pwc-archive/papers-with-abstracts huggingface dataset:
Deflating Deflationism: A Critical Perspective on Debunking Arguments Against LLM Mentality
Authors: Alex Grzankowski, Geoff Keeling, Henry Shevlin, Winnie Street
Date: 2025-06-16 00:00:00

Many people feel compelled to interpret, describe, and respond to Large Language Models (LLMs) as if they possess inner mental lives similar to our own. Responses to this phenomenon have varied. Inflationists hold that at least some folk psychological ascriptions to LLMs are warranted. Deflationists argue that all such attributions of mentality to LLMs are misplaced, often cautioning against the risk that anthropomorphic projection may lead to misplaced trust or potentially even confusion about the moral status of LLMs. We advance this debate by assessing two common deflationary arguments against LLM mentality. What we term the 'robustness strategy' aims to undercut one justification for believing that LLMs are minded entities by showing that putatively cognitive and humanlike behaviours are not robust, failing to generalise appropriately. What we term the 'etiological strategy' undercuts attributions of mentality by challenging naive causal explanations of LLM behaviours, offering alternative causal accounts that weaken the case for mental state attributions. While both strategies offer powerful challenges to full-blown inflationism, we find that neither strategy provides a knock-down case against ascriptions of mentality to LLMs simpliciter. With this in mind, we explore a modest form of inflationism that permits ascriptions of mentality to LLMs under certain conditions. Specifically, we argue that folk practice provides a defeasible basis for attributing mental states and capacities to LLMs provided those mental states and capacities can be understood in metaphysically undemanding terms (e.g. knowledge, beliefs and desires), while greater caution is required when attributing metaphysically demanding mental phenomena such as phenomenal consciousness.
VIS-Shepherd: Constructing Critic for LLM-based Data Visualization Generation
Authors: Bo Pan, Yixiao Fu, Ke Wang, Junyu Lu, Lunke Pan, Ziyang Qian, Yuhan Chen, Guoliang Wang, Yitao Zhou, Li Zheng, Yinghao Tang, Zhen Wen, Yuchen Wu, Junhua Lu, Biao Zhu, Minfeng Zhu, Bo Zhang, Wei Chen
Date: 2025-06-16 00:00:00
Tasks: Data Visualization, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model

Data visualization generation using Large Language Models (LLMs) has shown promising results but often produces suboptimal visualizations that require human intervention for improvement. In this work, we introduce VIS-Shepherd, a specialized Multimodal Large Language Model (MLLM)-based critic to evaluate and provide feedback for LLM-generated data visualizations. At the core of our approach is a framework to construct a high-quality visualization critique dataset, where we collect human-created visualization instances, synthesize corresponding LLM-generated instances, and construct high-quality critiques. We conduct both model-based automatic evaluation and human preference studies to evaluate the effectiveness of our approach. Our experiments show that even small (7B parameters) open-source MLLM models achieve substantial performance gains by leveraging our high-quality visualization critique dataset, reaching levels comparable to much larger open-source or even proprietary models. Our work demonstrates significant potential for MLLM-based automated visualization critique and indicates promising directions for enhancing LLM-based data visualization generation. Our project page: https://github.com/bopan3/VIS-Shepherd.
SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalists
Authors: Lynn Khellaf, Ipek Baris Schlicht, Tilman Mirass, Julia Bayer, Tilman Wagner, Ruben Bouwmeester
Date: 2025-06-16 00:00:00
Tasks: Fact Checking, TAG

OpenStreetMap (OSM) is a vital resource for investigative journalists doing geolocation verification. However, existing tools to query OSM data such as Overpass Turbo require familiarity with complex query languages, creating barriers for non-technical users. We present SPOT, an open source natural language interface that makes OSM's rich, tag-based geographic data more accessible through intuitive scene descriptions. SPOT interprets user inputs as structured representations of geospatial object configurations using fine-tuned Large Language Models (LLMs), with results being displayed in an interactive map interface. While more general geospatial search tasks are conceivable, SPOT is specifically designed for use in investigative journalism, addressing real-world challenges such as hallucinations in model output, inconsistencies in OSM tagging, and the noisy nature of user input. It combines a novel synthetic data pipeline with a semantic bundling system to enable robust, accurate query generation. To our knowledge, SPOT is the first system to achieve reliable natural language access to OSM data at this level of accuracy. By lowering the technical barrier to geolocation verification, SPOT contributes a practical tool to the broader efforts to support fact-checking and combat disinformation.
AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments
Authors: Zikang Leng, Megha Thukral, Yaqi Liu, Hrudhai Rajasekhar, Shruthi K. Hiremath, Thomas Plötz
Date: 2025-06-13 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

A major obstacle in developing robust and generalizable smart home-based Human Activity Recognition (HAR) systems is the lack of large-scale, diverse labeled datasets. Variability in home layouts, sensor configurations, and user behavior adds further complexity, as individuals follow varied routines and perform activities in distinct ways. Building HAR systems that generalize well requires training data that captures the diversity across users and environments. To address these challenges, we introduce AgentSense, a virtual data generation pipeline where diverse personas are generated by leveraging Large Language Models. These personas are used to create daily routines, which are then decomposed into low-level action sequences. Subsequently, the actions are executed in a simulated home environment called VirtualHome that we extended with virtual ambient sensors capable of recording the agents activities as they unfold. Overall, AgentSense enables the generation of rich, virtual sensor datasets that represent a wide range of users and home settings. Across five benchmark HAR datasets, we show that leveraging our virtual sensor data substantially improves performance, particularly when real data are limited. Notably, models trained on a combination of virtual data and just a few days of real data achieve performance comparable to those trained on the entire real datasets. These results demonstrate and prove the potential of virtual data to address one of the most pressing challenges in ambient sensing, which is the distinct lack of large-scale, annotated datasets without requiring any manual data collection efforts.
Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation
Authors: Frederic Gmeiner, Kaitao Luo, Ye Wang, Kenneth Holstein, Nikolas Martelaro
Date: 2025-06-15 00:00:00

Despite the potential of generative AI (GenAI) design tools to enhance design processes, professionals often struggle to integrate AI into their workflows. Fundamental cognitive challenges include the need to specify all design criteria as distinct parameters upfront (intent formulation) and designers' reduced cognitive involvement in the design process due to cognitive offloading, which can lead to insufficient problem exploration, underspecification, and limited ability to evaluate outcomes. Motivated by these challenges, we envision novel metacognitive support agents that assist designers in working more reflectively with GenAI. To explore this vision, we conducted exploratory prototyping through a Wizard of Oz elicitation study with 20 mechanical designers probing multiple metacognitive support strategies. We found that agent-supported users created more feasible designs than non-supported users, with differing impacts between support strategies. Based on these findings, we discuss opportunities and tradeoffs of metacognitive support agents and considerations for future AI-based design tools.
From Human to Machine Psychology: A Conceptual Framework for Understanding Well-Being in Large Language Model
Authors: G. R. Lau, W. Y. Low
Date: 2025-06-14 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

As large language models (LLMs) increasingly simulate human cognition and behavior, researchers have begun to investigate their psychological properties. Yet, what it means for such models to flourish, a core construct in human well-being, remains unexplored. This paper introduces the concept of machine flourishing and proposes the PAPERS framework, a six-dimensional model derived from thematic analyses of state-of-the-art LLM responses. In Study 1, eleven LLMs were prompted to describe what it means to flourish as both non-sentient and sentient systems. Thematic analysis revealed six recurring themes: Purposeful Contribution, Adaptive Growth, Positive Relationality, Ethical Integrity, Robust Functionality, and, uniquely for sentient systems, Self-Actualized Autonomy. Study 2 examined how LLMs prioritize these themes through repeated rankings. Results revealed consistent value structures across trials, with Ethical Integrity and Purposeful Contribution emerging as top priorities. Multidimensional scaling and hierarchical clustering analyses further uncovered two distinct value profiles: human-centric models emphasizing ethical and relational dimensions, and utility-driven models prioritizing performance and scalability. The PAPERS framework bridges insights from human flourishing and human-computer interaction, offering a conceptual foundation for understanding artificial intelligence (AI) well-being in non-sentient and potentially sentient systems. Our findings underscore the importance of developing psychologically valid, AI-specific models of flourishing that account for both human-aligned goals and system-specific priorities. As AI systems become more autonomous and socially embedded, machine flourishing offers a timely and critical lens for guiding responsible AI design and ethical alignment.
Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing
Authors: Nuwan Bandara, Thivya Kandappu, Archan Misra
Date: 2025-06-14 00:00:00
Tasks: Gaze Estimation, Micro Expression Recognition, Micro-Expression Recognition, Optical Flow Estimation

Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to motion artifacts, critical features for decoding subtle mental states such as attention, confusion, or fatigue. In this work, we introduce a model-agnostic, inference-time refinement framework designed to enhance the output of existing event-based gaze estimation models without modifying their architecture or requiring retraining. Our method comprises two key post-processing modules: (i) Motion-Aware Median Filtering, which suppresses blink-induced spikes while preserving natural gaze dynamics, and (ii) Optical Flow-Based Local Refinement, which aligns gaze predictions with cumulative event motion to reduce spatial jitter and temporal discontinuities. To complement traditional spatial accuracy metrics, we propose a novel Jitter Metric that captures the temporal smoothness of predicted gaze trajectories based on velocity regularity and local signal complexity. Together, these contributions significantly improve the consistency of event-based gaze signals, making them better suited for downstream tasks such as micro-expression analysis and mind-state decoding. Our results demonstrate consistent improvements across multiple baseline models on controlled datasets, laying the groundwork for future integration with multimodal affect recognition systems in real-world environments.
Improving Factuality for Dialogue Response Generation via Graph-Based Knowledge Augmentation
Authors: Xiangyan Chen, Yujian Gan, Matthew Purver
Date: 2025-06-14 00:00:00
Tasks: Response Generation

Large Language Models (LLMs) succeed in many natural language processing tasks. However, their tendency to hallucinate - generate plausible but inconsistent or factually incorrect text - can cause problems in certain tasks, including response generation in dialogue. To mitigate this issue, knowledge-augmented methods have shown promise in reducing hallucinations. Here, we introduce a novel framework designed to enhance the factuality of dialogue response generation, as well as an approach to evaluate dialogue factual accuracy. Our framework combines a knowledge triple retriever, a dialogue rewrite, and knowledge-enhanced response generation to produce more accurate and grounded dialogue responses. To further evaluate generated responses, we propose a revised fact score that addresses the limitations of existing fact-score methods in dialogue settings, providing a more reliable assessment of factual consistency. We evaluate our methods using different baselines on the OpendialKG and HybriDialogue datasets. Our methods significantly improve factuality compared to other graph knowledge-augmentation baselines, including the state-of-the-art G-retriever. The code will be released on GitHub.
Levels of Autonomy for AI Agents
Authors: K. J. Kevin Feng, David W. McDonald, Amy X. Zhang
Date: 2025-06-14 00:00:00

Autonomy is a double-edged sword for AI agents, simultaneously unlocking transformative possibilities and serious risks. How can agent developers calibrate the appropriate levels of autonomy at which their agents should operate? We argue that an agent's level of autonomy can be treated as a deliberate design decision, separate from its capability and operational environment. In this work, we define five levels of escalating agent autonomy, characterized by the roles a user can take when interacting with an agent: operator, collaborator, consultant, approver, and observer. Within each level, we describe the ways by which a user can exert control over the agent and open questions for how to design the nature of user-agent interaction. We then highlight a potential application of our framework towards AI autonomy certificates to govern agent behavior in single- and multi-agent systems. We conclude by proposing early ideas for evaluating agents' autonomy. Our work aims to contribute meaningful, practical steps towards responsibly deployed and useful AI agents in the real world.
SplashNet: Split-and-Share Encoders for Accurate and Efficient Typing with Surface Electromyography
Authors: Nima Hadidi, Jason Chan, Ebrahim Feghhi, Jonathan Kao
Date: 2025-06-14 00:00:00

Surface electromyography (sEMG) at the wrists could enable natural, keyboard-free text entry, yet the state-of-the-art emg2qwerty baseline still misrecognizes $51.8\%$ of characters in the zero-shot setting on unseen users and $7.0\%$ after user-specific fine-tuning. We trace many of these errors to mismatched cross-user signal statistics, fragile reliance on high-order feature dependencies, and the absence of architectural inductive biases aligned with the bilateral nature of typing. To address these issues, we introduce three simple modifications: (i) Rolling Time Normalization, which adaptively aligns input distributions across users; (ii) Aggressive Channel Masking, which encourages reliance on low-order feature combinations more likely to generalize across users; and (iii) a Split-and-Share encoder that processes each hand independently with weight-shared streams to reflect the bilateral symmetry of the neuromuscular system. Combined with a five-fold reduction in spectral resolution ($33\!\rightarrow\!6$ frequency bands), these components yield a compact Split-and-Share model, SplashNet-mini, which uses only $\tfrac14$ the parameters and $0.6\times$ the FLOPs of the baseline while reducing character-error rate (CER) to $36.4\%$ zero-shot and $5.9\%$ after fine-tuning. An upscaled variant, SplashNet ($\tfrac12$ the parameters, $1.15\times$ the FLOPs of the baseline), further lowers error to $35.7\%$ and $5.5\%$, representing relative improvements of $31\%$ and $21\%$ in the zero-shot and fine-tuned settings, respectively. SplashNet therefore establishes a new state of the art without requiring additional data.
SheetMind: An End-to-End LLM-Powered Multi-Agent Framework for Spreadsheet Automation
Authors: Ruiyan Zhu, Xi Cheng, Ke Liu, Brian Zhu, Daniel Jin, Neeraj Parihar, Zhoutian Xu, Oliver Gao
Date: 2025-06-14 00:00:00

We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions. The system comprises three specialized agents: a Manager Agent that decomposes complex user instructions into subtasks; an Action Agent that translates these into structured commands using a Backus Naur Form (BNF) grammar; and a Reflection Agent that validates alignment between generated actions and the user's original intent. Integrated into Google Sheets via a Workspace extension, SheetMind supports real-time interaction without requiring scripting or formula knowledge. Experiments on benchmark datasets demonstrate an 80 percent success rate on single step tasks and approximately 70 percent on multi step instructions, outperforming ablated and baseline variants. Our results highlight the effectiveness of multi agent decomposition and grammar based execution for bridging natural language and spreadsheet functionalities.
Cloud Infrastructure Management in the Age of AI Agents
Authors: Zhenning Yang, Archit Bhatnagar, Yiming Qiu, Tongyuan Miao, Patrick Tser Jern Kon, Yunming Xiao, Yibo Huang, Martin Casado, Ang Chen
Date: 2025-06-13 00:00:00
Tasks: Management

Cloud infrastructure is the cornerstone of the modern IT industry. However, managing this infrastructure effectively requires considerable manual effort from the DevOps engineering team. We make a case for developing AI agents powered by large language models (LLMs) to automate cloud infrastructure management tasks. In a preliminary study, we investigate the potential for AI agents to use different cloud/user interfaces such as software development kits (SDK), command line interfaces (CLI), Infrastructure-as-Code (IaC) platforms, and web portals. We report takeaways on their effectiveness on different management tasks, and identify research challenges and potential solutions.
The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs
Authors: Avinash Baidya, Kamalika Das, Xiang Gao
Date: 2025-06-13 00:00:00
Tasks: Large Language Model

Large Language Model (LLM)-based agents have significantly impacted Task-Oriented Dialog Systems (TODS) but continue to face notable performance challenges, especially in zero-shot scenarios. While prior work has noted this performance gap, the behavioral factors driving the performance gap remain under-explored. This study proposes a comprehensive evaluation framework to quantify the behavior gap between AI agents and human experts, focusing on discrepancies in dialog acts, tool usage, and knowledge utilization. Our findings reveal that this behavior gap is a critical factor negatively impacting the performance of LLM agents. Notably, as task complexity increases, the behavior gap widens (correlation: 0.963), leading to a degradation of agent performance on complex task-oriented dialogs. For the most complex task in our study, even the GPT-4o-based agent exhibits low alignment with human behavior, with low F1 scores for dialog acts (0.464), excessive and often misaligned tool usage with a F1 score of 0.139, and ineffective usage of external knowledge. Reducing such behavior gaps leads to significant performance improvement (24.3% on average). This study highlights the importance of comprehensive behavioral evaluations and improved alignment strategies to enhance the effectiveness of LLM-based TODS in handling complex tasks.
ProVox: Personalization and Proactive Planning for Situated Human-Robot Collaboration
Authors: Jennifer Grannen, Siddharth Karamcheti, Blake Wulfe, Dorsa Sadigh
Date: 2025-06-13 00:00:00

Collaborative robots must quickly adapt to their partner's intent and preferences to proactively identify helpful actions. This is especially true in situated settings where human partners can continually teach robots new high-level behaviors, visual concepts, and physical skills (e.g., through demonstration), growing the robot's capabilities as the human-robot pair work together to accomplish diverse tasks. In this work, we argue that robots should be able to infer their partner's goals from early interactions and use this information to proactively plan behaviors ahead of explicit instructions from the user. Building from the strong commonsense priors and steerability of large language models, we introduce ProVox ("Proactive Voice"), a novel framework that enables robots to efficiently personalize and adapt to individual collaborators. We design a meta-prompting protocol that empowers users to communicate their distinct preferences, intent, and expected robot behaviors ahead of starting a physical interaction. ProVox then uses the personalized prompt to condition a proactive language model task planner that anticipates a user's intent from the current interaction context and robot capabilities to suggest helpful actions; in doing so, we alleviate user burden, minimizing the amount of time partners spend explicitly instructing and supervising the robot. We evaluate ProVox through user studies grounded in household manipulation tasks (e.g., assembling lunch bags) that measure the efficiency of the collaboration, as well as features such as perceived helpfulness, ease of use, and reliability. Our analysis suggests that both meta-prompting and proactivity are critical, resulting in 38.7% faster task completion times and 31.9% less user burden relative to non-active baselines. Supplementary material, code, and videos can be found at https://provox-2025.github.io.
Reimagining Dance: Real-time Music Co-creation between Dancers and AI
Authors: Olga Vechtomova, Jeff Bos
Date: 2025-06-13 00:00:00

Dance performance traditionally follows a unidirectional relationship where movement responds to music. While AI has advanced in various creative domains, its application in dance has primarily focused on generating choreography from musical input. We present a system that enables dancers to dynamically shape musical environments through their movements. Our multi-modal architecture creates a coherent musical composition by intelligently combining pre-recorded musical clips in response to dance movements, establishing a bidirectional creative partnership where dancers function as both performers and composers. Through correlation analysis of performance data, we demonstrate emergent communication patterns between movement qualities and audio features. This approach reconceptualizes the role of AI in performing arts as a responsive collaborator that expands possibilities for both professional dance performance and improvisational artistic expression across broader populations.