Ruwen Zhang, Bo Liu, Zhang Sheng Xiang, Yida Chen, Hantao Zhao, Ding Ding, Jiahui Jin, Jiuxin Cao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

People frequently face preference-based planning tasks requiring balancing goals with nuanced constraints, yet even advanced LLMs demand considerable effort to produce and adjust plans reflecting complex user preferences. We present MAVIS (Multi-Agent Virtual Interactive Synergy), a multi-agent system within a virtual workspace that introduces an incremental collaboration mechanism. This mechanism automatically decomposes tasks into guidelines and sequentially introduces expert agents. Each agent proactively engages users in focused dialog to uncover implicit preferences, while successive agents add perspectives and transparently negotiate trade-offs. To mitigate textual overload, MAVIS employs spatial visualizations that externalize agents’ reasoning through step-linked summaries and context-aware boards, with embodied avatars supporting natural interaction. Across studies, Study 1 showed our collaboration mechanism doubled expressed preferences and improved planning quality by 60.3% over a conventional LLM baseline. Study 2 affirmed visualization’s benefits over a non-spatial baseline, while Study 3 confirmed its versatility across VR and desktop modalities and diverse tasks.

The rapid emergence of smart homes offers convenience but also raises significant privacy concerns, such as challenges in comprehending privacy-related statuses, information, and settings. This study aims to mitigate these concerns by leveraging augmented reality technology to enhance privacy protection user experiences. Through interviews with experienced users in the Chinese culture context, we systematically identify and categorize three primary aspects of concerns encountered during smart home usage: sensor range and status, data transmission directions, and transparency of mode settings. Drawing on these insights, we design and implement a streamlined AR system for interacting with smart home devices. Our user studies demonstrate that our AR system offers significant advantages over traditional 2D smart home applications in terms of information representation, workload reduction (i.e., number of clicks), and enhanced privacy awareness. This research presents a notable step forward in privacy protection while suggesting pathways for AR-enhanced smart home systems.

In the context of rapid urbanization, traditional manual guidance and static evacuation signs are increasingly inadequate for addressing complex and dynamic emergencies. This study proposes an innovative emergency evacuation framework that optimizes the crowd evacuation by integrating multiagent reinforcement learning (MARL) with adversarial reinforcement learning (ARL). The developed simulation environment models realistic human behavior in complex buildings and incorporates robotic navigation and intelligent path planning. A novel simulated human behavior model was integrated, capable of complex human–robot interaction, independent escape route searching, and exhibiting herd mentality and memory mechanisms. We also proposed a multiagent framework that combines MARL and ARL to enhance overall evacuation efficiency and robustness. Additionally, we developed a new ARL evaluation framework that provides a novel method for quantifying agents’ performance. Various experiments of differing difficulty levels were conducted, and the results demonstrate that the proposed framework exhibits advantages in emergency evacuation scenarios. Specifically, our ARLR approach increased survival rates by 1.8% points in low-difficulty evacuation tasks compared to the RLR approach using only MARL algorithms. In high-difficulty evacuation tasks, the ARLR approach raised survival rates from 46.7% without robots to 64.4%, exceeding the RLR approach by 1.7% points. This study aims to enhance the efficiency and safety of human–robot collaborative fire evacuations and provides theoretical support for evaluating and improving the performance and robustness of ARL agents.

Ballistocardiograms (BCG) is a passive, non-contact heart rate detection technology that requires no action on the part of the individual. However, during the BCG signal acquisition process, the surface pressure generated by cardiac contraction is easily disturbed by external factors, and as people's health deteriorates, the j-peak (the main peak of the BCG signal) is no longer prominent. Our aim is to establish a non-contact, self-supervised heart rate detection method based on physiological information, to improve the accuracy and robustness of BCG heart rate detection under wider and more adverse conditions. The algorithm is guided by the heart rate estimation based on BCG itself, thereby reconstructing a signal with physiological significance. We also propose a heartbeat mapping algorithm based on Bidirectional Long Short-Term Memory Network (BiLSTM) for extracting global deep features, achieving real-time heartbeat prediction, and eliminating local deviations brought about by reconstruction. To verify the effectiveness of the proposed method, this paper evaluated 40 young subjects and 4 elderly subjects. Compared with the existing state-of-the-art methods, beat-to-beat heart rate estimation and heartbeat detection both performed excellently, surpassing most methods using precise labels. The experimental results show that the proposed method achieves effective heartbeat detection, demonstrating robustness and effectiveness in the face of unavoidable noise and variations.

Understanding the movement of crowds is important to the management of public places and urban safety. Existing researches mostly focused on tracking pedestrians in video clips from a single camera or across multiple cameras (Multi-Object Tracking) by identifying individuals with similar appearance or spatial-temporal movement features. However, how crowds navigate through invisible area between cameras in crowded environments have been overlooked. Moreover, identifying individuals across camera in a crowded environment could be challenging due to cluttered pedestrian appearance and highly uncertain movements. In this paper, we focus on recovering crowd trajectories in the invisible area of sparse camera networks within crowded public environments. We achieve better spatial-temporal feature matching by estimating the most likely travel time between segmented tracklet observations of individuals with elaborate consideration of pedestrian interactions, which reduces the dependence on unreliable appearance features. Subsequently, we recover trajectories for matched tracklets in the invisible area with a high fidelity crowd simulation model. Extensive experiments on two real-world trajectory datasets show that our proposed method is superior to existing spatial-temporal based MOT methods and improves the appearance-based MOT models in terms of association accuracy and trajectory fidelity.

Fandom crowdfunding, where fans collectively raise funds for idols, fosters dynamic interactions within fandom communities, evoking a range of emotions. Despite the prevalence of such activities, the specific emotions involved and their effects on participant behavior remain underexplored. Addressing this, our mixed-methods study—encompassing observations, interviews, and analysis of crowdfunding data—investigated emotions during fandom crowdfunding and their influence on behavior across crowdfunding stages: planning, support, encouragement, realization, and auditing. We identified 10 key emotions related to idols and the community, finding these emotions crucial in shaping participant actions. Our findings highlight the dual impact of fandom crowdfunding on the community’s internal dynamics and its relationships with idols and broader society. We propose design recommendations for enhancing fandom crowdfunding and suggest how general crowdfunding can benefit from insights gained from the fandom context, offering a novel understanding of emotions in collaborative monetary activities.

Understanding the human factors governing effective information exchange is increasingly indispensable for the design of day to day human-computer systems. Moreover, effective information exchange becomes a matter of life or death during emergency egress. The complexity of an unknown environment and the unpredictable locations of hazards often prevent evacuees from identifying safe routes. Successful evacuations from locations impacted by fire or earthquakes may depend on user-generated information to increase the chance of collective survival. The present paper employed multi-user virtual reality experiments and an online survey to investigate the mechanisms underlying social influence and collective intelligence during emergencies. Our results demonstrate that information sharing helps to reduce evacuation time and trajectory length. Participants also shared more when given incentives or when there was a lack of knowledge in the public information pool. This work provides further indications of how collective intelligence can be promoted and deployed during emergencies.

The urban metro system accommodates significant travel demand and alleviates traffic congestion. Improving metro operational efficiency can increase the metro operator revenue and promote the development of robust urban transportation. To achieve this goal, passenger flow forecasting is a crucial and well-recognized task in metro operation. However, passenger flow forecasting is a challenging task as there exist many unquantifiable factors in resident travel. To address this problem, we propose an innovative model named Interaction Graph Network (IG-Net) to perform passenger flow forecasting at the station level, capable of capturing the non-Euclidean relationships between stations. Three kinds of inter-station interaction graphs are developed to model these inter-station interactions: connectivity, similarity, and temporal correlation graphs. Moreover, we apply multiple channels of graph convolutional neural networks to capture interaction representations and develop a multi-task learning architecture across multiple stations. The proposed IG-Net achieved better performance than the benchmark models when forecasting passenger flow over multiple stations, based on experiments with the Suzhou metro. Finally, we identify the significant effects of interaction graph combinations and multi-task loss functions via further experimentation.

Crowd simulation is a convenient method to evaluate pedestrians’ status and their corresponding management strategies in large public spaces. However, the performance of real-time simulation can be limited by the model’s large-scale computational cost. In order to overcome this difficulty, this study proposes PaCS (Parallel Computation for Crowd Simulation), a parallel computation framework for field-based crowd simulation, based on an enhanced status update method and an efficient task assignment strategy. Parallel computing is introduced with synchronous updates, task division and multiprocessing calculation mechanisms. The movement model is split into the smallest and independent computational units. The field model for simulating the crowd movement has also been improved in terms of weighted multi-direction choice and multi-field environment division. The experiments confirmed that the parallel synchronous algorithm has a significant advantage at the computational scale of more than 10,000 pedestrians. The speedup ratio of the parallel approach can be more than 5 times when simulating one million pedestrians. This framework can help to establish the essential methods for multi-modal transportation systems that require fast simulations for a large-scale crowd. It would also help future digital twin systems to evaluate and validate any potential management strategies when applied in metro stations, railway stations, and other transportation hubs.

Abstract With the mobile technology continues to grow and evolve, the technology of presenting building information modeling (BIM) with an online platform has become an important application in the fields of civil engineering, architecture, and computer visualization. However, due to network bandwidth and browser performance limitations, it was difficult to display large‐scale BIM scenes in a flawless manner on mobile browsers. CEBOW, a Cloud‐Edge‐Browser Online architecture for visualizing BIM components with online solutions, is proposed in this article. The method combines transmission scheduling, cache management, and optimal initial loading into a single system architecture. For network transmission testing, BIM scenes are used, and the results show that our method effectively reduces scene loading time and networking delay while improving the visualization effect of large‐scale scenes.

Abstract The increased growth of building complexity confronts the limited device resources, especially for lightweight personal digital devices. The research of handling these complex buildings with limited resources has become a pivotal research topic. This paper explores the task of lightweight indoor path planning in complex building information modeling (BIM) buildings. Both the environment modeling and path searching are important addressed through indoor path planning with a lightweight web‐based approach. A lightweight framework is designed in which the complex indoor path planning of a whole BIM building can be simplified into many local path searching of the building's interior spaces and all the online computation is decreased within no more than two simpler ones. In addition, an automatically indoor environment modeling method is proposed to partition the BIM building into many local interior spaces, constructing their spatial relationship and local grid map in a multilayered network. In the end, a heuristics optimized A star algorithm is implemented to speed up the shortest local path searching for the case of the path ends in a narrow corner. The final experiment shows that the online computational cost can be reduced greatly and dynamically to an average of 2 s when searching for the shortest path in a complex BIM building.