest the potential for more inclusive and empathetic interactions, making Affective-CARA an avenue for fostering culturally grounded user experiences across domains such as cross-cultural communication, mental health support, and education.
VideoMAR: Autoregressive Video Generatio with Continuous Tokens
Venue: arXiv cs.CV 2025
Year: 2025

Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video generation remains under-explored. In this paper, we propose \textbf{VideoMAR}, a concise and efficient decoder-only autoregressive image-to-video model with continuous tokens, composing temporal frame-by-frame and spatial masked generation. We first identify temporal causality and spatial bi-directionality as the first principle of video AR models, and propose the next-frame diffusion loss for the integration of mask and video generation. Besides, the huge cost and difficulty of long sequence autoregressive modeling is a basic but crucial issue. To this end, we propose the temporal short-to-long curriculum learning and spatial progressive resolution training, and employ progressive temperature strategy at inference time to mitigate the accumulation error. Furthermore, VideoMAR replicates several unique capacities of language models to video generation. It inherently bears high efficiency due to simultaneous temporal-wise KV cache and spatial-wise parallel generation, and presents the capacity of spatial and temporal extrapolation via 3D rotary embeddings. On the VBench-I2V benchmark, VideoMAR surpasses the previous state-of-the-art (Cosmos I2V) while requiring significantly fewer parameters ($9.3\%$), training data ($0.5\%$), and GPU resources ($0.2\%$).
AsyncSwitch: Asynchronous Text-Speech Adaptation for Code-Switched ASR
Venue: arXiv cs.CL 2025
Year: 2025

Developing code-switched ASR systems is challenging due to language ambiguity and limited exposure to multilingual, code-switched data, while collecting such speech is costly. Prior work generates synthetic audio from text, but these methods are computationally intensive and hard to scale. We introduce AsyncSwitch, a novel asynchronous adaptation framework that leverages large-scale, text-rich web data to pre-expose ASR models to diverse code-switched domains before fine-tuning on paired speech-text corpora. Our three-stage process (1) trains decoder self-attention and feedforward layers on code-switched text, (2) aligns decoder and encoder via cross-attention using limited speech-text data, and (3) fully fine-tunes the entire model. Experiments with Whisper on Malay-English code-switching demonstrate a 9.02% relative WER reduction, while improving monolingual performance in Singlish, Malay, and other English variants.