pwc-archive/papers-with-abstracts huggingface dataset:
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
Authors: Di Jin, Jingyi Cao, Xiaobao Wang, Bingdao Feng, Dongxiao He, Longbiao Wang, Jianwu Dang
Date: 2025-05-23 00:00:00
Tasks: Anomaly Detection, Contrastive Learning, Fraud Detection, Graph Anomaly Detection

Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods typically rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality. However, these approaches overlook a crucial limitation: the presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process. Consequently, this limitation impairs the ability to effectively learn meaningful representations of normal patterns, leading to suboptimal detection performance. To address this issue, we propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process. Moreover, to mitigate bias from the one-step edge removal process, we introduce a novel progressive purification module. This module incrementally refines the graph by iteratively identifying and removing interfering edges, thereby enhancing model performance. Extensive experiments on five benchmark datasets validate the effectiveness of our approach.
SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning
Authors: Shuai Zhao, Heyan Huang, Xinge Li, Xiaokang Chen, Rui Wang
Date: 2025-05-31 00:00:00
Proceeding: Information Processing & Management 2025 4

Neural networks have demonstrated exceptional performance in supervised learning, benefiting from abundant high-quality annotated data. However, obtaining such data in real-world scenarios is costly and labor-intensive. Semi-supervised learning (SSL) offers a solution to this problem. Recent studies, such as Semi-ViT and Noisy Student, which employ consistency regularization or pseudo-labeling, have demonstrated significant achievements. However, they still face challenges, particularly in accurately selecting sufficient high-quality pseudo-labels due to their reliance on fixed thresholds. Recent methods such as FlexMatch and FreeMatch have introduced flexible or self-adaptive thresholding techniques, greatly advancing SSL research. Nonetheless, their process of updating thresholds at each iteration is deemed time-consuming, computationally intensive, and potentially unnecessary. To address these issues, we propose Self-training with Self-adaptive Thresholding (SST), a novel, effective, and efficient SSL framework. SST introduces an innovative Self-Adaptive Thresholding (SAT) mechanism that adaptively adjusts class-specific thresholds based on the model's learning progress. SAT ensures the selection of high-quality pseudo-labeled data, mitigating the risks of inaccurate pseudo-labels and confirmation bias. Extensive experiments demonstrate that SST achieves state-of-the-art performance with remarkable efficiency, generalization, and scalability across various architectures and datasets. Semi-SST-ViT-Huge achieves the best results on competitive ImageNet-1K SSL benchmarks, with 80.7% / 84.9% Top-1 accuracy using only 1% / 10% labeled data. Compared to the fully-supervised DeiT-III-ViT-Huge, which achieves 84.8% Top-1 accuracy using 100% labeled data, our method demonstrates superior performance using only 10% labeled data.
TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation
Authors: Amin Karimi Monsefi, Mridul Khurana, Rajiv Ramnath, Anuj Karpatne, Wei-Lun Chao, Cheng Zhang
Date: 2025-06-02 00:00:00
Tasks: Image Generation, Transfer Learning

We propose TaxaDiffusion, a taxonomy-informed training framework for diffusion models to generate fine-grained animal images with high morphological and identity accuracy. Unlike standard approaches that treat each species as an independent category, TaxaDiffusion incorporates domain knowledge that many species exhibit strong visual similarities, with distinctions often residing in subtle variations of shape, pattern, and color. To exploit these relationships, TaxaDiffusion progressively trains conditioned diffusion models across different taxonomic levels -- starting from broad classifications such as Class and Order, refining through Family and Genus, and ultimately distinguishing at the Species level. This hierarchical learning strategy first captures coarse-grained morphological traits shared by species with common ancestors, facilitating knowledge transfer before refining fine-grained differences for species-level distinction. As a result, TaxaDiffusion enables accurate generation even with limited training samples per species. Extensive experiments on three fine-grained animal datasets demonstrate that outperforms existing approaches, achieving superior fidelity in fine-grained animal image generation. Project page: https://amink8.github.io/TaxaDiffusion/
The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives
Authors: Hemanth Sabbella, Archit Mukherjee, Thivya Kandappu, Sounak Dey, Arpan Pal, Archan Misra, Dong Ma
Date: 2025-06-02 00:00:00
Tasks: Time Series

Spiking neural networks (SNNs) have emerged as a class of bio -inspired networks that leverage sparse, event- driven signaling to achieve low-power computation while inherently modeling temporal dynamics. Such characteristics align closely with the demands of ubiquitous computing systems, which often operate on resource- constrained devices while continuously monitoring and processing time - series sensor data. Despite their unique and promising features, SNNs have received limited attention and remain underexplored (or at least, under-adopted) within the ubiquitous computing community. To address this gap, this paper first introduces the core components of SNNs, both in terms of models and training mechanisms. It then presents a systematic survey of 76 SNN-based studies focused on time-series data analysis, categorizing them into six key application domains. For each domain, we summarize relevant works and subsequent advancements, distill core insights, and highlight key takeaways for researchers and practitioners. To facilitate hands-on experimentation, we also provide a comprehensive review of current software frameworks and neuromorphic hardware platforms, detailing their capabilities and specifications, and then offering tailored recommendations for selecting development tools based on specific application needs. Finally, we identify prevailing challenges within each application domain and propose future research directions that need be explored in ubiquitous community. Our survey highlights the transformative potential of SNNs in enabling energy-efficient ubiquitous sensing across diverse application domains, while also serving as an essential introduction for researchers looking to enter this emerging field.
IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory
Authors: Wei Song, Zhenya Huang, Cheng Cheng, Weibo Gao, Bihan Xu, Guanhao Zhao, Fei Wang, Runze Wu
Date: 2025-06-01 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Large language models (LLMs) have demonstrated exceptional performance across a wide range of natural language tasks. However, selecting the optimal LLM to respond to a user query often necessitates a delicate balance between performance and cost. While powerful models deliver better results, they come at a high cost, whereas smaller models are more cost-effective but less capable. To address this trade-off, we propose IRT-Router, a multi-LLM routing framework that efficiently routes user queries to the most suitable LLM. Inspired by Item Response Theory (IRT), a psychological measurement methodology, IRT-Router explicitly models the relationship between LLM capabilities and user query attributes. This not only enables accurate prediction of response performance but also provides interpretable insights, such as LLM abilities and query difficulty. Additionally, we design an online query warm-up technique based on semantic similarity, further enhancing the online generalization capability of IRT-Router. Extensive experiments on 20 LLMs and 12 datasets demonstrate that IRT-Router outperforms most baseline methods in terms of effectiveness and interpretability. Its superior performance in cold-start scenarios further confirms the reliability and practicality of IRT-Router in real-world applications. Code is available at https://github.com/Mercidaiha/IRT-Router.
Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?
Authors: Zijian Zhao, Dian Jin, Zijing Zhou, XiaoYu Zhang
Date: 2025-06-02 00:00:00

Stage lighting plays an essential role in live music performances, influencing the engaging experience of both musicians and audiences. Given the high costs associated with hiring or training professional lighting engineers, Automatic Stage Lighting Control (ASLC) has gained increasing attention. However, most existing approaches only classify music into limited categories and map them to predefined light patterns, resulting in formulaic and monotonous outcomes that lack rationality. To address this issue, this paper presents an end-to-end solution that directly learns from experienced lighting engineers -- Skip-BART. To the best of our knowledge, this is the first work to conceptualize ASLC as a generative task rather than merely a classification problem. Our method modifies the BART model to take audio music as input and produce light hue and value (intensity) as output, incorporating a novel skip connection mechanism to enhance the relationship between music and light within the frame grid.We validate our method through both quantitative analysis and an human evaluation, demonstrating that Skip-BART outperforms conventional rule-based methods across all evaluation metrics and shows only a limited gap compared to real lighting engineers.Specifically, our method yields a p-value of 0.72 in a statistical comparison based on human evaluations with human lighting engineers, suggesting that the proposed approach closely matches human lighting engineering performance. To support further research, we have made our self-collected dataset, code, and trained model parameters available at https://github.com/RS2002/Skip-BART .
Speech Token Prediction via Compressed-to-fine Language Modeling for Speech Generation
Authors: Wenrui Liu, Qian Chen, Wen Wang, Yafeng Chen, Jin Xu, Zhifang Guo, Guanrou Yang, Weiqin Li, Xiaoda Yang, Tao Jin, Minghui Fang, Jialong Zuo, Bai Jionghao, Zemin Liu
Date: 2025-05-30 00:00:00
Tasks: Language Modeling, Language Modelling, text-to-speech, Text to Speech

Neural audio codecs, used as speech tokenizers, have demonstrated remarkable potential in the field of speech generation. However, to ensure high-fidelity audio reconstruction, neural audio codecs typically encode audio into long sequences of speech tokens, posing a significant challenge for downstream language models in long-context modeling. We observe that speech token sequences exhibit short-range dependency: due to the monotonic alignment between text and speech in text-to-speech (TTS) tasks, the prediction of the current token primarily relies on its local context, while long-range tokens contribute less to the current token prediction and often contain redundant information. Inspired by this observation, we propose a \textbf{compressed-to-fine language modeling} approach to address the challenge of long sequence speech tokens within neural codec language models: (1) \textbf{Fine-grained Initial and Short-range Information}: Our approach retains the prompt and local tokens during prediction to ensure text alignment and the integrity of paralinguistic information; (2) \textbf{Compressed Long-range Context}: Our approach compresses long-range token spans into compact representations to reduce redundant information while preserving essential semantics. Extensive experiments on various neural audio codecs and downstream language models validate the effectiveness and generalizability of the proposed approach, highlighting the importance of token compression in improving speech generation within neural codec language models. The demo of audio samples will be available at https://anonymous.4open.science/r/SpeechTokenPredictionViaCompressedToFinedLM.
Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study
Authors: Laura Müller, Piklu Mallick, Antonio B. Marín-Carballo, Philipp Dönges, Robyn J. N. Kettlitz, Carolina J. Klett-Tammen, Mirjam Kretzschmar, Viola Priesemann, Seba Contreras
Date: 2025-05-30 00:00:00

HIV pre-exposure Prophylaxis (PrEP) has become essential for global HIV control, but its implementation coincides with rising bacterial STI rates among men who have sex with men (MSM). While risk-compensation behavioral changes like reduced condom use are frequently reported, we examine whether intensified asymptomatic screening in PrEP programs creates surveillance artifacts that could be misinterpreted. We developed a compartmental model to represent the simultaneous spread of HIV and chlamydia (as an example of a curable STI), integrating three mechanisms: 1) risk-mediated self-protective behavior, 2) condom use reduction post-PrEP initiation, and 3) PrEP-related asymptomatic STI screening. Increasing PrEP uptake may help to reduce chlamydia prevalence, only if the PrEP-related screening is frequent enough. Otherwise, the effect of PrEP can be disadvantageous, as the drop in self-protective actions caused by larger PrEP uptake cannot be compensated for. Additionally, the change in testing behavior may lead to situations where the trend in the number of positive tests is not a reliable sign of the actual dynamics. We found a plausible mechanism to reconcile conflicting observational evidence on the effect of PrEP on STI rates, showing that simultaneous changes in testing and spreading rates may generate conflicting signals, i.e., that observed trends increase while true prevalence decreases. Asymptomatic screening, together with personalized infection treatment to minimize putative pressure to generate antibiotic resistance, is one of the key determinants of the positive side effects of PrEP in reducing STI incidence.
A Conceptual Introduction to Hetero-functional Graph Theory for Systems-of-Systems
Authors: Amro M. Farid, Amirreza Hosseini, John C. Little
Date: 2025-05-29 00:00:00

A defining feature of twenty first century engineering challenges is their inherent complexity, demanding the convergence of knowledge across diverse disciplines. Establishing consistent methodological foundations for engineering systems remains a challenge -- one that both systems engineering and network science have sought to address. Model-based systems engineering (MBSE) has recently emerged as a practical, interdisciplinary approach for developing complex systems from concept through implementation. In contrast, network science focuses on the quantitative analysis of networks present within engineering systems. This paper introduces hetero-functional graph theory (HFGT) as a conceptual bridge between these two fields, serving as a tutorial for both communities. For systems engineers, HFGT preserves the heterogeneity of conceptual and ontological constructs in MBSE, including system form, function, and concept. For network scientists, it provides multiple graph-based data structures enabling matrix-based quantitative analysis. The modeling process begins with ontological foundations, defining an engineering system as an abstraction and representing it with a model. Model fidelity is assessed using four linguistic properties: soundness, completeness, lucidity, and laconicity. A meta-architecture is introduced to manage the convergence challenges between domain-specific reference architectures and case-specific instantiations. Unlike other meta-architectures, HFGT is rooted in linguistic structures, modeling resources as subjects, system processes as predicates, and operands-such as matter, energy, organisms, information, and money-as objects. These elements are integrated within a system meta-architecture expressed in the Systems Modeling Language (SysML). The paper concludes by offering guidance for further reading.
Towards model-based design of causal manipulations of brain circuits with high spatiotemporal precision
Authors: Anandita De, Roozbeh Kiani, Luca Mazzucato
Date: 2025-05-30 00:00:00

Recent advancements in neurotechnology enable precise spatiotemporal patterns of microstimulations with single-cell resolution. The choice of perturbation sites must satisfy two key criteria: efficacy in evoking significant responses and selectivity for the desired target effects. This choice is currently based on laborious trial-and-error procedures, unfeasible for sequences of multi-site stimulations. Efficient methods to design complex perturbation patterns are urgently needed. Can we design a spatiotemporal pattern of stimulation to steer neural activity and behavior towards a desired target? We outline a method for achieving this goal in two steps. First, we identify the most effective perturbation sites, or hubs, only based on short observations of spontaneous neural activity. Second, we provide an efficient method to design multi-site stimulation patterns by combining approaches from nonlinear dynamical systems, control theory and data-driven methods. We demonstrate the feasibility of our approach using multi-site stimulation patterns in recurrent network models.
WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
Authors: Yaoyao Qian, Jindan Huang, Yuanli Wang, Simon Yu, Kyrie Zhixuan Zhou, Jiayuan Mao, Mingfu Liang, Hanhan Zhou
Date: 2025-06-02 00:00:00
Tasks: Task-Oriented Dialogue Systems

Task-oriented dialogue systems often face difficulties when user utterances seem semantically complete but lack necessary structural information for appropriate system action. This arises because users frequently do not fully understand their own needs, while systems require precise intent definitions. Current LLM-based agents cannot effectively distinguish between linguistically complete and contextually triggerable expressions, lacking frameworks for collaborative intent formation. We present STORM, a framework modeling asymmetric information dynamics through conversations between UserLLM (full internal access) and AgentLLM (observable behavior only). STORM produces annotated corpora capturing expression trajectories and latent cognitive transitions, enabling systematic analysis of collaborative understanding development. Our contributions include: (1) formalizing asymmetric information processing in dialogue systems; (2) modeling intent formation tracking collaborative understanding evolution; and (3) evaluation metrics measuring internal cognitive improvements alongside task performance. Experiments across four language models reveal that moderate uncertainty (40-60%) can outperform complete transparency in certain scenarios, with model-specific patterns suggesting reconsideration of optimal information completeness in human-AI collaboration. These findings contribute to understanding asymmetric reasoning dynamics and inform uncertainty-calibrated dialogue system design.
Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibility
Authors: Yidi Li, Jun Xiao, Zhengda Lu, Yiqun Wang, Haiyong Jiang
Date: 2025-05-27 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, Scheduling, Vector Graphics

This work presents a novel text-to-vector graphics generation approach, Dream3DVG, allowing for arbitrary viewpoint viewing, progressive detail optimization, and view-dependent occlusion awareness. Our approach is a dual-branch optimization framework, consisting of an auxiliary 3D Gaussian Splatting optimization branch and a 3D vector graphics optimization branch. The introduced 3DGS branch can bridge the domain gaps between text prompts and vector graphics with more consistent guidance. Moreover, 3DGS allows for progressive detail control by scheduling classifier-free guidance, facilitating guiding vector graphics with coarse shapes at the initial stages and finer details at later stages. We also improve the view-dependent occlusions by devising a visibility-awareness rendering module. Extensive results on 3D sketches and 3D iconographies, demonstrate the superiority of the method on different abstraction levels of details, cross-view consistency, and occlusion-aware stroke culling.
GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation
Authors: Krishna Acharya, Aleksandr V. Petrov, Juba Ziani
Date: 2025-06-02 00:00:00
Tasks: Sequential Recommendation

We propose Generative Low-rank language model with Semantic Search (GLoSS), a generative recommendation framework that combines large language models with dense retrieval for sequential recommendation. Unlike prior methods such as GPT4Rec, which rely on lexical matching via BM25, GLoSS uses semantic search to retrieve relevant items beyond lexical matching. For query generation, we employ 4-bit quantized LlaMA-3 models fine-tuned with low-rank adaptation (LoRA), enabling efficient training and inference on modest hardware. We evaluate GLoSS on three real-world Amazon review datasets: Beauty, Toys, and Sports, and find that it achieves state-of-the-art performance. Compared to traditional ID-based baselines, GLoSS improves Recall@5 by 33.3%, 52.8%, and 15.2%, and NDCG@5 by 30.0%, 42.6%, and 16.1%, respectively. It also outperforms LLM-based recommenders such as P5, GPT4Rec, LlamaRec and E4SRec with Recall@5 gains of 4.3%, 22.8%, and 29.5%. Additionally, user segment evaluations show that GLoSS performs particularly well for cold-start users in the Amazon Toys and Sports datasets, and benefits from longer user histories in Amazon Beauty dataset, demonstrating robustness across different levels of interaction lengths.
MIRROR: Cognitive Inner Monologue Between Conversational Turns for Persistent Reflection and Reasoning in Conversational LLMs
Authors: Nicole Hsing
Date: 2025-05-31 00:00:00

Human intelligence relies on inner monologue to process complex information through simultaneous reflection, memory retrieval, and response formulation. We introduce MIRROR (Modular Internal Reasoning, Reflection, Orchestration, and Response), a cognitive architecture that systematically implements these parallel reasoning capabilities in large language models. MIRROR operates as a unified system with two distinct functional layers: the Thinker and the Talker. The Thinker encompasses: (1) the Inner Monologue Manager, coordinating reasoning threads across cognitive dimensions (Goals, Reasoning, and Memory); and (2) the Cognitive Controller, synthesizing these threads into a coherent internal narrative maintained across conversation turns. The Talker component then leverages this integrated narrative for context-aware responses. Evaluated on the CuRaTe benchmark--testing personalized dialogue with safety-critical constraints, conflicting preferences, and multi-turn consistency--LLMs utilizing the MIRROR architecture achieve up to 156% relative improvement in critical safety scenarios involving three persons with conflicting preferences, maintaining an average accuracy of ~>80% on all scenarios. Across scenario-specific comparisons, GPT-4o, Gemini 1.5 Pro, Claude 3.7 Sonnet, Llama 4 variants, and Mistral 3 variants with the MIRROR architecture outperformed baseline models by 21% on average (15.5 percentage points absolute). MIRROR directly addresses three critical LLM failure modes: sycophancy, attentional deficits to critical information, and inconsistent prioritization of conflicting constraints. This work bridges cognitive science and AI by implementing modular internal reasoning inspired by human cognition, creating a persistent internal model that significantly enhances multi-turn conversation capabilities.