cs papers | Gist.Science

Distributed Coordination Algorithms with Efficient Communication for Open Multi-Agent Systems with Dynamic Communication Links and Processing Delays

This paper proposes and analyzes three communication-efficient distributed algorithms that achieve finite-time quantized average consensus in open multi-agent systems with dynamic directed links, arbitrary bounded processing delays, and continuous node turnover, while establishing novel topological conditions for convergence and demonstrating superior performance through simulations.

Jiaqi Hu, Karl H. Johansson, Apostolos I. Rikos2026-03-10💻 cs

Step Automata

This paper proposes the concepts of step automata and step Turing machines (STM) as a natural extension of traditional automata and Turing machines to bridge the gap between classical computation and concurrency by enabling the execution of atomic action steps without partial orders.

Yong Wang2026-03-10💻 cs

Trajectory Tracking Control Design for Autonomous Helicopters with Guaranteed Error Bounds

This paper proposes a systematic framework using Robust Positive Invariant sets to compute formally guaranteed trajectory tracking error bounds for autonomous helicopters, comparing three controller architectures to balance tracking performance with the conservatism of the derived error bounds.

Philipp Schitz, Johann C. Dauer, Paolo Mercorelli2026-03-10💻 cs

WhispEar: A Bi-directional Framework for Scaling Whispered Speech Conversion via Pseudo-Parallel Whisper Generation

This paper introduces WhispEar, a bidirectional framework that leverages a normal-to-whisper model to generate scalable pseudo-parallel data for training a whisper-to-normal conversion system, thereby overcoming data scarcity challenges and achieving superior performance on a newly released bilingual whispered-normal corpus.

Zihao Fang, Yingda Shen, Zifan Guan, Tongtong Song, Zhenyi Liu, Zhizheng Wu2026-03-10💻 cs

S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis

The paper proposes S2S-FDD, a novel framework that bridges the semantic gap between high-dimensional industrial time-series signals and natural language by converting sensor data into descriptive summaries and utilizing a multi-turn tree-structured reasoning process with historical documents to achieve explainable, zero-shot fault diagnosis.

Baoxue Li, Chunhui Zhao2026-03-10💻 cs

Rendering Forces With a Modular Cable System, Motors, and Brakes

This paper presents a new reconfigurable haptic interface that utilizes a modular network of hybrid motor-brake cable actuation modules to deliver rich, multi-degree-of-freedom force feedback, capable of rendering smooth active forces up to 6 N and high-magnitude passive collision forces up to 186 N.

Jan Ulrich Bartels, Alexander Achberger, Katherine J. Kuchenbecker, Michael Sedlmair2026-03-10💻 cs

Speed3R: Sparse Feed-forward 3D Reconstruction Models

Speed3R is a sparse feed-forward 3D reconstruction model that overcomes the quadratic computational bottleneck of dense attention by employing a dual-branch mechanism to focus on informative tokens, achieving a 12.4x inference speedup with minimal accuracy trade-offs.

Weining Ren, Xiao Tan, Kai Han2026-03-10💻 cs

See and Switch: Vision-Based Branching for Interactive Robot-Skill Programming

This paper introduces "See & Switch," a vision-based interactive framework for Programming by Demonstration that utilizes eye-in-hand images to enable reliable online conditional branching and anomaly detection in dexterous robot tasks, achieving high accuracy across diverse conditions and novice users.

Petr Vanc, Jan Kristof Behrens, Václav Hlaváč, Karla Stepanova2026-03-10💻 cs

ImageEdit-R1: Boosting Multi-Agent Image Editing via Reinforcement Learning

ImageEdit-R1 is a novel multi-agent framework that employs reinforcement learning to coordinate specialized vision-language and generative agents, enabling dynamic, context-aware image editing that outperforms existing monolithic models and baselines in handling complex, multi-step user instructions.

Yiran Zhao, Yaoqi Ye, Xiang Liu, Michael Qizhe Shieh, Trung Bui2026-03-10💻 cs

CinemaWorld: Generative Augmented Reality with LLMs and 3D Scene Generation for Movie Augmentation

CinemaWorld is a generative augmented reality system for the Meta Quest 3 that uses multimodal large language models and generative AI to extract features from 2D movie scenes and automatically synthesize synchronized 3D mixed reality content, thereby enhancing viewer immersion and enjoyment as validated through technical, user, and expert evaluations.

Keiichi Ihara, DaeHo Lee, Manato Abe, Hye-Young Jo, Ryo Suzuki2026-03-10💻 cs

Enhancing Cross-View UAV Geolocalization via LVLM-Driven Relational Modeling

This paper proposes a novel plug-and-play ranking architecture that leverages Large Vision-Language Models (LVLMs) and a relational-aware loss function to explicitly model cross-view interactions, thereby significantly enhancing the accuracy and stability of UAV-to-satellite image geolocalization.

Bowen Liu, Pengyue Jia, Wanyu Wang, Derong Xu, Jiawei Cheng, Jiancheng Dong, Xiao Han, Zimo Zhao, Chao Zhang, Bowen Yu, Fangyu Hong, Xiangyu Zhao2026-03-10💻 cs

Evaluating Generative Models via One-Dimensional Code Distributions

This paper proposes a novel evaluation framework for generative models that replaces traditional continuous feature-based metrics with training-free and no-reference metrics operating in discrete visual token space, demonstrating superior correlation with human judgments across a new large-scale benchmark called VisForm.

Zexi Jia, Pengcheng Luo, Yijia Zhong, Jinchao Zhang, Jie Zhou2026-03-10💻 cs

In-Context Reinforcement Learning for Tool Use in Large Language Models

This paper proposes In-Context Reinforcement Learning (ICRL), a novel framework that eliminates the need for supervised fine-tuning by leveraging few-shot prompting during reinforcement learning rollouts to progressively teach large language models how to effectively use external tools, ultimately achieving state-of-the-art performance in a data-efficient, zero-shot manner.

Yaoqi Ye, Yiran Zhao, Keyu Duan, Zeyu Zheng, Kenji Kawaguchi, Cihang Xie, Michael Qizhe Shieh2026-03-10💻 cs

Synthetic Defect Image Generation for Power Line Insulator Inspection Using Multimodal Large Language Models

This paper proposes a training-free pipeline using multimodal large language models to generate diverse, high-fidelity synthetic defect images for power line insulators, which significantly improves classification performance and data efficiency in low-data regimes by augmenting limited real-world datasets.

Xuesong Wang, Caisheng Wang2026-03-10💻 cs

Geometric Give and Take

This paper analyzes a geometric balancing game on line arrangements, determining the minimum initial number of pebbles required for Alice to prevent Bob from emptying any box and proving that this threshold scales as $\Theta(n^3)$ for $n$ lines in general position.

Oswin Aichholzer, Katharina Klost, Kristin Knorr, Viola Mészáros, Josef Tkadlec2026-03-10💻 cs

TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery

The paper proposes TALON, a test-time adaptive learning framework for on-the-fly category discovery that overcomes the limitations of static hash-based methods by dynamically updating semantic prototypes and the feature encoder to continuously integrate new knowledge, while employing margin-aware logit calibration to prevent category explosion and significantly improve novel-class accuracy.

Yanan Wu, Yuhan Yan, Tailai Chen, Zhixiang Chi, ZiZhang Wu, Yi Jin, Yang Wang, Zhenbo Li2026-03-10💻 cs

Why Large Language Models can Secretly Outperform Embedding Similarity in Information Retrieval

Although the study finds that Large Language Model-based relevance judgment systems do not outperform embedding-based retrieval on standard TREC-DL 2019 benchmarks due to the short-sightedness inherent in human annotations, it argues that these models possess the theoretical capability to surpass embedding methods by better understanding relevance through reasoning.

Matei Benescu, Ivo Pascal de Jong2026-03-10💻 cs

Augmented Model Predictive Control: A Balance between Satellite Agility and Computation Complexity

This paper introduces an augmented Model Predictive Control method for agile earth observation satellites that effectively balances high-performance nonlinear control capabilities with the computational simplicity required for hardware implementation, validated through both numerical simulations and physical experiments.

Yiming Wang, Mihindukulasooriya Sheral Crescent Tissera, Haihong Yu, Kai Jie Ethan Foo, Sean Yeo Keyuan, Ankit Srivastava, Hao An2026-03-10💻 cs

M-ABD: Scalable, Efficient, and Robust Multi-Affine-Body Dynamics

This paper introduces M-ABD, a scalable and robust framework that leverages linear kinematic mapping and a compact dual-space formulation of Affine Body Dynamics to enable interactive, stable simulation of large-scale articulated assemblies with hundreds of thousands of bodies on a single CPU core.

Zhiyong He (University of Utah), Dewen Guo (University of Utah), Minghao Guo (MIT), Yili Zhao (ByteDance), Wojciech Matusik (MIT), Hao Su (UCSD), Chenfanfu Jiang (UCLA), Peter Yichen Chen (UBC), Yin Yang (University of Utah)2026-03-10💻 cs

MRDrive: An Open Source Mixed Reality Driving Simulator for Automotive User Research

This paper introduces MRDrive, an open-source mixed reality driving simulator that bridges the gap between ecological validity and experimental control by allowing users to interact with a real vehicle cabin while immersed in a virtual driving environment to support automotive user research.

Patrick Ebel, Michał Patryk Miazga, Martin Lorenz, Timur Getselev, Pavlo Bazilinskyy, Celine Conzen2026-03-10💻 cs

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