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1. Communication-Aware Robotics: Exploiting Motion for Communication

   https://doi.org/10.1146/annurev-control-071420-080708  

   Muralidharan, Arjun; Mostofi, Yasamin (May 2021, Annual Review of Control, Robotics, and Autonomous Systems)

   In this review, we present a comprehensive perspective on communication-aware robotics, an area that considers realistic communication environments and aims to jointly optimize communication and navigation. The main focus of the article is theoretical characterization and understanding of performance guarantees. We begin by summarizing the best prediction an unmanned vehicle can have of the channel quality at unvisited locations. We then consider the case of a single robot, showing how it can mathematically characterize the statistics of its traveled distance until connectivity and further plan its path to reach a connected location with optimality guarantees, in real channel environments and with minimum energy consumption. We then move to the case of multiple robots, showing how they can utilize their motions to enable robust information flow. We consider two specific robotic network configurations—robotic beamformers and robotic routers—and mathematically characterize properties of the co-optimum motion–communication decisions. 

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2. Catalytic Communication

   Pyne, Edward; Sheffield, Nathan S; Wang, William (January 2025, 16th Innovations in Theoretical Computer Science Conference (ITCS 2025))

   The study of space-bounded computation has drawn extensively from ideas and results in the field of communication complexity. Catalytic Computation (Buhrman, Cleve, Koucký, Loff and Speelman, STOC 2013) studies the power of bounded space augmented with a pre-filled hard drive that can be used non-destructively during the computation. Presently, many structural questions in this model remain open. Towards a better understanding of catalytic space, we define a model of catalytic communication complexity and prove new upper and lower bounds. In our model, Alice and Bob share a blackboard with a tiny number of free bits, and a larger section with an arbitrary initial configuration. They must jointly compute a function of their inputs, communicating only via the blackboard, and must always reset the blackboard to its initial configuration. We prove several upper and lower bounds: 1) We characterize the simplest nontrivial model, that of one bit of free space and three rounds, in terms of 𝔽₂ rank. In particular, we give natural problems that are solvable with a minimal-sized blackboard that require near-maximal (randomized) communication complexity, and vice versa. 2) We show that allowing constantly many free bits, as opposed to one, allows an exponential improvement on the size of the blackboard for natural problems. To do so, we connect the problem to existence questions in extremal graph theory. 3) We give tight connections between our model and standard notions of non-uniform catalytic computation. Using this connection, we show that with an arbitrary constant number of rounds and bits of free space, one can compute all functions in TC⁰. We view this model as a step toward understanding the value of filled space in computation. 

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3. Decentralized Fictitious Play with Voluntary Communication in Random Communication Networks

   https://doi.org/10.1109/CDC42340.2020.9303789  

   Aydin, Sarper; Eksin, Ceyhun (December 2020, IEEE Conf. Decision and Control)

   null (Ed.)
4. Nonverbal Communication Cue Recognition: A Pathway to More Accessible Communication

   Shafique, Zoya; Wang, Haiyan; Tian, Yingli. (June 2023, In proceedings of Women in Computer Vision Workshop in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR))

   Nonverbal communication, such as body language, facial expressions, and hand gestures, is crucial to human communication as it conveys more information about emotions and attitudes than spoken words. However, individuals who are blind or have low-vision (BLV) may not have access to this method of communication, leading to asymmetry in conversations. Developing systems to recognize nonverbal communication cues (NVCs) for the BLV community would enhance communication and understanding for both parties. This paper focuses on developing a multimodal computer vision system to recognize and detect NVCs. To accomplish our objective, we are collecting a dataset focused on nonverbal communication cues. Here, we propose a baseline model for recognizing NVCs and present initial results on the Aff-Wild2 dataset. Our baseline model achieved an accuracy of 68% and a F1-Score of 64% on the Aff-Wild2 validation set, making it comparable with previous state of the art results. Furthermore, we discuss the various challenges associated with NVC recognition as well as the limitations of our current work. 

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5. Communication Makes Perfect: Persuasion Dataset Construction via Multi-LLM Communication

   https://doi.org/10.18653/v1/2025.naacl-long.203  

   Ma, Weicheng; Zhang, Hefan; Yang, Ivory; Ji, Shiyu; Chen, Joice; Hashemi, Farnoosh; Mohole, Shubham; Gearey, Ethan; Macy, Michael; Hassanpour, Saeed; et al (May 2025, Association for Computational Linguistics)