pwc-archive/papers-with-abstracts huggingface dataset:
AI2-Active Safety: AI-enabled Interaction-aware Active Safety Analysis with Vehicle Dynamics
Authors: Keshu Wu, Zihao Li, Sixu Li, Xinyue Ye, Dominique Lord, Yang Zhou
Date: 2025-05-01 00:00:00

This paper introduces an AI-enabled, interaction-aware active safety analysis framework that accounts for groupwise vehicle interactions. Specifically, the framework employs a bicycle model-augmented with road gradient considerations-to accurately capture vehicle dynamics. In parallel, a hypergraph-based AI model is developed to predict probabilistic trajectories of ambient traffic. By integrating these two components, the framework derives vehicle intra-spacing over a 3D road surface as the solution of a stochastic ordinary differential equation, yielding high-fidelity surrogate safety measures such as time-to-collision (TTC). To demonstrate its effectiveness, the framework is analyzed using stochastic numerical methods comprising 4th-order Runge-Kutta integration and AI inference, generating probability-weighted high-fidelity TTC (HF-TTC) distributions that reflect complex multi-agent maneuvers and behavioral uncertainties. Evaluated with HF-TTC against traditional constant-velocity TTC and non-interaction-aware approaches on highway datasets, the proposed framework offers a systematic methodology for active safety analysis with enhanced potential for improving safety perception in complex traffic environments.
CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation
Authors: Sizhe Wang, Zhengren Wang, Dongsheng Ma, Yongan Yu, Rui Ling, Zhiyu Li, Feiyu Xiong, Wentao Zhang
Date: 2025-04-30 00:00:00
Tasks: Code Generation

Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes. We formalize this iterative, multi-turn paradigm as codeflow and introduce CodeFlowBench, the first benchmark designed to comprehensively evaluate LLMs' ability to perform codeflow, namely implementing new functionality by reusing existing functions over multiple turns. CodeFlowBench comprises 5,258 problems from Codeforces and is continuously updated via an automated pipeline, which decomposes each problem into subproblems with unit tests based on dependency tree analysis and dataflow analysis. We further propose a novel evaluation framework featured dual assessment protocol and structural metrics derived from dependency trees. Extensive experiments on 16 popular LLMs reveal significant performance degradation in multi-turn scenarios. For instance, o1-mini retains only 20.8% Pass@1 in multi-turn scenario versus 37.8% in single-turn scenario. More fine-grained analysis illustrates that model performance inversely correlates with dependency complexity. These findings not only highlight the critical challenges for supporting real-world workflows, but also establish CodeFlowBench as an essential tool for advancing code generation research.
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
Authors: Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng, Shuai Wang, Ming Tang, Yik-Chung Wu
Date: 2025-05-01 00:00:00
Tasks: Autonomous Driving, Federated Learning, Privacy Preserving

Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization due to domain-shift. Federated Learning (FL) has emerged as a promising paradigm for enhancing the generalization of AD models through privacy-preserving distributed learning. However, these FL AD models face significant temporal catastrophic forgetting when deployed in dynamically evolving environments, where continuous adaptation causes abrupt erosion of historical knowledge. This paper proposes Federated Exponential Moving Average (FedEMA), a novel framework that addresses this challenge through two integral innovations: (I) Server-side model's historical fitting capability preservation via fusing current FL round's aggregation model and a proposed previous FL round's exponential moving average (EMA) model; (II) Vehicle-side negative entropy regularization to prevent FL models' possible overfitting to EMA-introduced temporal patterns. Above two strategies empower FedEMA a dual-objective optimization that balances model generalization and adaptability. In addition, we conduct theoretical convergence analysis for the proposed FedEMA. Extensive experiments both on Cityscapes dataset and Camvid dataset demonstrate FedEMA's superiority over existing approaches, showing 7.12% higher mean Intersection-over-Union (mIoU).
Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data
Authors: Maxime Bouthors, Josep Crego, François Yvon
Date: 2025-04-30 00:00:00
Tasks: Machine Translation, Retrieval, Sentence, Translation

Conventional retrieval-augmented neural machine translation (RANMT) systems leverage bilingual corpora, e.g., translation memories (TMs). Yet, in many settings, in-domain monolingual target-side corpora are often available. This work explores ways to take advantage of such resources by retrieving relevant segments directly in the target language, based on a source-side query. For this, we design improved cross-lingual retrieval systems, trained with both sentence level and word-level matching objectives. In our experiments with two RANMT architectures, we first demonstrate the benefits of such cross-lingual objectives in a controlled setting, obtaining translation performances that surpass standard TM-based models. We then showcase our method on a real-world set-up, where the target monolingual resources far exceed the amount of parallel data and observe large improvements of our new techniques, which outperform both the baseline setting, and general-purpose cross-lingual retrievers.
LightEMMA: Lightweight End-to-End Multimodal Model for Autonomous Driving
Authors: Zhijie Qiao, Haowei Li, Zhong Cao, Henry X. Liu
Date: 2025-05-01 00:00:00
Tasks: Autonomous Driving

Vision-Language Models (VLMs) have demonstrated significant potential for end-to-end autonomous driving. However, fully exploiting their capabilities for safe and reliable vehicle control remains an open research challenge. To systematically examine advances and limitations of VLMs in driving tasks, we introduce LightEMMA, a Lightweight End-to-End Multimodal Model for Autonomous driving. LightEMMA provides a unified, VLM-based autonomous driving framework without ad hoc customizations, enabling easy integration and evaluation of evolving state-of-the-art commercial and open-source models. We construct twelve autonomous driving agents using various VLMs and evaluate their performance on the nuScenes prediction task, comprehensively assessing metrics such as inference time, computational cost, and predictive accuracy. Illustrative examples highlight that, despite their strong scenario interpretation capabilities, VLMs' practical performance in autonomous driving tasks remains concerning, emphasizing the need for further improvements. The code is available at https://github.com/michigan-traffic-lab/LightEMMA.
Investigating Literary Motifs in Ancient and Medieval Novels with Large Language Models
Authors: Emelie Hallenberg
Date: 2025-04-30 00:00:00

The Greek fictional narratives often termed love novels or romances, ranging from the first century CE to the middle of the 15th century, have long been considered as similar in many ways, not least in the use of particular literary motifs. By applying the use of fine-tuned large language models, this study aims to investigate which motifs exactly that the texts in this corpus have in common, and in which ways they differ from each other. The results show that while some motifs persist throughout the corpus, others fluctuate in frequency, indicating certain trends or external influences. Conclusively, the method proves to adequately extract literary motifs according to a set definition, providing data for both quantitative and qualitative analyses.
GSFeatLoc: Visual Localization Using Feature Correspondence on 3D Gaussian Splatting
Authors: Jongwon Lee, Timothy Bretl
Date: 2025-04-29 00:00:00
Tasks: 3DGS, NeRF, Translation, Visual Localization

In this paper, we present a method for localizing a query image with respect to a precomputed 3D Gaussian Splatting (3DGS) scene representation. First, the method uses 3DGS to render a synthetic RGBD image at some initial pose estimate. Second, it establishes 2D-2D correspondences between the query image and this synthetic image. Third, it uses the depth map to lift the 2D-2D correspondences to 2D-3D correspondences and solves a perspective-n-point (PnP) problem to produce a final pose estimate. Results from evaluation across three existing datasets with 38 scenes and over 2,700 test images show that our method significantly reduces both inference time (by over two orders of magnitude, from more than 10 seconds to as fast as 0.1 seconds) and estimation error compared to baseline methods that use photometric loss minimization. Results also show that our method tolerates large errors in the initial pose estimate of up to 55{\deg} in rotation and 1.1 units in translation (normalized by scene scale), achieving final pose errors of less than 5{\deg} in rotation and 0.05 units in translation on 90% of images from the Synthetic NeRF and Mip-NeRF360 datasets and on 42% of images from the more challenging Tanks and Temples dataset.
Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction
Authors: Ze Zhang, Georg Hess, Junjie Hu, Emmanuel Dean, Lennart Svensson, Knut Åkesson
Date: 2025-05-01 00:00:00
Tasks: Model Predictive Control, Motion Planning, motion prediction, Navigate, Prediction

This paper proposes an integrated approach for the safe and efficient control of mobile robots in dynamic and uncertain environments. The approach consists of two key steps: one-shot multimodal motion prediction to anticipate motions of dynamic obstacles and model predictive control to incorporate these predictions into the motion planning process. Motion prediction is driven by an energy-based neural network that generates high-resolution, multi-step predictions in a single operation. The prediction outcomes are further utilized to create geometric shapes formulated as mathematical constraints. Instead of treating each dynamic obstacle individually, predicted obstacles are grouped by proximity in an unsupervised way to improve performance and efficiency. The overall collision-free navigation is handled by model predictive control with a specific design for proactive dynamic obstacle avoidance. The proposed approach allows mobile robots to navigate effectively in dynamic environments. Its performance is accessed across various scenarios that represent typical warehouse settings. The results demonstrate that the proposed approach outperforms other existing dynamic obstacle avoidance methods.
LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics
Authors: Marc Glocker, Peter Hönig, Matthias Hirschmanner, Markus Vincze
Date: 2025-04-30 00:00:00
Tasks: In-Context Learning, Object, object-detection, Object Detection, Object Tracking, RAG, Robust Object Detection, Scene Understanding, Task Planning

We present an embodied robotic system with an LLM-driven agent-orchestration architecture for autonomous household object management. The system integrates memory-augmented task planning, enabling robots to execute high-level user commands while tracking past actions. It employs three specialized agents: a routing agent, a task planning agent, and a knowledge base agent, each powered by task-specific LLMs. By leveraging in-context learning, our system avoids the need for explicit model training. RAG enables the system to retrieve context from past interactions, enhancing long-term object tracking. A combination of Grounded SAM and LLaMa3.2-Vision provides robust object detection, facilitating semantic scene understanding for task planning. Evaluation across three household scenarios demonstrates high task planning accuracy and an improvement in memory recall due to RAG. Specifically, Qwen2.5 yields best performance for specialized agents, while LLaMA3.1 excels in routing tasks. The source code is available at: https://github.com/marc1198/chat-hsr.
Guidance and Control of Unmanned Surface Vehicles via HEOL
Authors: Loïck Degorre, Emmanuel Delaleau, Cédric Join, Michel Fliess
Date: 2025-04-30 00:00:00

This work presents a new approach to the guidance and control of marine craft via HEOL, i.e., a new way of combining flatness-based and model-free controllers. Its goal is to develop a general regulator for Unmanned Surface Vehicles (USV). To do so, the well-known USV maneuvering model is simplified into a nominal Hovercraft model which is flat. A flatness-based controller is derived for the simplified USV model and the loop is closed via an intelligent proportional-derivative (iPD) regulator. We thus associate the well-documented natural robustness of flatness-based control and adaptivity of iPDs. The controller is applied in simulation to two surface vessels, one meeting the simplifying hypotheses, the other one being a generic USV of the literature. It is shown to stabilize both systems even in the presence of unmodeled environmental disturbances.
Fact-Consistency Evaluation of Text-to-SQL Generation for Business Intelligence Using Exaone 3.5
Authors: Jeho Choi
Date: 2025-04-30 00:00:00
Tasks: Arithmetic Reasoning, Text to SQL, Text-To-SQL

Large Language Models (LLMs) have shown promise in enabling natural language interfaces for structured data querying through text-to-SQL generation. However, their application in real-world Business Intelligence (BI) contexts remains limited due to semantic hallucinations, structural errors, and a lack of domain-specific evaluation frameworks. In this study, we propose a Fact-Consistency Evaluation Framework for assessing the semantic accuracy of LLM-generated SQL outputs using Exaone 3.5--an instruction-tuned, bilingual LLM optimized for enterprise tasks. We construct a domain-specific benchmark comprising 219 natural language business questions across five SQL complexity levels, derived from actual sales data in LG Electronics' internal BigQuery environment. Each question is paired with a gold-standard SQL query and a validated ground-truth answer. We evaluate model performance using answer accuracy, execution success rate, semantic error rate, and non-response rate. Experimental results show that while Exaone 3.5 performs well on simple aggregation tasks (93% accuracy in L1), it exhibits substantial degradation in arithmetic reasoning (4% accuracy in H1) and grouped ranking tasks (31% in H4), with semantic errors and non-responses concentrated in complex cases. Qualitative error analysis further identifies common failure types such as misapplied arithmetic logic, incomplete filtering, and incorrect grouping operations. Our findings highlight the current limitations of LLMs in business-critical environments and underscore the need for fact-consistency validation layers and hybrid reasoning approaches. This work contributes a reproducible benchmark and evaluation methodology for advancing reliable natural language interfaces to structured enterprise data systems.
Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuning
Authors: Reem Abdel-Salam, Mary Adewunmi
Date: 2025-04-30 00:00:00
Tasks: Descriptive, POS

Health Mention Classification (HMC) plays a critical role in leveraging social media posts for real-time tracking and public health monitoring. Nevertheless, the process of HMC presents significant challenges due to its intricate nature, primarily stemming from the contextual aspects of health mentions, such as figurative language and descriptive terminology, rather than explicitly reflecting a personal ailment. To address this problem, we argue that clearer mentions can be achieved through conventional fine-tuning with enhanced parameters of biomedical natural language methods (NLP). In this study, we explore different techniques such as the utilisation of part-of-speech (POS) tagger information, improving on PEFT techniques, and different combinations thereof. Extensive experiments are conducted on three widely used datasets: RHDM, PHM, and Illness. The results incorporated POS tagger information, and leveraging PEFT techniques significantly improves performance in terms of F1-score compared to state-of-the-art methods across all three datasets by utilising smaller models and efficient training. Furthermore, the findings highlight the effectiveness of incorporating POS tagger information and leveraging PEFT techniques for HMC. In conclusion, the proposed methodology presents a potentially effective approach to accurately classifying health mentions in social media posts while optimising the model size and training efficiency.
Investigating the Effect of Parallel Data in the Cross-Lingual Transfer for Vision-Language Encoders
Authors: Andrei-Alexandru Manea, Jindřich Libovický
Date: 2025-04-30 00:00:00
Tasks: Cross-Lingual Transfer

Most pre-trained Vision-Language (VL) models and training data for the downstream tasks are only available in English. Therefore, multilingual VL tasks are solved using cross-lingual transfer: fine-tune a multilingual pre-trained model or transfer the text encoder using parallel data. We study the alternative approach: transferring an already trained encoder using parallel data. We investigate the effect of parallel data: domain and the number of languages, which were out of focus in previous work. Our results show that even machine-translated task data are the best on average, caption-like authentic parallel data outperformed it in some languages. Further, we show that most languages benefit from multilingual training.
20min-XD: A Comparable Corpus of Swiss News Articles
Authors: Michelle Wastl, Jannis Vamvas, Selena Calleri, Rico Sennrich
Date: 2025-04-30 00:00:00
Tasks: Articles, Semantic Similarity, Semantic Textual Similarity, Sentence

We present 20min-XD (20 Minuten cross-lingual document-level), a French-German, document-level comparable corpus of news articles, sourced from the Swiss online news outlet 20 Minuten/20 minutes. Our dataset comprises around 15,000 article pairs spanning 2015 to 2024, automatically aligned based on semantic similarity. We detail the data collection process and alignment methodology. Furthermore, we provide a qualitative and quantitative analysis of the corpus. The resulting dataset exhibits a broad spectrum of cross-lingual similarity, ranging from near-translations to loosely related articles, making it valuable for various NLP applications and broad linguistically motivated studies. We publicly release the dataset in document- and sentence-aligned versions and code for the described experiments.
REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining
Authors: Abu Mohammed Raisuddin, Jesper Holmblad, Hamed Haghighi, Yuri Poledna, Maikol Funk Drechsler, Valentina Donzella, Eren Erdal Aksoy
Date: 2025-04-30 00:00:00
Tasks: Autonomous Driving

Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, the interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In this study, we release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining. Distinct from the most relevant competitors, our dataset is unique in several respects. First, it is the largest point-wise annotated dataset, and second, it is the only one with high-resolution LiDAR data (LiDAR-256) enriched with 4D Radar point clouds logged in both daytime and nighttime conditions in a controlled weather environment. Furthermore, REHEARSE-3D involves rain-characteristic information, which is of significant value not only for sensor noise modeling but also for analyzing the impact of weather at a point level. Leveraging REHEARSE-3D, we benchmark raindrop detection and removal in fused LiDAR and 4D Radar point clouds. Our comprehensive study further evaluates the performance of various statistical and deep-learning models. Upon publication, the dataset and benchmark models will be made publicly available at: https://sporsho.github.io/REHEARSE3D.
Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization
Authors: Haotian Luo, Haiying He, Yibo Wang, Jinluan Yang, Rui Liu, Naiqiang Tan, Xiaochun Cao, DaCheng Tao, Li Shen
Date: 2025-04-30 00:00:00

Recently, long-thought reasoning models achieve strong performance on complex reasoning tasks, but often incur substantial inference overhead, making efficiency a critical concern. Our empirical analysis reveals that the benefit of using Long-CoT varies across problems: while some problems require elaborate reasoning, others show no improvement, or even degraded accuracy. This motivates adaptive reasoning strategies that tailor reasoning depth to the input. However, prior work primarily reduces redundancy within long reasoning paths, limiting exploration of more efficient strategies beyond the Long-CoT paradigm. To address this, we propose a novel two-stage framework for adaptive and efficient reasoning. First, we construct a hybrid reasoning model by merging long and short CoT models to enable diverse reasoning styles. Second, we apply bi-level preference training to guide the model to select suitable reasoning styles (group-level), and prefer concise and correct reasoning within each style group (instance-level). Experiments demonstrate that our method (Ada-R1) significantly reduces inference costs compared to other baseline approaches, while maintaining performance. Notably, on five mathematical datasets, the average length of reasoning is reduced by more than 50%, highlighting the potential of adaptive strategies to optimize reasoning efficiency in large language models. Our code is coming soon at https://github.com/StarDewXXX/AdaR1