pwc-archive/papers-with-abstracts huggingface dataset:
Value Profiles for Encoding Human Variation
Authors: Taylor Sorensen, Pushkar Mishra, Roma Patel, Michael Henry Tessler, Michiel Bakker, Georgina Evans, Iason Gabriel, Noah Goodman, Verena Rieser
Date: 2025-03-19 00:00:00
Tasks: Decoder

Modelling human variation in rating tasks is crucial for enabling AI systems for personalization, pluralistic model alignment, and computational social science. We propose representing individuals using value profiles -- natural language descriptions of underlying values compressed from in-context demonstrations -- along with a steerable decoder model to estimate ratings conditioned on a value profile or other rater information. To measure the predictive information in rater representations, we introduce an information-theoretic methodology. We find that demonstrations contain the most information, followed by value profiles and then demographics. However, value profiles offer advantages in terms of scrutability, interpretability, and steerability due to their compressed natural language format. Value profiles effectively compress the useful information from demonstrations (>70% information preservation). Furthermore, clustering value profiles to identify similarly behaving individuals better explains rater variation than the most predictive demographic groupings. Going beyond test set performance, we show that the decoder models interpretably change ratings according to semantic profile differences, are well-calibrated, and can help explain instance-level disagreement by simulating an annotator population. These results demonstrate that value profiles offer novel, predictive ways to describe individual variation beyond demographics or group information.
A Review on Large Language Models for Visual Analytics
Authors: Navya Sonal Agarwal, Sanjay Kumar Sonbhadra
Date: 2025-03-19 00:00:00
Tasks: Natural Language Understanding, Text Generation

This paper provides a comprehensive review of the integration of Large Language Models (LLMs) with visual analytics, addressing their foundational concepts, capabilities, and wide-ranging applications. It begins by outlining the theoretical underpinnings of visual analytics and the transformative potential of LLMs, specifically focusing on their roles in natural language understanding, natural language generation, dialogue systems, and text-to-media transformations. The review further investigates how the synergy between LLMs and visual analytics enhances data interpretation, visualization techniques, and interactive exploration capabilities. Key tools and platforms including LIDA, Chat2VIS, Julius AI, and Zoho Analytics, along with specialized multimodal models such as ChartLlama and CharXIV, are critically evaluated. The paper discusses their functionalities, strengths, and limitations in supporting data exploration, visualization enhancement, automated reporting, and insight extraction. The taxonomy of LLM tasks, ranging from natural language understanding (NLU), natural language generation (NLG), to dialogue systems and text-to-media transformations, is systematically explored. This review provides a SWOT analysis of integrating Large Language Models (LLMs) with visual analytics, highlighting strengths like accessibility and flexibility, weaknesses such as computational demands and biases, opportunities in multimodal integration and user collaboration, and threats including privacy concerns and skill degradation. It emphasizes addressing ethical considerations and methodological improvements for effective integration.
Evaluating ASR Confidence Scores for Automated Error Detection in User-Assisted Correction Interfaces
Authors: Korbinian Kuhn, Verena Kersken, Gottfried Zimmermann
Date: 2025-03-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Despite advances in Automatic Speech Recognition (ASR), transcription errors persist and require manual correction. Confidence scores, which indicate the certainty of ASR results, could assist users in identifying and correcting errors. This study evaluates the reliability of confidence scores for error detection through a comprehensive analysis of end-to-end ASR models and a user study with 36 participants. The results show that while confidence scores correlate with transcription accuracy, their error detection performance is limited. Classifiers frequently miss errors or generate many false positives, undermining their practical utility. Confidence-based error detection neither improved correction efficiency nor was perceived as helpful by participants. These findings highlight the limitations of confidence scores and the need for more sophisticated approaches to improve user interaction and explainability of ASR results.
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning
Authors: Jinsheng Yuan, Yun Tang, Weisi Guo
Date: 2025-03-19 00:00:00
Tasks: Federated Learning, Quantization, RAG

Mixed-precision computing, a widely applied technique in AI, offers a larger trade-off space between accuracy and efficiency. The recent purposed Mixed-Precision Over-the-Air Federated Learning (MP-OTA-FL) enables clients to operate at appropriate precision levels based on their heterogeneous hardware, taking advantages of the larger trade-off space while covering the quantization overheads in the mixed-precision modulation scheme for the OTA aggregation process. A key to further exploring the potential of the MP-OTA-FL framework is the optimization of client precision levels. The choice of precision level hinges on multifaceted factors including hardware capability, potential client contribution, and user satisfaction, among which factors can be difficult to define or quantify. In this paper, we propose a RAG-based User Profiling for precision planning framework that integrates retrieval-augmented LLMs and dynamic client profiling to optimize satisfaction and contributions. This includes a hybrid interface for gathering device/user insights and an RAG database storing historical quantization decisions with feedback. Experiments show that our method boosts satisfaction, energy savings, and global model accuracy in MP-OTA-FL systems.
ChatStitch: Visualizing Through Structures via Surround-View Unsupervised Deep Image Stitching with Collaborative LLM-Agents
Authors: Hao Liang, Zhipeng Dong, Kaixin Chen, Jiyuan Guo, Yufeng Yue, Yi Yang, Mengyin Fu
Date: 2025-03-19 00:00:00
Tasks: Autonomous Driving, Image Stitching, SSIM

Surround-view perception has garnered significant attention for its ability to enhance the perception capabilities of autonomous driving vehicles through the exchange of information with surrounding cameras. However, existing surround-view perception systems are limited by inefficiencies in unidirectional interaction pattern with human and distortions in overlapping regions exponentially propagating into non-overlapping areas. To address these challenges, this paper introduces ChatStitch, a surround-view human-machine co-perception system capable of unveiling obscured blind spot information through natural language commands integrated with external digital assets. To dismantle the unidirectional interaction bottleneck, ChatStitch implements a cognitively grounded closed-loop interaction multi-agent framework based on Large Language Models. To suppress distortion propagation across overlapping boundaries, ChatStitch proposes SV-UDIS, a surround-view unsupervised deep image stitching method under the non-global-overlapping condition. We conducted extensive experiments on the UDIS-D, MCOV-SLAM open datasets, and our real-world dataset. Specifically, our SV-UDIS method achieves state-of-the-art performance on the UDIS-D dataset for 3, 4, and 5 image stitching tasks, with PSNR improvements of 9\%, 17\%, and 21\%, and SSIM improvements of 8\%, 18\%, and 26\%, respectively.
Enhancing Code LLM Training with Programmer Attention
Authors: Yifan Zhang, Chen Huang, Zachary Karas, Dung Thuy Nguyen, Kevin Leach, Yu Huang
Date: 2025-03-19 00:00:00
Tasks: Code Summarization

Human attention provides valuable yet underexploited signals for code LLM training, offering a perspective beyond purely machine-driven attention. Despite the complexity and cost of collecting eye-tracking data, there has also been limited progress in systematically using these signals for code LLM training. To address both issues, we propose a cohesive pipeline spanning augmentation and reward-based fine-tuning. Specifically, we introduce (1) an eye-tracking path augmentation method to expand programmer attention datasets, (2) a pattern abstraction step that refines raw fixations into learnable attention motifs, and (3) a reward-guided strategy for integrating these insights directly into a CodeT5 supervised fine-tuning process. Our experiments yield +7.16 in CodeBLEU on the CodeXGlue benchmark for code summarization, underscoring how uniting human and machine attention can boost code intelligence. We hope this work encourages broader exploration of human-centric methods in next-generation AI4SE.
Envisioning an AI-Enhanced Mental Health Ecosystem
Authors: Kellie Yu Hui Sim, Kenny Tsu Wei Choo
Date: 2025-03-19 00:00:00

The rapid advancement of Large Language Models (LLMs), reasoning models, and agentic AI approaches coincides with a growing global mental health crisis, where increasing demand has not translated into adequate access to professional support, particularly for underserved populations. This presents a unique opportunity for AI to complement human-led interventions, offering scalable and context-aware support while preserving human connection in this sensitive domain. We explore various AI applications in peer support, self-help interventions, proactive monitoring, and data-driven insights, using a human-centred approach that ensures AI supports rather than replaces human interaction. However, AI deployment in mental health fields presents challenges such as ethical concerns, transparency, privacy risks, and risks of over-reliance. We propose a hybrid ecosystem where where AI assists but does not replace human providers, emphasising responsible deployment and evaluation. We also present some of our early work and findings in several of these AI applications. Finally, we outline future research directions for refining AI-enhanced interventions while adhering to ethical and culturally sensitive guidelines.
A low-PAPR Pilot Design and Optimization for OTFS Modulation
Authors: Davide Bergamasco, Federico Clazzer, Andrea Munari, Paolo Casari
Date: 2025-03-19 00:00:00

Orthogonal time frequency space (OTFS) modulation has been proposed recently as a new waveform in the context of doubly-selective multi-path channels. This article proposes a novel pilot design that improves OTFS spectral efficiency (SE) while reducing its peak-to-average power ratio (PAPR). Instead of adopting an embedded data-orthogonal pilot for channel estimation, our scheme relies on Chu sequences superimposed to data symbols. We optimize the construction by investigating the best energy split between pilot and data symbols. Two equalizers, and an iterative channel estimation and equalization procedure are considered. We present extensive numerical results of relevant performance metrics, including the normalized mean squared error of the estimator, bit error rate, PAPR and SE. Our results show that, while the embedded pilot scheme estimates the channel more accurately, our approach yields a better tradeoff by achieving much higher spectral efficiency and lower PAPR.
Synthesis of omnidirectional path loss model based on directional model and multi-elliptical geometry
Authors: Jaroslaw Wojtun, Cezary Ziolkowski, Jan M. Kelner, Tomas Mikulasek, Radek Zavorka, Jiri Blumenstein, Ales Prokes, Aniruddha Chandra, Niraj Narayan, Anirban Ghosh
Date: 2025-03-18 00:00:00
Tasks: model

Millimeter wave (mmWave) technology offers high throughput but has a limited radio range, necessitating the use of directional antennas or beamforming systems such as massive MIMO. Path loss (PL) models using narrow-beam antennas are known as directional models, while those using omnidirectional antennas are referred to as omnidirectional models. To standardize the analysis, omnidirectional PL models for mmWave ranges have been introduced, including TR 38.901 by 3GPP, which is based on measurements from directional antennas. However, synthesizing these measurements can be complex and time-consuming. This study proposes a numerical approach to derive an omnidirectional model from directional data using multi-elliptical geometry. We assessed the effectiveness of this method against existing PL models for mmWaves that are available in the literature.
A Logic of Uncertain Interpretation
Authors: Adam Bjorndahl
Date: 2025-03-15 00:00:00

We introduce a logical framework for reasoning about "uncertain interpretations" and investigate two key applications: a new semantics for implication capturing a kind of "meaning entailment", and a conservative notion of "evidentially supported" belief that takes the form of a Dempster-Shafer belief function.
Predicting Multi-Agent Specialization via Task Parallelizability
Authors: Elizabeth Mieczkowski, Ruaridh Mon-Williams, Neil Bramley, Christopher G. Lucas, Natalia Velez, Thomas L. Griffiths
Date: 2025-03-19 00:00:00
Tasks: Multi-agent Reinforcement Learning

Multi-agent systems often rely on specialized agents with distinct roles rather than general-purpose agents that perform the entire task independently. However, the conditions that govern the optimal degree of specialization remain poorly understood. In this work, we propose that specialist teams outperform generalist ones when environmental constraints limit task parallelizability -- the potential to execute task components concurrently. Drawing inspiration from distributed systems, we introduce a heuristic to predict the relative efficiency of generalist versus specialist teams by estimating the speed-up achieved when two agents perform a task in parallel rather than focus on complementary subtasks. We validate this heuristic through three multi-agent reinforcement learning (MARL) experiments in Overcooked-AI, demonstrating that key factors limiting task parallelizability influence specialization. We also observe that as the state space expands, agents tend to converge on specialist strategies, even when generalist ones are theoretically more efficient, highlighting potential biases in MARL training algorithms. Our findings provide a principled framework for interpreting specialization given the task and environment, and introduce a novel benchmark for evaluating whether MARL finds optimal strategies.
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation
Authors: Cheng Wang, Lingxin Kong, Massimiliano Tamborski, Stefano V. Albrecht
Date: 2025-03-19 00:00:00
Tasks: Autonomous Vehicles, Imitation Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Simulation-based testing has emerged as an essential tool for verifying and validating autonomous vehicles (AVs). However, contemporary methodologies, such as deterministic and imitation learning-based driver models, struggle to capture the variability of human-like driving behavior. Given these challenges, we propose HAD-Gen, a general framework for realistic traffic scenario generation that simulates diverse human-like driving behaviors. The framework first clusters the vehicle trajectory data into different driving styles according to safety features. It then employs maximum entropy inverse reinforcement learning on each of the clusters to learn the reward function corresponding to each driving style. Using these reward functions, the method integrates offline reinforcement learning pre-training and multi-agent reinforcement learning algorithms to obtain general and robust driving policies. Multi-perspective simulation results show that our proposed scenario generation framework can simulate diverse, human-like driving behaviors with strong generalization capability. The proposed framework achieves a 90.96% goal-reaching rate, an off-road rate of 2.08%, and a collision rate of 6.91% in the generalization test, outperforming prior approaches by over 20% in goal-reaching performance. The source code is released at https://github.com/RoboSafe-Lab/Sim4AD.
Generating Causal Explanations of Vehicular Agent Behavioural Interactions with Learnt Reward Profiles
Authors: Rhys Howard, Nick Hawes, Lars Kunze
Date: 2025-03-18 00:00:00
Tasks: Autonomous Vehicles

Transparency and explainability are important features that responsible autonomous vehicles should possess, particularly when interacting with humans, and causal reasoning offers a strong basis to provide these qualities. However, even if one assumes agents act to maximise some concept of reward, it is difficult to make accurate causal inferences of agent planning without capturing what is of importance to the agent. Thus our work aims to learn a weighting of reward metrics for agents such that explanations for agent interactions can be causally inferred. We validate our approach quantitatively and qualitatively across three real-world driving datasets, demonstrating a functional improvement over previous methods and competitive performance across evaluation metrics.
A Generalist Hanabi Agent
Authors: Arjun V Sudhakar, Hadi Nekoei, Mathieu Reymond, Miao Liu, Janarthanan Rajendran, Sarath Chandar
Date: 2025-03-17 00:00:00
Tasks: Multi-agent Reinforcement Learning

Traditional multi-agent reinforcement learning (MARL) systems can develop cooperative strategies through repeated interactions. However, these systems are unable to perform well on any other setting than the one they have been trained on, and struggle to successfully cooperate with unfamiliar collaborators. This is particularly visible in the Hanabi benchmark, a popular 2-to-5 player cooperative card-game which requires complex reasoning and precise assistance to other agents. Current MARL agents for Hanabi can only learn one specific game-setting (e.g., 2-player games), and play with the same algorithmic agents. This is in stark contrast to humans, who can quickly adjust their strategies to work with unfamiliar partners or situations. In this paper, we introduce Recurrent Replay Relevance Distributed DQN (R3D2), a generalist agent for Hanabi, designed to overcome these limitations. We reformulate the task using text, as language has been shown to improve transfer. We then propose a distributed MARL algorithm that copes with the resulting dynamic observation- and action-space. In doing so, our agent is the first that can play all game settings concurrently, and extend strategies learned from one setting to other ones. As a consequence, our agent also demonstrates the ability to collaborate with different algorithmic agents -- agents that are themselves unable to do so. The implementation code is available at: $\href{https://github.com/chandar-lab/R3D2-A-Generalist-Hanabi-Agent}{R3D2-A-Generalist-Hanabi-Agent}$
MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance
Authors: Quanhao Li, Zhen Xing, Rui Wang, HUI ZHANG, Qi Dai, Zuxuan Wu
Date: 2025-03-20 00:00:00
Tasks: Image to Video Generation, Object, Video Generation

Recent advances in video generation have led to remarkable improvements in visual quality and temporal coherence. Upon this, trajectory-controllable video generation has emerged to enable precise object motion control through explicitly defined spatial paths. However, existing methods struggle with complex object movements and multi-object motion control, resulting in imprecise trajectory adherence, poor object consistency, and compromised visual quality. Furthermore, these methods only support trajectory control in a single format, limiting their applicability in diverse scenarios. Additionally, there is no publicly available dataset or benchmark specifically tailored for trajectory-controllable video generation, hindering robust training and systematic evaluation. To address these challenges, we introduce MagicMotion, a novel image-to-video generation framework that enables trajectory control through three levels of conditions from dense to sparse: masks, bounding boxes, and sparse boxes. Given an input image and trajectories, MagicMotion seamlessly animates objects along defined trajectories while maintaining object consistency and visual quality. Furthermore, we present MagicData, a large-scale trajectory-controlled video dataset, along with an automated pipeline for annotation and filtering. We also introduce MagicBench, a comprehensive benchmark that assesses both video quality and trajectory control accuracy across different numbers of objects. Extensive experiments demonstrate that MagicMotion outperforms previous methods across various metrics. Our project page are publicly available at https://quanhaol.github.io/magicmotion-site.
Do image and video quality metrics model low-level human vision?
Authors: Dounia Hammou, Yancheng Cai, Pavan Madhusudanarao, Christos G. Bampis, Rafał K. Mantiuk
Date: 2025-03-20 00:00:00
Tasks: MS-SSIM, SSIM

Image and video quality metrics, such as SSIM, LPIPS, and VMAF, are aimed to predict the perceived quality of the evaluated content and are often claimed to be "perceptual". Yet, few metrics directly model human visual perception, and most rely on hand-crafted formulas or training datasets to achieve alignment with perceptual data. In this paper, we propose a set of tests for full-reference quality metrics that examine their ability to model several aspects of low-level human vision: contrast sensitivity, contrast masking, and contrast matching. The tests are meant to provide additional scrutiny for newly proposed metrics. We use our tests to analyze 33 existing image and video quality metrics and find their strengths and weaknesses, such as the ability of LPIPS and MS-SSIM to predict contrast masking and poor performance of VMAF in this task. We further find that the popular SSIM metric overemphasizes differences in high spatial frequencies, but its multi-scale counterpart, MS-SSIM, addresses this shortcoming. Such findings cannot be easily made using existing evaluation protocols.
PromptMobile: Efficient Promptus for Low Bandwidth Mobile Video Streaming
Authors: LiMing Liu, Jiangkai Wu, Haoyang Wang, Peiheng Wang, Zongming Guo, Xinggong Zhang
Date: 2025-03-20 00:00:00
Tasks: Image Generation, Video Compression

Traditional video compression algorithms exhibit significant quality degradation at extremely low bitrates. Promptus emerges as a new paradigm for video streaming, substantially cutting down the bandwidth essential for video streaming. However, Promptus is computationally intensive and can not run in real-time on mobile devices. This paper presents PromptMobile, an efficient acceleration framework tailored for on-device Promptus. Specifically, we propose (1) a two-stage efficient generation framework to reduce computational cost by 8.1x, (2) a fine-grained inter-frame caching to reduce redundant computations by 16.6%, (3) system-level optimizations to further enhance efficiency. The evaluations demonstrate that compared with the original Promptus, PromptMobile achieves a 13.6x increase in image generation speed. Compared with other streaming methods, PromptMobile achives an average LPIPS improvement of 0.016 (compared with H.265), reducing 60% of severely distorted frames (compared to VQGAN).