pwc-archive/papers-with-abstracts huggingface dataset:
WonderPlay: Dynamic 3D Scene Generation from a Single Image and Actions
Authors: Zizhang Li, Hong-Xing Yu, Wei Liu, Yin Yang, Charles Herrmann, Gordon Wetzstein, Jiajun Wu
Date: 2025-05-23 00:00:00
Tasks: Sand, Scene Generation, Video Generation

WonderPlay is a novel framework integrating physics simulation with video generation for generating action-conditioned dynamic 3D scenes from a single image. While prior works are restricted to rigid body or simple elastic dynamics, WonderPlay features a hybrid generative simulator to synthesize a wide range of 3D dynamics. The hybrid generative simulator first uses a physics solver to simulate coarse 3D dynamics, which subsequently conditions a video generator to produce a video with finer, more realistic motion. The generated video is then used to update the simulated dynamic 3D scene, closing the loop between the physics solver and the video generator. This approach enables intuitive user control to be combined with the accurate dynamics of physics-based simulators and the expressivity of diffusion-based video generators. Experimental results demonstrate that WonderPlay enables users to interact with various scenes of diverse content, including cloth, sand, snow, liquid, smoke, elastic, and rigid bodies -- all using a single image input. Code will be made public. Project website: https://kyleleey.github.io/WonderPlay/
Multi-Person Interaction Generation from Two-Person Motion Priors
Authors: Wenning Xu, Shiyu Fan, Paul Henderson, Edmond S. L. Ho
Date: 2025-05-23 00:00:00
Tasks: Motion Generation

Generating realistic human motion with high-level controls is a crucial task for social understanding, robotics, and animation. With high-quality MOCAP data becoming more available recently, a wide range of data-driven approaches have been presented. However, modelling multi-person interactions still remains a less explored area. In this paper, we present Graph-driven Interaction Sampling, a method that can generate realistic and diverse multi-person interactions by leveraging existing two-person motion diffusion models as motion priors. Instead of training a new model specific to multi-person interaction synthesis, our key insight is to spatially and temporally separate complex multi-person interactions into a graph structure of two-person interactions, which we name the Pairwise Interaction Graph. We thus decompose the generation task into simultaneous single-person motion generation conditioned on one other's motion. In addition, to reduce artifacts such as interpenetrations of body parts in generated multi-person interactions, we introduce two graph-dependent guidance terms into the diffusion sampling scheme. Unlike previous work, our method can produce various high-quality multi-person interactions without having repetitive individual motions. Extensive experiments demonstrate that our approach consistently outperforms existing methods in reducing artifacts when generating a wide range of two-person and multi-person interactions.
From Flight to Insight: Semantic 3D Reconstruction for Aerial Inspection via Gaussian Splatting and Language-Guided Segmentation
Authors: Mahmoud Chick Zaouali, Todd Charter, Homayoun Najjaran
Date: 2025-05-23 00:00:00
Tasks: 3DGS, 3D Reconstruction, Neural Rendering, Scene Understanding

High-fidelity 3D reconstruction is critical for aerial inspection tasks such as infrastructure monitoring, structural assessment, and environmental surveying. While traditional photogrammetry techniques enable geometric modeling, they lack semantic interpretability, limiting their effectiveness for automated inspection workflows. Recent advances in neural rendering and 3D Gaussian Splatting (3DGS) offer efficient, photorealistic reconstructions but similarly lack scene-level understanding. In this work, we present a UAV-based pipeline that extends Feature-3DGS for language-guided 3D segmentation. We leverage LSeg-based feature fields with CLIP embeddings to generate heatmaps in response to language prompts. These are thresholded to produce rough segmentations, and the highest-scoring point is then used as a prompt to SAM or SAM2 for refined 2D segmentation on novel view renderings. Our results highlight the strengths and limitations of various feature field backbones (CLIP-LSeg, SAM, SAM2) in capturing meaningful structure in large-scale outdoor environments. We demonstrate that this hybrid approach enables flexible, language-driven interaction with photorealistic 3D reconstructions, opening new possibilities for semantic aerial inspection and scene understanding.
Emotion Knowledge Enhancement for Vision Large Language Models: A Self-Verification Approach for High-Quality Emotion Instruction Data Generation
Authors: Feifan Wang, Tengfei Song, Minggui He, Chang Su, Zhanglin Wu, Hao Yang, Wenming Zheng, Osamu Yoshie
Date: 2025-05-14 00:00:00
Tasks: Emotion Recognition, Large Language Model

Facial emotion perception in the vision large language model (VLLM) is crucial for achieving natural human-machine interaction. However, creating high-quality annotations for both coarse- and fine-grained facial emotion analysis demands costly expertise. The lack of such high-quality instruction data limits the performance of VLLMs in facial emotion perception. To address this, we propose a self-verification approach with emotion knowledge enhancement (SEKE), which generates high-quality instruction data for multi-grained emotion analysis cost-effectively using closed-source VLLM. This approach integrates prior human knowledge to VLLM inference, guided by the inherent correlations between three grained levels of emotion descriptions, i.e., discrete expression, valence-arousal, and action unit, to reliably generate comprehensive annotations. A self-verification strategy with Uncertainty-Aware Monte Carlo sampling (SV-UAMC) is further embedded to efficiently extract more accurate VLLM predictions, further improving annotation reliability. Consequently, we construct a facial emotion instruction dataset (FEID) containing three comprehensive descriptions, which provides coarse- and fine-grained emotional information for effective model training. Additionally, we introduce a facial emotion analysis benchmark (FEAB) to measure the VLLM's corresponding ability. Our method significantly outperforms state-of-the-art methods on three downstream facial emotion analysis tasks.
The Limits of Preference Data for Post-Training
Authors: Eric Zhao, Jessica Dai, Pranjal Awasthi
Date: 2025-05-26 00:00:00

Recent progress in strengthening the capabilities of large language models has stemmed from applying reinforcement learning to domains with automatically verifiable outcomes. A key question is whether we can similarly use RL to optimize for outcomes in domains where evaluating outcomes inherently requires human feedback; for example, in tasks like deep research and trip planning, outcome evaluation is qualitative and there are many possible degrees of success. One attractive and scalable modality for collecting human feedback is preference data: ordinal rankings (pairwise or $k$-wise) that indicate, for $k$ given outcomes, which one is preferred. In this work, we study a critical roadblock: preference data fundamentally and significantly limits outcome-based optimization. Even with idealized preference data (infinite, noiseless, and online), the use of ordinal feedback can prevent obtaining even approximately optimal solutions. We formalize this impossibility using voting theory, drawing an analogy between how a model chooses to answer a query with how voters choose a candidate to elect. This indicates that grounded human scoring and algorithmic innovations are necessary for extending the success of RL post-training to domains demanding human feedback. We also explore why these limitations have disproportionately impacted RLHF when it comes to eliciting reasoning behaviors (e.g., backtracking) versus situations where RLHF has been historically successful (e.g., instruction-tuning and safety training), finding that the limitations of preference data primarily suppress RLHF's ability to elicit robust strategies -- a class that encompasses most reasoning behaviors.
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications
Authors: Christoph R. Landolt, Christoph Würsch, Roland Meier, Alain Mermoud, Julian Jang-Jaccard
Date: 2025-05-26 00:00:00
Tasks: Adversarial Robustness, Intrusion Detection, Multi-agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) has shown great potential as an adaptive solution for addressing modern cybersecurity challenges. MARL enables decentralized, adaptive, and collaborative defense strategies and provides an automated mechanism to combat dynamic, coordinated, and sophisticated threats. This survey investigates the current state of research in MARL applications for automated cyber defense (ACD), focusing on intruder detection and lateral movement containment. Additionally, it examines the role of Autonomous Intelligent Cyber-defense Agents (AICA) and Cyber Gyms in training and validating MARL agents. Finally, the paper outlines existing challenges, such as scalability and adversarial robustness, and proposes future research directions. This also discusses how MARL integrates in AICA to provide adaptive, scalable, and dynamic solutions to counter the increasingly sophisticated landscape of cyber threats. It highlights the transformative potential of MARL in areas like intrusion detection and lateral movement containment, and underscores the value of Cyber Gyms for training and validation of AICA.
Eliciting Informed Preferences
Authors: Modibo K. Camara, Nicole Immorlica, Brendan Lucier
Date: 2025-05-26 00:00:00

If people find it costly to evaluate the options available to them, their choices may not directly reveal their preferences. Yet, it is conceivable that a researcher can still learn about a population's preferences with careful experiment design. We formalize the researcher's problem in a model of robust mechanism design where it is costly for individuals to learn about how much they value a product. We characterize the statistics that the researcher can identify, and find that they are quite restricted. Finally, we apply our positive results to social choice and propose a way to combat uninformed voting.
Continuous-Time Analysis of Heavy Ball Momentum in Min-Max Games
Authors: Yi Feng, Kaito Fujii, Stratis Skoulakis, Xiao Wang, Volkan Cevher
Date: 2025-05-26 00:00:00

Since Polyak's pioneering work, heavy ball (HB) momentum has been widely studied in minimization. However, its role in min-max games remains largely unexplored. As a key component of practical min-max algorithms like Adam, this gap limits their effectiveness. In this paper, we present a continuous-time analysis for HB with simultaneous and alternating update schemes in min-max games. Locally, we prove smaller momentum enhances algorithmic stability by enabling local convergence across a wider range of step sizes, with alternating updates generally converging faster. Globally, we study the implicit regularization of HB, and find smaller momentum guides algorithms trajectories towards shallower slope regions of the loss landscapes, with alternating updates amplifying this effect. Surprisingly, all these phenomena differ from those observed in minimization, where larger momentum yields similar effects. Our results reveal fundamental differences between HB in min-max games and minimization, and numerical experiments further validate our theoretical results.
Incentivizing High-Quality Human Annotations with Golden Questions
Authors: Shang Liu, Zhongze Cai, Hanzhao Wang, Zhongyao Ma, Xiaocheng Li
Date: 2025-05-25 00:00:00

Human-annotated data plays a vital role in training large language models (LLMs), such as supervised fine-tuning and human preference alignment. However, it is not guaranteed that paid human annotators produce high-quality data. In this paper, we study how to incentivize human annotators to do so. We start from a principal-agent model to model the dynamics between the company (the principal) and the annotator (the agent), where the principal can only monitor the annotation quality by examining $n$ samples. We investigate the maximum likelihood estimators (MLE) and the corresponding hypothesis testing to incentivize annotators: the agent is given a bonus if the MLE passes the test. By analyzing the variance of the outcome, we show that the strategic behavior of the agent makes the hypothesis testing very different from traditional ones: Unlike the exponential rate proved by the large deviation theory, the principal-agent model's hypothesis testing rate is of $\Theta(1/\sqrt{n \log n})$. Our theory implies two criteria for the \emph{golden questions} to monitor the performance of the annotators: they should be of (1) high certainty and (2) similar format to normal ones. In that light, we select a set of golden questions in human preference data. By doing incentive-compatible experiments, we find out that the annotators' behavior is better revealed by those golden questions, compared to traditional survey techniques such as instructed manipulation checks.
An AI Capability Threshold for Rent-Funded Universal Basic Income in an AI-Automated Economy
Authors: Aran Nayebi
Date: 2025-05-24 00:00:00

We derive the first closed-form condition under which artificial intelligence (AI) capital profits could sustainably finance a universal basic income (UBI) without additional taxes or new job creation. In a Solow-Zeira economy characterized by a continuum of automatable tasks, a constant net saving rate $s$, and task-elasticity $\sigma < 1$, we analyze how the AI capability threshold--defined as the productivity level of AI relative to pre-AI automation--varies under different economic scenarios. At present economic parameters, we find that AI systems must achieve only approximately 5-6 times existing automation productivity to finance an 11\%-of-GDP UBI, in the worst case situation where \emph{no} new jobs or tasks are created. Our analysis also reveals some specific policy levers: raising public revenue share (e.g. profit taxation) of AI capital from the current 15\% to about 33\% halves the required AI capability threshold to attain UBI to 3 times existing automotion productivity, but gains diminish beyond 50\% public revenue share, especially if regulatory costs increase. Market structure also strongly affects outcomes: monopolistic or concentrated oligopolistic markets reduce the threshold by increasing economic rents, whereas heightened competition significantly raises it. Overall, these results suggest a couple policy recommendations: maximizing public revenue share up to a point so that operating costs are minimized, and strategically managing market competition can ensure AI's growing capabilities translate into meaningful social benefits within realistic technological progress scenarios.
Efficient Algorithms for Electing Successive Committees
Authors: Pallavi Jain, Andrzej Kaczmarczyk
Date: 2025-05-23 00:00:00

In a recently introduced model of successive committee elections (Bredereck et al., AAAI-20) for a given set of ordinal or approval preferences one aims to find a sequence of a given length of "best" same-size committees such that each candidate is a member of a limited number of consecutive committees. However, the practical usability of this model remains limited, as the described task turns out to be NP-hard for most selection criteria already for seeking committees of size three. Non-trivial or somewhat efficient algorithms for these cases are lacking too. Motivated by a desire to unlock the full potential of the described temporal model of committee elections, we devise (parameterized) algorithms that effectively solve the mentioned hard cases in realistic scenarios of a moderate number of candidates or of a limited time horizon.
Explanation User Interfaces: A Systematic Literature Review
Authors: Eleonora Cappuccio, Andrea Esposito, Francesco Greco, Giuseppe Desolda, Rosa Lanzilotti, Salvatore Rinzivillo
Date: 2025-05-26 00:00:00
Tasks: Decision Making, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Systematic Literature Review

Artificial Intelligence (AI) is one of the major technological advancements of this century, bearing incredible potential for users through AI-powered applications and tools in numerous domains. Being often black-box (i.e., its decision-making process is unintelligible), developers typically resort to eXplainable Artificial Intelligence (XAI) techniques to interpret the behaviour of AI models to produce systems that are transparent, fair, reliable, and trustworthy. However, presenting explanations to the user is not trivial and is often left as a secondary aspect of the system's design process, leading to AI systems that are not useful to end-users. This paper presents a Systematic Literature Review on Explanation User Interfaces (XUIs) to gain a deeper understanding of the solutions and design guidelines employed in the academic literature to effectively present explanations to users. To improve the contribution and real-world impact of this survey, we also present a framework for Human-cEnteRed developMent of Explainable user interfaceS (HERMES) to guide practitioners and academics in the design and evaluation of XUIs.
On the Same Page: Dimensions of Perceived Shared Understanding in Human-AI Interaction
Authors: Qingyu Liang, Jaime Banks
Date: 2025-05-26 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Shared understanding plays a key role in the effective communication in and performance of human-human interactions. With the increasingly common integration of AI into human contexts, the future of personal and workplace interactions will likely see human-AI interaction (HAII) in which the perception of shared understanding is important. Existing literature has addressed the processes and effects of PSU in human-human interactions, but the construal remains underexplored in HAII. To better understand PSU in HAII, we conducted an online survey to collect user reflections on interactions with a large language model when it sunderstanding of a situation was thought to be similar to or different from the participant's. Through inductive thematic analysis, we identified eight dimensions comprising PSU in human-AI interactions: Fluency, aligned operation, fluidity, outcome satisfaction, contextual awareness, lack of humanlike abilities, computational limits, and suspicion.
The Many Challenges of Human-Like Agents in Virtual Game Environments
Authors: Maciej Swiechowski, Dominik Slezak
Date: 2025-05-26 00:00:00

Human-like agents are an increasingly important topic in games and beyond. Believable non-player characters enhance the gaming experience by improving immersion and providing entertainment. They also offer players the opportunity to engage with AI entities that can function as opponents, teachers, or cooperating partners. Additionally, in games where bots are prohibited -- and even more so in non-game environments -- there is a need for methods capable of identifying whether digital interactions occur with bots or humans. This leads to two fundamental research questions: (1) how to model and implement human-like AI, and (2) how to measure its degree of human likeness. This article offers two contributions. The first one is a survey of the most significant challenges in implementing human-like AI in games (or any virtual environment featuring simulated agents, although this article specifically focuses on games). Thirteen such challenges, both conceptual and technical, are discussed in detail. The second is an empirical study performed in a tactical video game that addresses the research question: "Is it possible to distinguish human players from bots (AI agents) based on empirical data?" A machine-learning approach using a custom deep recurrent convolutional neural network is presented. We hypothesize that the more challenging it is to create human-like AI for a given game, the easier it becomes to develop a method for distinguishing humans from AI-driven players.
ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows
Authors: Qiushi Sun, Zhoumianze Liu, Chang Ma, Zichen Ding, Fangzhi Xu, Zhangyue Yin, Haiteng Zhao, Zhenyu Wu, Kanzhi Cheng, Zhaoyang Liu, Jianing Wang, Qintong Li, Xiangru Tang, Tianbao Xie, Xiachong Feng, Xiang Li, Ben Kao, Wenhai Wang, Biqing Qi, Lingpeng Kong, Zhiyong Wu
Date: 2025-05-26 00:00:00
Tasks: Astronomy, scientific discovery

Large Language Models (LLMs) have extended their impact beyond Natural Language Processing, substantially fostering the development of interdisciplinary research. Recently, various LLM-based agents have been developed to assist scientific discovery progress across multiple aspects and domains. Among these, computer-using agents, capable of interacting with operating systems as humans do, are paving the way to automated scientific problem-solving and addressing routines in researchers' workflows. Recognizing the transformative potential of these agents, we introduce ScienceBoard, which encompasses two complementary contributions: (i) a realistic, multi-domain environment featuring dynamic and visually rich scientific workflows with integrated professional software, where agents can autonomously interact via different interfaces to accelerate complex research tasks and experiments; and (ii) a challenging benchmark of 169 high-quality, rigorously validated real-world tasks curated by humans, spanning scientific-discovery workflows in domains such as biochemistry, astronomy, and geoinformatics. Extensive evaluations of agents with state-of-the-art backbones (e.g., GPT-4o, Claude 3.7, UI-TARS) show that, despite some promising results, they still fall short of reliably assisting scientists in complex workflows, achieving only a 15% overall success rate. In-depth analysis further provides valuable insights for addressing current agent limitations and more effective design principles, paving the way to build more capable agents for scientific discovery. Our code, environment, and benchmark are at https://qiushisun.github.io/ScienceBoard-Home/.
Ocular Authentication: Fusion of Gaze and Periocular Modalities
Authors: Dillon Lohr, Michael J. Proulx, Mehedi Hasan Raju, Oleg V. Komogortsev
Date: 2025-05-22 00:00:00
Tasks: Gaze Estimation

This paper investigates the feasibility of fusing two eye-centric authentication modalities-eye movements and periocular images-within a calibration-free authentication system. While each modality has independently shown promise for user authentication, their combination within a unified gaze-estimation pipeline has not been thoroughly explored at scale. In this report, we propose a multimodal authentication system and evaluate it using a large-scale in-house dataset comprising 9202 subjects with an eye tracking (ET) signal quality equivalent to a consumer-facing virtual reality (VR) device. Our results show that the multimodal approach consistently outperforms both unimodal systems across all scenarios, surpassing the FIDO benchmark. The integration of a state-of-the-art machine learning architecture contributed significantly to the overall authentication performance at scale, driven by the model's ability to capture authentication representations and the complementary discriminative characteristics of the fused modalities.