pwc-archive/papers-with-abstracts huggingface dataset:
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning
Authors: Junru Zhang, Lang Feng, Xu Guo, Yuhan Wu, Yabo Dong, Duanqing Xu
Date: 2025-06-16 00:00:00
Tasks: Reinforcement Learning (RL), Time Series, Time Series Classification

Time-series reasoning remains a significant challenge in multimodal large language models (MLLMs) due to the dynamic temporal patterns, ambiguous semantics, and lack of temporal priors. In this work, we introduce TimeMaster, a reinforcement learning (RL)-based method that enables time-series MLLMs to perform structured, interpretable reasoning directly over visualized time-series inputs and task prompts. TimeMaster adopts a three-part structured output format, reasoning, classification, and domain-specific extension, and is optimized via a composite reward function that aligns format adherence, prediction accuracy, and open-ended insight quality. The model is trained using a two-stage pipeline: we first apply supervised fine-tuning (SFT) to establish a good initialization, followed by Group Relative Policy Optimization (GRPO) at the token level to enable stable and targeted reward-driven improvement in time-series reasoning. We evaluate TimeMaster on the TimerBed benchmark across six real-world classification tasks based on Qwen2.5-VL-3B-Instruct. TimeMaster achieves state-of-the-art performance, outperforming both classical time-series models and few-shot GPT-4o by over 14.6% and 7.3% performance gain, respectively. Notably, TimeMaster goes beyond time-series classification: it also exhibits expert-like reasoning behavior, generates context-aware explanations, and delivers domain-aligned insights. Our results highlight that reward-driven RL can be a scalable and promising path toward integrating temporal understanding into time-series MLLMs.
DualEdit: Dual Editing for Knowledge Updating in Vision-Language Models
Authors: Zhiyi Shi, Binjie Wang, Chongjie Si, Yichen Wu, Junsik Kim, Hanspeter Pfister
Date: 2025-06-16 00:00:00
Tasks: Model Editing

Model editing aims to efficiently update a pre-trained model's knowledge without the need for time-consuming full retraining. While existing pioneering editing methods achieve promising results, they primarily focus on editing single-modal language models (LLMs). However, for vision-language models (VLMs), which involve multiple modalities, the role and impact of each modality on editing performance remain largely unexplored. To address this gap, we explore the impact of textual and visual modalities on model editing and find that: (1) textual and visual representations reach peak sensitivity at different layers, reflecting their varying importance; and (2) editing both modalities can efficiently update knowledge, but this comes at the cost of compromising the model's original capabilities. Based on our findings, we propose DualEdit, an editor that modifies both textual and visual modalities at their respective key layers. Additionally, we introduce a gating module within the more sensitive textual modality, allowing DualEdit to efficiently update new knowledge while preserving the model's original information. We evaluate DualEdit across multiple VLM backbones and benchmark datasets, demonstrating its superiority over state-of-the-art VLM editing baselines as well as adapted LLM editing methods on different evaluation metrics.
EUNIS Habitat Maps: Enhancing Thematic and Spatial Resolution for Europe through Machine Learning
Authors: Sara Si-Moussi, Stephan Hennekens, Sander Mücher, Wanda De Keersmaecker, Milan Chytrý, Emiliano Agrillo, Fabio Attorre, Idoia Biurrun, Gianmaria Bonari, Andraž Čarni, Renata Ćušterevska, Tetiana Dziuba, Klaus Ecker, Behlül Güler, Ute Jandt, Borja Jiménez-Alfaro, Jonathan Lenoir, Jens-Christian Svenning, Grzegorz Swacha, Wilfried Thuiller
Date: 2025-06-16 00:00:00

The EUNIS habitat classification is crucial for categorising European habitats, supporting European policy on nature conservation and implementing the Nature Restoration Law. To meet the growing demand for detailed and accurate habitat information, we provide spatial predictions for 260 EUNIS habitat types at hierarchical level 3, together with independent validation and uncertainty analyses. Using ensemble machine learning models, together with high-resolution satellite imagery and ecologically meaningful climatic, topographic and edaphic variables, we produced a European habitat map indicating the most probable EUNIS habitat at 100-m resolution across Europe. Additionally, we provide information on prediction uncertainty and the most probable habitats at level 3 within each EUNIS level 1 formation. This product is particularly useful for both conservation and restoration purposes. Predictions were cross-validated at European scale using a spatial block cross-validation and evaluated against independent data from France (forests only), the Netherlands and Austria. The habitat maps obtained strong predictive performances on the validation datasets with distinct trade-offs in terms of recall and precision across habitat formations.
Stimulus Motion Perception Studies Imply Specific Neural Computations in Human Visual Stabilization
Authors: David W Arathorn, Josephine C. D'Angelo, Austin Roorda
Date: 2025-06-16 00:00:00

Even during fixation the human eye is constantly in low amplitude motion, jittering over small angles in random directions at up to 100Hz. This motion results in all features of the image on the retina constantly traversing a number of cones, yet objects which are stable in the world are perceived to be stable, and any object which is moving in the world is perceived to be moving. A series of experiments carried out over a dozen years revealed the psychophysics of visual stabilization to be more nuanced than might be assumed, say, from the mechanics of stabilization of camera images, or what might be assumed to be the simplest solution from an evolutionary perspective. The psychophysics revealed by the experiments strongly implies a specific set of operations on retinal signals resulting in the observed stabilization behavior. The presentation is in two levels. First is a functional description of the action of the mechanism that is very likely responsible for the experimentally observed behavior. Second is a more speculative proposal of circuit-level neural elements that might implement the functional behavior.
MambaMia: A State-Space-Model-Based Compression for Efficient Video Understanding in Large Multimodal Models
Authors: Geewook Kim, Minjoon Seo
Date: 2025-06-16 00:00:00
Tasks: Video Understanding

We propose an efficient framework to compress multiple video-frame features before feeding them into large multimodal models, thereby mitigating the severe token explosion arising from long or dense videos. Our design leverages a bidirectional state-space-based block equipped with a gated skip connection and a learnable weighted-average pooling mechanism applied to periodically inserted learned queries. This structure enables hierarchical downsampling across both spatial and temporal dimensions, preserving performance in a cost-effective manner. Across challenging long and dense video understanding tasks, our approach demonstrates competitive results against state-of-the-art models, while significantly reducing overall token budget. Notably, replacing our proposed state-space block with a conventional Transformer results in substantial performance degradation, highlighting the advantages of state-space modeling for effectively compressing multi-frame video data. Our framework emphasizes resource-conscious efficiency, making it practical for real-world deployments. We validate its scalability and generality across multiple benchmarks, achieving the dual objectives of efficient resource usage and comprehensive video understanding.
Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Authors: Chia-Heng Yu, Yen-Lung Tsai
Date: 2025-06-16 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation, Text Retrieval

Traditional Retrieval-Augmented Generation (RAG) systems employ brute-force inner product search to retrieve the top-k most similar documents, then combined with the user query and passed to a language model. This allows the model to access external knowledge and reduce hallucinations. However, selecting an appropriate k value remains a significant challenge in practical applications: a small k may fail to retrieve sufficient information, while a large k can introduce excessive and irrelevant content. To address this, we propose a hierarchical clustering-based retrieval method that eliminates the need to predefine k. Our approach maintains the accuracy and relevance of system responses while adaptively selecting semantically relevant content. In the experiment stage, we applied our method to a Taiwanese legal dataset with expert-graded queries. The results show that our approach achieves superior performance in expert evaluations and maintains high precision while eliminating the need to predefine k, demonstrating improved accuracy and interpretability in legal text retrieval tasks. Our framework is simple to implement and easily integrates with existing RAG pipelines, making it a practical solution for real-world applications under limited resources.
AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video Understanding
Authors: Zhucun Xue, Jiangning Zhang, Xurong Xie, Yuxuan Cai, Yong liu, Xiangtai Li, DaCheng Tao
Date: 2025-06-16 00:00:00
Tasks: Optical Character Recognition (OCR), RAG, Retrieval, Retrieval-augmented Generation, Video Understanding

Multimodal Large Language Models (MLLMs) struggle with long videos due to fixed context windows and weak long-term dependency modeling. Existing Retrieval-Augmented Generation (RAG) methods for videos use static retrieval strategies, leading to inefficiencies for simple queries and information loss for complex tasks. To address this, we propose AdaVideoRAG, a novel framework that dynamically adapts retrieval granularity based on query complexity using a lightweight intent classifier. Our framework employs an Omni-Knowledge Indexing module to build hierarchical databases from text (captions, ASR, OCR), visual features, and semantic graphs, enabling optimal resource allocation across tasks. We also introduce the HiVU benchmark for comprehensive evaluation. Experiments demonstrate improved efficiency and accuracy for long-video understanding, with seamless integration into existing MLLMs. AdaVideoRAG establishes a new paradigm for adaptive retrieval in video analysis. Codes will be open-sourced at https://github.com/xzc-zju/AdaVideoRAG.
Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings
Authors: David Dukić, Ana Barić, Marko Čuljak, Josip Jukić, Martin Tutek
Date: 2025-06-16 00:00:00
Tasks: Articles, Diachronic Word Embeddings, Sentiment Analysis, Word Embeddings

Measuring how semantics of words change over time improves our understanding of how cultures and perspectives change. Diachronic word embeddings help us quantify this shift, although previous studies leveraged substantial temporally annotated corpora. In this work, we use a corpus of 9.5 million Croatian news articles spanning the past 25 years and quantify semantic change using skip-gram word embeddings trained on five-year periods. Our analysis finds that word embeddings capture linguistic shifts of terms pertaining to major topics in this timespan (COVID-19, Croatia joining the European Union, technological advancements). We also find evidence that embeddings from post-2020 encode increased positivity in sentiment analysis tasks, contrasting studies reporting a decline in mental health over the same period.
Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrieval
Authors: Kshitij Kavimandan, Angelos Nalmpantis, Emma Beauxis-Aussalet, Robert-Jan Sips
Date: 2025-06-16 00:00:00
Tasks: Contrastive Learning, Domain Adaptation, Image Retrieval, Information Retrieval, Retrieval

Patent images are technical drawings that convey information about a patent's innovation. Patent image retrieval systems aim to search in vast collections and retrieve the most relevant images. Despite recent advances in information retrieval, patent images still pose significant challenges due to their technical intricacies and complex semantic information, requiring efficient fine-tuning for domain adaptation. Current methods neglect patents' hierarchical relationships, such as those defined by the Locarno International Classification (LIC) system, which groups broad categories (e.g., "furnishing") into subclasses (e.g., "seats" and "beds") and further into specific patent designs. In this work, we introduce a hierarchical multi-positive contrastive loss that leverages the LIC's taxonomy to induce such relations in the retrieval process. Our approach assigns multiple positive pairs to each patent image within a batch, with varying similarity scores based on the hierarchical taxonomy. Our experimental analysis with various vision and multimodal models on the DeepPatent2 dataset shows that the proposed method enhances the retrieval results. Notably, our method is effective with low-parameter models, which require fewer computational resources and can be deployed on environments with limited hardware.
Enhancing Omics Cohort Discovery for Research on Neurodegeneration through Ontology-Augmented Embedding Models
Authors: José A. Pardo, Alicia Gómez-Pascual, José T. Palma, Juan A. Botía
Date: 2025-06-16 00:00:00
Tasks: Question Answering

The growing volume of omics and clinical data generated for neurodegenerative diseases (NDs) requires new approaches for their curation so they can be ready-to-use in bioinformatics. NeuroEmbed is an approach for the engineering of semantically accurate embedding spaces to represent cohorts and samples. The NeuroEmbed method comprises four stages: (1) extraction of ND cohorts from public repositories; (2) semi-automated normalization and augmentation of metadata of cohorts and samples using biomedical ontologies and clustering on the embedding space; (3) automated generation of a natural language question-answering (QA) dataset for cohorts and samples based on randomized combinations of standardized metadata dimensions and (4) fine-tuning of a domain-specific embedder to optimize queries. We illustrate the approach using the GEO repository and the PubMedBERT pretrained embedder. Applying NeuroEmbed, we semantically indexed 2,801 repositories and 150,924 samples. Amongst many biology-relevant categories, we normalized more than 1,700 heterogeneous tissue labels from GEO into 326 unique ontology-aligned concepts and enriched annotations with new ontology-aligned terms, leading to a fold increase in size for the metadata terms between 2.7 and 20 fold. After fine-tuning PubMedBERT with the QA training data augmented with the enlarged metadata, the model increased its mean Retrieval Precision from 0.277 to 0.866 and its mean Percentile Rank from 0.355 to 0.896. The NeuroEmbed methodology for the creation of electronic catalogues of omics cohorts and samples will foster automated bioinformatic pipelines construction. The NeuroEmbed catalogue of cohorts and samples is available at https://github.com/JoseAdrian3/NeuroEmbed.
RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning
Authors: Yueru Luo, Changqing Zhou, Yiming Yang, Erlong Li, Chao Zheng, Shuqi Mei, Shuguang Cui, Zhen Li
Date: 2025-06-16 00:00:00
Tasks: Autonomous Driving, Contrastive Learning, Lane Detection

Accurate road topology reasoning is critical for autonomous driving, enabling effective navigation and adherence to traffic regulations. Central to this task are lane perception and topology reasoning. However, existing methods typically focus on either lane detection or Lane-to-Lane (L2L) topology reasoning, often \textit{neglecting} Lane-to-Traffic-element (L2T) relationships or \textit{failing} to optimize these tasks jointly. Furthermore, most approaches either overlook relational modeling or apply it in a limited scope, despite the inherent spatial relationships among road elements. We argue that relational modeling is beneficial for both perception and reasoning, as humans naturally leverage contextual relationships for road element recognition and their connectivity inference. To this end, we introduce relational modeling into both perception and reasoning, \textit{jointly} enhancing structural understanding. Specifically, we propose: 1) a relation-aware lane detector, where our geometry-biased self-attention and \curve\ cross-attention refine lane representations by capturing relational dependencies; 2) relation-enhanced topology heads, including a geometry-enhanced L2L head and a cross-view L2T head, boosting reasoning with relational cues; and 3) a contrastive learning strategy with InfoNCE loss to regularize relationship embeddings. Extensive experiments on OpenLane-V2 demonstrate that our approach significantly improves both detection and topology reasoning metrics, achieving +3.1 in DET$_l$, +5.3 in TOP$_{ll}$, +4.9 in TOP$_{lt}$, and an overall +4.4 in OLS, setting a new state-of-the-art. Code will be released.
Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value
Authors: Yixian Xu, Shengjie Luo, LiWei Wang, Di He, Chang Liu
Date: 2025-06-16 00:00:00

Diffusion models have achieved remarkable success in generative modeling. Despite more stable training, the loss of diffusion models is not indicative of absolute data-fitting quality, since its optimal value is typically not zero but unknown, leading to confusion between large optimal loss and insufficient model capacity. In this work, we advocate the need to estimate the optimal loss value for diagnosing and improving diffusion models. We first derive the optimal loss in closed form under a unified formulation of diffusion models, and develop effective estimators for it, including a stochastic variant scalable to large datasets with proper control of variance and bias. With this tool, we unlock the inherent metric for diagnosing the training quality of mainstream diffusion model variants, and develop a more performant training schedule based on the optimal loss. Moreover, using models with 120M to 1.5B parameters, we find that the power law is better demonstrated after subtracting the optimal loss from the actual training loss, suggesting a more principled setting for investigating the scaling law for diffusion models.
Discrete Diffusion in Large Language and Multimodal Models: A Survey
Authors: Runpeng Yu, Qi Li, Xinchao Wang
Date: 2025-06-16 00:00:00
Tasks: Denoising

In this work, we provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs). Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy. This paradigm naturally enables parallel generation, fine-grained output controllability, and dynamic, response-aware perception. These capabilities are previously difficult to achieve with AR models. Recently, a growing number of industrial-scale proprietary d(M)LLMs, as well as a large number of open-source academic d(M)LLMs, have demonstrated performance comparable to their autoregressive counterparts, while achieving up to 10x acceleration in inference speed. The advancement of discrete diffusion LLMs and MLLMs has been largely driven by progress in two domains. The first is the development of autoregressive LLMs and MLLMs, which has accumulated vast amounts of data, benchmarks, and foundational infrastructure for training and inference. The second contributing domain is the evolution of the mathematical models underlying discrete diffusion. Together, these advancements have catalyzed a surge in dLLMs and dMLLMs research in early 2025. In this work, we present a comprehensive overview of the research in the dLLM and dMLLM domains. We trace the historical development of dLLMs and dMLLMs, formalize the underlying mathematical frameworks, and categorize representative models. We further analyze key techniques for training and inference, and summarize emerging applications across language, vision-language, and biological domains. We conclude by discussing future directions for research and deployment. Paper collection: https://github.com/LiQiiiii/DLLM-Survey
AI reconstruction of European weather from the Euro-Atlantic regimes
Authors: A. Camilletti, G. Franch, E. Tomasi, M. Cristoforetti
Date: 2025-06-16 00:00:00

We present a non-linear AI-model designed to reconstruct monthly mean anomalies of the European temperature and precipitation based on the Euro-Atlantic Weather regimes (WR) indices. WR represent recurrent, quasi-stationary, and persistent states of the atmospheric circulation that exert considerable influence over the European weather, therefore offering an opportunity for sub-seasonal to seasonal forecasting. While much research has focused on studying the correlation and impacts of the WR on European weather, the estimation of ground-level climate variables, such as temperature and precipitation, from Euro-Atlantic WR remains largely unexplored and is currently limited to linear methods. The presented AI model can capture and introduce complex non-linearities in the relation between the WR indices, describing the state of the Euro-Atlantic atmospheric circulation and the corresponding surface temperature and precipitation anomalies in Europe. We discuss the AI-model performance in reconstructing the monthly mean two-meter temperature and total precipitation anomalies in the European winter and summer, also varying the number of WR used to describe the monthly atmospheric circulation. We assess the impact of errors on the WR indices in the reconstruction and show that a mean absolute relative error below 80% yields improved seasonal reconstruction compared to the ECMWF operational seasonal forecast system, SEAS5. As a demonstration of practical applicability, we evaluate the model using WR indices predicted by SEAS5, finding slightly better or comparable skill relative to the SEAS5 forecast itself. Our findings demonstrate that WR-based anomaly reconstruction, powered by AI tools, offers a promising pathway for sub-seasonal and seasonal forecasting.