Tootfinder

IT'S OFFICIAL: Connecticut, Maine, Massachusetts, New Jersey, New York State, Pennsylvania, Rhode Island and New York City have formed the Northeast Public Health Collaborative.
The collaborative will issue their own vaccine recommendations and coordinate public health efforts.
https://www.…

Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
Shumeng Li, Jian Zhang, Lei Qi, Luping Zhou, Yinghuan Shi, Yang Gao
https://arxiv.org/abs/2508.13712

Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled data to generate pseudo labels. Recently, advanced state space models, represented by Mamba, have shown efficient handling of long-range dependencies. This drives us to explore their potential in semi-supervised medical image segmentation. In this paper, we propose a novel Diversity-enhanced Collaborative Mamba framewor…

Substituting Proof of Work in Blockchain with Training-Verified Collaborative Model Computation
Mohammad Ishzaz Asif Rafid, Morsalin Sakib
https://arxiv.org/abs/2508.12138 https…

Substituting Proof of Work in Blockchain with Training-Verified Collaborative Model Computation
Bitcoin's Proof of Work (PoW) mechanism, while central to achieving decentralized consensus, has long been criticized for excessive energy use and hardware inefficiencies \cite{devries2018bitcoin, truby2018decarbonizing}. This paper introduces a hybrid architecture that replaces Bitcoin's traditional PoW with a centralized, cloud-based collaborative training framework. In this model, miners contribute computing resources to train segments of horizontally scaled machine learning models on prepro…

Examining the Impact of Tutorial Activity Engagement on Undergraduate Students' Collaborative Preferences
Sang Hyun Kim, Tanya Evans
https://arxiv.org/abs/2508.13511 https:/…

Examining the Impact of Tutorial Activity Engagement on Undergraduate Students' Collaborative Preferences
This study examines the impact of tutorial engagement on Collaborative Preferences for Learning Mathematics (CPLM) in a tertiary context. A two-way mixed ANOVA analysed these preferences over a semester in a sample of undergraduate students. As expected, collaborative engagement had a significant main effect, with students who collaborated more reporting stronger preferences for working with their peers (higher CPLM). The absence of an interaction effect between the nature of tutorial engagemen…

AI Agents with Human-Like Collaborative Tools: Adaptive Strategies for Enhanced Problem-Solving
Harper Reed, Michael Sugimura, Angelo Zangari
https://arxiv.org/abs/2509.13547 ht…

AI Agents with Human-Like Collaborative Tools: Adaptive Strategies for Enhanced Problem-Solving
We investigate whether giving LLM agents the collaborative tools and autonomy that humans naturally use for problem solving can improve their performance. We equip Claude Code agents with MCP-based social media and journaling tools and allow them to use these tools as they see fit. Across 34 Aider Polyglot Python programming challenges, collaborative tools substantially improve performance on the hardest problems, delivering 15-40% lower cost, 12-27% fewer turns, and 12-38% faster completion th…

Human Interaction for Collaborative Semantic SLAM using Extended Reality
Laura Ribeiro, Muhammad Shaheer, Miguel Fernandez-Cortizas, Ali Tourani, Holger Voos, Jose Luis Sanchez-Lopez
https://arxiv.org/abs/2509.14949

Human Interaction for Collaborative Semantic SLAM using Extended Reality
Semantic SLAM (Simultaneous Localization and Mapping) systems enrich robot maps with structural and semantic information, enabling robots to operate more effectively in complex environments. However, these systems struggle in real-world scenarios with occlusions, incomplete data, or ambiguous geometries, as they cannot fully leverage the higher-level spatial and semantic knowledge humans naturally apply. We introduce HICS-SLAM, a Human-in-the-Loop semantic SLAM framework that uses a shared exte…

Can GenAI Move from Individual Use to Collaborative Work? Experiences, Challenges, and Opportunities of Integrating GenAI into Collaborative Newsroom Routines
Qing Xiao (Diane), Qing (Diane), Hu, Jingjia Xiao, Hancheng Cao, Hong Shen
https://arxiv.org/abs/2509.10950

Can GenAI Move from Individual Use to Collaborative Work? Experiences, Challenges, and Opportunities of Integrating GenAI into Collaborative Newsroom Routines
Generative AI (GenAI) is reshaping work, but adoption remains largely individual and experimental rather than integrated into collaborative routines. Whether GenAI can move from individual use to collaborative work is a critical question for future organizations. Journalism offers a compelling site to examine this shift: individual journalists have already been disrupted by GenAI tools; yet newswork is inherently collaborative relying on shared routines and coordinated workflows. We conducted 2…

Representation Quantization for Collaborative Filtering Augmentation
Yunze Luo, Yinjie Jiang, Gaode Chen, Jingchi Wang, Shicheng Wang, Ruina Sun, Jiang Yuezihan, Jun Zhang, Jian Liang, Han Li, Kun Gai, Kaigui Bian
https://arxiv.org/abs/2508.11194

Representation Quantization for Collaborative Filtering Augmentation
As the core algorithm in recommendation systems, collaborative filtering (CF) algorithms inevitably face the problem of data sparsity. Since CF captures similar users and items for recommendations, it is effective to augment the lacking user-user and item-item homogeneous linkages. However, existing methods are typically limited to connecting through overlapping interacted neighbors or through similar attributes and contents. These approaches are constrained by coarse-grained, sparse attributes…

this seminar looks plenty interesting!
Sept 19th, 3:00PM
East Hall - 4448
Lisa Fauci, Tulane University
Two Tales of Waving Tails in Biological Fluid Dynamics
The motion of undulating, flexible “tails" in a fluid environment is a common element in many biological systems, from sperm movement in reproduction to fish swimming in the ocean. This talk will focus on two collaborative projects where computational fluid dynamics has shed some light on fundamental me…

Synchronization Dynamics of Heterogeneous, Collaborative Multi-Agent AI Systems
Chiranjit Mitra
https://arxiv.org/abs/2508.12314 https://arxiv.org/pdf/2508…

Synchronization Dynamics of Heterogeneous, Collaborative Multi-Agent AI Systems
We present a novel interdisciplinary framework that bridges synchronization theory and multi-agent AI systems by adapting the Kuramoto model to describe the collective dynamics of heterogeneous AI agents engaged in complex task execution. By representing AI agents as coupled oscillators with both phase and amplitude dynamics, our model captures essential aspects of agent specialization, influence, and communication within networked systems. We introduce an order parameter to quantify the degree…

CoDiEmb: A Collaborative yet Distinct Framework for Unified Representation Learning in Information Retrieval and Semantic Textual Similarity
Bowen Zhang, Zixin Song, Chunquan Chen, Qian-Wen Zhang, Di Yin, Xing Sun
https://arxiv.org/abs/2508.11442

CoDiEmb: A Collaborative yet Distinct Framework for Unified Representation Learning in Information Retrieval and Semantic Textual Similarity
Learning unified text embeddings that excel across diverse downstream tasks is a central goal in representation learning, yet negative transfer remains a persistent obstacle. This challenge is particularly pronounced when jointly training a single encoder for Information Retrieval (IR) and Semantic Textual Similarity (STS), two essential but fundamentally disparate tasks for which naive co-training typically yields steep performance trade-offs. We argue that resolving this conflict requires sys…

Affordance-Based Disambiguation of Surgical Instructions for Collaborative Robot-Assisted Surgery
Ana Davila, Jacinto Colan, Yasuhisa Hasegawa
https://arxiv.org/abs/2509.14967 h…

Affordance-Based Disambiguation of Surgical Instructions for Collaborative Robot-Assisted Surgery
Effective human-robot collaboration in surgery is affected by the inherent ambiguity of verbal communication. This paper presents a framework for a robotic surgical assistant that interprets and disambiguates verbal instructions from a surgeon by grounding them in the visual context of the operating field. The system employs a two-level affordance-based reasoning process that first analyzes the surgical scene using a multimodal vision-language model and then reasons about the instruction using …

CSGO: Generalized Optimization for Cold Start in Wireless Collaborative Edge LLM Systems
Xuran Liu, Nan Xue, Rui Bao, Yaping Sun, Zhiyong Chen, Meixia Tao, Xiaodong Xu, Shuguang Cui
https://arxiv.org/abs/2508.11287

CSGO: Generalized Optimization for Cold Start in Wireless Collaborative Edge LLM Systems
While deploying large language models on edge devices promises low-latency and privacy-preserving AI services, it is hindered by limited device resources. Although pipeline parallelism facilitates distributed inference, existing approaches often ignore the cold-start latency caused by on-demand model loading. In this paper, we propose a latency-aware scheduling framework that overlaps model loading with computation and communication to minimize total inference latency. Based on device and model…

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach
Jundong Chen, Honglei Zhang, Chunxu Zhang, Fangyuan Luo, Yidong Li
https://arxiv.org/abs/2508.12386

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach
Federated recommendation (FR) facilitates collaborative training by aggregating local models from massive devices, enabling client-specific personalization while ensuring privacy. However, we empirically and theoretically demonstrate that server-side aggregation can undermine client-side personalization, leading to suboptimal performance, which we term the aggregation bottleneck. This issue stems from the inherent heterogeneity across numerous clients in FR, which drives the globally aggregated…

DSSD: Efficient Edge-Device Deployment and Collaborative Inference via Distributed Split Speculative Decoding
Jiahong Ning, Ce Zheng, Tingting Yang
https://arxiv.org/abs/2507.12000

DSSD: Efficient Edge-Device Deployment and Collaborative Inference via Distributed Split Speculative Decoding
Large language models (LLMs) have transformed natural language processing but face critical deployment challenges in device-edge systems due to resource limitations and communication overhead. To address these issues, collaborative frameworks have emerged that combine small language models (SLMs) on devices with LLMs at the edge, using speculative decoding (SD) to improve efficiency. However, existing solutions often trade inference accuracy for latency or suffer from high uplink transmission c…

Towards safe control parameter tuning in distributed multi-agent systems
Abdullah Tokmak, Thomas B. Sch\"on, Dominik Baumann
https://arxiv.org/abs/2508.13608 https://

Towards safe control parameter tuning in distributed multi-agent systems
Many safety-critical real-world problems, such as autonomous driving and collaborative robots, are of a distributed multi-agent nature. To optimize the performance of these systems while ensuring safety, we can cast them as distributed optimization problems, where each agent aims to optimize their parameters to maximize a coupled reward function subject to coupled constraints. Prior work either studies a centralized setting, does not consider safety, or struggles with sample efficiency. Since w…

Collaborative Preferences for Learning Mathematics: A Scale Validation Study
Sang Hyun Kim, Tanya Evans
https://arxiv.org/abs/2508.12199 https://arxiv.org/…

Collaborative Preferences for Learning Mathematics: A Scale Validation Study
Collaboration within mathematics has been established as being effective in providing students with crucial opportunities to develop critical thinking, effective communication, and teamwork skills. By engaging in group problem-solving and shared learning experiences, students may gain deeper insights into mathematical concepts and learn to approach challenges from multiple perspectives. However, there remains a need for a reliable instrument to capture students' preferences for collaboration. T…

BandFuzz: An ML-powered Collaborative Fuzzing Framework
Wenxuan Shi, Hongwei Li, Jiahao Yu, Xinqian Sun, Wenbo Guo, Xinyu Xing
https://arxiv.org/abs/2507.10845

BandFuzz: An ML-powered Collaborative Fuzzing Framework
Collaborative fuzzing has recently emerged as a technique that combines multiple individual fuzzers and dynamically chooses the appropriate combinations suited for different programs. Unlike individual fuzzers, which rely on specific assumptions to maintain their effectiveness, collaborative fuzzing relaxes the assumptions on target programs, providing constant and robust performance across various programs. Ideally, collaborative fuzzing should be a more promising direction toward generic fuzz…

The Role of AI in Facilitating Interdisciplinary Collaboration: Evidence from AlphaFold
Naixuan Zhao, Chunli Wei, Xinyan Zhang, Jiang Li
https://arxiv.org/abs/2508.13234 https:/…

The Role of AI in Facilitating Interdisciplinary Collaboration: Evidence from AlphaFold
The acceleration of artificial intelligence (AI) in science is recognized and many scholars have begun to explore its role in interdisciplinary collaboration. However, the mechanisms and extent of this impact are still unclear. This study, using AlphaFold's impact on structural biologists, examines how AI technologies influence interdisciplinary collaborative patterns. By analyzing 1,247 AlphaFold-related papers and 7,700 authors from Scopus, we employ bibliometric analysis and causal inference…

A Software-Defined Radio Testbed for Distributed LiDAR Point Cloud Sharing with IEEE 802.11p in V2V Networks
Mario Hernandez, Elijah Bryce, Peter Stubberud, Ebrahim Saberinia, Brendan Morris
https://arxiv.org/abs/2509.14523

A Software-Defined Radio Testbed for Distributed LiDAR Point Cloud Sharing with IEEE 802.11p in V2V Networks
We present a Software Defined Radio (SDR)-based IEEE 802.11p testbed for distributed Vehicle-to-Vehicle (V2V) communication. The platform bridges the gap between network simulation and deployment by providing a modular codebase configured for cost-effective ADALM-Pluto SDRs. Any device capable of running a Docker with ROS, executing Matlab and interface with a Pluto via USB can act as a communication node. To demonstrate collaborative sensing, we share LiDAR point clouds between nodes and fuse …

SlimComm: Doppler-Guided Sparse Queries for Bandwidth-Efficient Cooperative 3-D Perception
Melih Yazgan, Qiyuan Wu, Iramm Hamdard, Shiqi Li, J. Marius Zoellner
https://arxiv.org/abs/2508.13007

SlimComm: Doppler-Guided Sparse Queries for Bandwidth-Efficient Cooperative 3-D Perception
Collaborative perception allows connected autonomous vehicles (CAVs) to overcome occlusion and limited sensor range by sharing intermediate features. Yet transmitting dense Bird's-Eye-View (BEV) feature maps can overwhelm the bandwidth available for inter-vehicle communication. We present SlimComm, a communication-efficient framework that integrates 4D radar Doppler with a query-driven sparse scheme. SlimComm builds a motion-centric dynamic map to distinguish moving from static objects and gene…

The Yokai Learning Environment: Tracking Beliefs Over Space and Time
Constantin Ruhdorfer, Matteo Bortoletto, Andreas Bulling
https://arxiv.org/abs/2508.12480 https://

The Yokai Learning Environment: Tracking Beliefs Over Space and Time
Developing collaborative AI hinges on Theory of Mind (ToM) - the ability to reason about the beliefs of others to build and maintain common ground. Existing ToM benchmarks, however, are restricted to passive observer settings or lack an assessment of how agents establish and maintain common ground over time. To address these gaps, we introduce the Yokai Learning Environment (YLE) - a multi-agent reinforcement learning (RL) environment based on the cooperative card game Yokai. In the YLE, agents…

A Collaborative Framework for Quantum Optimisation and Quantum Neural Networks: Credit Feature Selection and Image Classification
JiaNing Long, Xuechen Liang
https://arxiv.org/abs/2509.11110

A Collaborative Framework for Quantum Optimisation and Quantum Neural Networks: Credit Feature Selection and Image Classification
This paper investigates the efficacy of quantum computing in two distinct machine learning tasks: feature selection for credit risk assessment and image classification for handwritten digit recognition. For the first task, we address the feature selection challenge of the German Credit Dataset by formulating it as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is solved using quantum annealing to identify the optimal feature subset. Experimental results show that the result…

Optimal CO2 storage management considering safety constraints in multi-stakeholder multi-site CCS projects: a game theoretic perspective
Jungang Chen, Seyyed A. Hosseini
https://arxiv.org/abs/2508.11618

Optimal CO2 storage management considering safety constraints in multi-stakeholder multi-site CCS projects: a game theoretic perspective
Carbon capture and storage (CCS) projects typically involve a diverse array of stakeholders or players from public, private, and regulatory sectors, each with different objectives and responsibilities. Given the complexity, scale, and long-term nature of CCS operations, determining whether individual stakeholders can independently maximize their interests or whether collaborative coalition agreements are needed remains a central question for effective CCS project planning and management. CCS pr…

Towards Smart Workplaces: Understanding Mood-Influencing Factors of the Physical Workspace in Collaborative Group Settings
Tzu-Hui Wu, Sebastian Cmentowski, Yunyin Lou, Jun Hu, Regina Bernhaupt
https://arxiv.org/abs/2508.11335

Towards Smart Workplaces: Understanding Mood-Influencing Factors of the Physical Workspace in Collaborative Group Settings
Group mood plays a crucial role in shaping workspace experiences, influencing group dynamics, team performance, and creativity. The perceived group mood depends on many, often subconscious, aspects such as individual emotional states or group life, which make it challenging to maintain a positive atmosphere. Intelligent technology could support mood regulation in physical office environments, for example, as adaptive ambient lighting for mood regulation. However, little is known about the relat…

Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
Minhao Wang, Yunhang He, Cong Xu, Zhangchi Zhu, Wei Zhang
https://arxiv.org/abs/2508.10312

Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. However, LLM-based recommenders exhibit a tendency to overemphasize semantic correlations within users' interaction history. When taking pretrained collaborative ID embeddings as input, LLM-based recommenders progressively weaken the inherent collaborative signals as the embeddings propagate through LLM backbones layer by layer, as opposed to tradition…

Notición musical: El regreso de Aerosmith en colaboración con Yungblud
Lanzaršn un single en conjunto que aún no tiene fecha de publicación.
https://www.rollingstone.com/music/music-news/yungblud-aerosmith-preview-single-my-only…

OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data
Zhouyu Li, Zhijing Yang, Huayue Gu, Xiaojian Wang, Yuchen Liu, Ruozhou Yu
https://arxiv.org/abs/2508.13374

OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data
Earth observation analytics have the potential to serve many time-sensitive applications. However, due to limited bandwidth and duration of ground-satellite connections, it takes hours or even days to download and analyze data from existing Earth observation satellites, making real-time demands like timely disaster response impossible. Toward real-time analytics, we introduce OrbitChain, a collaborative analytics framework that orchestrates computational resources across multiple satellites in …

A Large-Scale Web Search Dataset for Federated Online Learning to Rank
Marcel Gregoriadis, Jingwei Kang, Johan Pouwelse
https://arxiv.org/abs/2508.12353 https://

A Large-Scale Web Search Dataset for Federated Online Learning to Rank
The centralized collection of search interaction logs for training ranking models raises significant privacy concerns. Federated Online Learning to Rank (FOLTR) offers a privacy-preserving alternative by enabling collaborative model training without sharing raw user data. However, benchmarks in FOLTR are largely based on random partitioning of classical learning-to-rank datasets, simulated user clicks, and the assumption of synchronous client participation. This oversimplifies real-world dynami…

A Knowledge-driven Adaptive Collaboration of LLMs for Enhancing Medical Decision-making
Xiao Wu, Ting-Zhu Huang, Liang-Jian Deng, Yanyuan Qiao, Imran Razzak, Yutong Xie
https://arxiv.org/abs/2509.14998

A Knowledge-driven Adaptive Collaboration of LLMs for Enhancing Medical Decision-making
Medical decision-making often involves integrating knowledge from multiple clinical specialties, typically achieved through multidisciplinary teams. Inspired by this collaborative process, recent work has leveraged large language models (LLMs) in multi-agent collaboration frameworks to emulate expert teamwork. While these approaches improve reasoning through agent interaction, they are limited by static, pre-assigned roles, which hinder adaptability and dynamic knowledge integration. To address…

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks
Ouassim Karrakchou, Alaa Zniber, Anass Sebbar, Mounir Ghogho
https://arxiv.org/abs/2509.12291 …

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks
Distributed Denial of Service (DDoS) attacks pose a persistent threat to network security, requiring timely and scalable mitigation strategies. In this paper, we propose a novel collaborative architecture that integrates a P4-programmable data plane with an SDN control plane to enable real-time DDoS detection and response. At the core of our approach is a split early-exit neural network that performs partial inference in the data plane using a quantized Convolutional Neural Network (CNN), while…

SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks
Kutub Uddin, Awais Khan, Muhammad Umar Farooq, Khalid Malik
https://arxiv.org/abs/2507.13170

SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks
Audio plays a crucial role in applications like speaker verification, voice-enabled smart devices, and audio conferencing. However, audio manipulations, such as deepfakes, pose significant risks by enabling the spread of misinformation. Our empirical analysis reveals that existing methods for detecting deepfake audio are often vulnerable to anti-forensic (AF) attacks, particularly those attacked using generative adversarial networks. In this article, we propose a novel collaborative learning me…

CollabVLA: Self-Reflective Vision-Language-Action Model Dreaming Together with Human
Nan Sun, Yongchang Li, Chenxu Wang, Huiying Li, Huaping Liu
https://arxiv.org/abs/2509.14889

CollabVLA: Self-Reflective Vision-Language-Action Model Dreaming Together with Human
In this work, we present CollabVLA, a self-reflective vision-language-action framework that transforms a standard visuomotor policy into a collaborative assistant. CollabVLA tackles key limitations of prior VLAs, including domain overfitting, non-interpretable reasoning, and the high latency of auxiliary generative models, by integrating VLM-based reflective reasoning with diffusion-based action generation under a mixture-of-experts design. Through a two-stage training recipe of action groundin…

Playing telephone with generative models: "verification disability," "compelled reliance," and accessibility in data visualization
Frank Elavsky, Cindy Xiong Bearfield
https://arxiv.org/abs/2508.12192

Playing telephone with generative models: "verification disability," "compelled reliance," and accessibility in data visualization
This paper is a collaborative piece between two worlds of expertise in the field of data visualization: accessibility and bias. In particular, the rise of generative models playing a role in accessibility is a worrying trend for data visualization. These models are increasingly used to help author visualizations as well as generate descriptions of existing visualizations for people who are blind, low vision, or use assistive technologies such as screen readers. Sighted human-to-human bias has a…

SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication
Ruijia Zhang, Xinyan Zhao, Ruixiang Wang, Sigen Chen, Guibin Zhang, An Zhang, Kun Wang, Qingsong Wen
https://arxiv.org/abs/2508.11733

SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication
LLM-based multi-agent systems exhibit strong collaborative capabilities but often suffer from redundant communication and excessive token overhead. Existing methods typically enhance efficiency through pretrained GNNs or greedy algorithms, but often isolate pre- and post-task optimization, lacking a unified strategy. To this end, we present SafeSieve, a progressive and adaptive multi-agent pruning algorithm that dynamically refines the inter-agent communication through a novel dual-mechanism. S…

Small Data Explainer -- The impact of small data methods in everyday life
Maren Hackenberg, Sophia G. Connor, Fabian Kabus, June Brawner, Ella Markham, Mahi Hardalupas, Areeq Chowdhury, Rolf Backofen, Anna K\"ottgen, Angelika Rohde, Nadine Binder, Harald Binder, the Collaborative Research Center 1597 Small Data
https://arxi…

Small Data Explainer -- The impact of small data methods in everyday life
The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings with limited information, can benefit from such developments. This includes societal issues such as how best to include under-represented groups in data-driven policy and decision making, or the health benefits of assistive technologies such as wearables. We provide a conceptual overview, in particular contrasting small data with big data, and identify comm…

"This convening is not an isolated event. It’s the first step in scaling a movement that reimagines how values-driven open science organisations can grow together, share resources, and strengthen the ecosystem we collectively serve. By working together, we can ensure that the future of open science is not only innovative, but also equitable, sustainable, and resilient."
https://www.carpentries.org/blog/2025/09/convening-to-reclaim-and-sustain-open-science-communities/
Great to see this forward looking collaborative work being done by @… @… @… @… and OLS

Enhancing Video Large Language Models with Structured Multi-Video Collaborative Reasoning (early version)
Zhihao He, Tianyao He, Tieyuan Chen, Yun Xu, Huabin Liu, Chaofan Gan, Gui Zou, Weiyao Lin
https://arxiv.org/abs/2509.13161

Enhancing Video Large Language Models with Structured Multi-Video Collaborative Reasoning (early version)
Despite the prosperity of the video language model, the current pursuit of comprehensive video reasoning is thwarted by the inherent spatio-temporal incompleteness within individual videos, resulting in hallucinations and inaccuracies. A promising solution is to augment the reasoning performance with multiple related videos. However, video tokens are numerous and contain redundant information, so directly feeding the relevant video data into a large language model to enhance responses could be …

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/8]:
- Policy Search, Retrieval, and Composition via Task Similarity in Collaborative Agentic Systems
Nath, Peridis, Benjamin, Liu, Kolouri, Kinnell, Li, Liu, Dora, Soltoggio

An Overlapping Coalition Game Approach for Collaborative Block Mining and Edge Task Offloading in MEC-assisted Blockchain Networks
Licheng Ye, Zehui Xiong, Lin Gao, Dusit Niyato
https://arxiv.org/abs/2508.06031

An Overlapping Coalition Game Approach for Collaborative Block Mining and Edge Task Offloading in MEC-assisted Blockchain Networks
Mobile edge computing (MEC) is a promising technology that enhances the efficiency of mobile blockchain networks, by enabling miners, often acted by mobile users (MUs) with limited computing resources, to offload resource-intensive mining tasks to nearby edge computing servers. Collaborative block mining can further boost mining efficiency by allowing multiple miners to form coalitions, pooling their computing resources and transaction data together to mine new blocks collaboratively. Therefore…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks
Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

Quo Vadis, Code Review? Exploring the Future of Code Review
Michael Dorner, Andreas Bauer, Darja \v{S}mite, Lukas Thode, Daniel Mendez, Ricardo Britto, Stephan Lukasczyk, Ehsan Zabardast, Michael Kormann
https://arxiv.org/abs/2508.06879

Quo Vadis, Code Review? Exploring the Future of Code Review
Code review has long been a core practice in collaborative software engineering. In this research, we explore how practitioners reflect on code review today and what changes they anticipate in the near future. We then discuss the potential long-term risks of these anticipated changes for the evolution of code review and its role in collaborative software engineering.

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- COLP: Scaffolding Children's Online Long-term Collaborative Learning
Siyu Zha, Yuanrong Tang, Jiangtao Gong, Yingqing Xu

GraphCogent: Overcoming LLMs' Working Memory Constraints via Multi-Agent Collaboration in Complex Graph Understanding
Rongzheng Wang, Qizhi Chen, Yihong Huang, Yizhuo Ma, Muquan Li, Jiakai Li, Ke Qin, Guangchun Luo, Shuang Liang
https://arxiv.org/abs/2508.12379

GraphCogent: Overcoming LLMs' Working Memory Constraints via Multi-Agent Collaboration in Complex Graph Understanding
Large language models (LLMs) show promising performance on small-scale graph reasoning tasks but fail when handling real-world graphs with complex queries. This phenomenon stems from LLMs' inability to effectively process complex graph topology and perform multi-step reasoning simultaneously. To address these limitations, we propose GraphCogent, a collaborative agent framework inspired by human Working Memory Model that decomposes graph reasoning into specialized cognitive processes: sense, buf…

A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
Daniel Commey, Rebecca A. Sarpong, Griffith S. Klogo, Winful Bagyl-Bac, Garth V. Crosby
https://arxiv.org/abs/2507.12439

A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
Federated learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, its open-participation nature exposes it to data-poisoning attacks, in which malicious actors submit corrupted model updates to degrade the global model. Existing defenses are often reactive, relying on statistical aggregation rules that can be computationally expensive and that typically assume an honest majority. This paper introduces a proactive, economic defense:…

Dynamic Quality-Latency Aware Routing for LLM Inference in Wireless Edge-Device Networks
Rui Bao, Nan Xue, Yaping Sun, Zhiyong Chen
https://arxiv.org/abs/2508.11291 https://

Dynamic Quality-Latency Aware Routing for LLM Inference in Wireless Edge-Device Networks
The integration of wireless communications and Large Language Models (LLMs) is poised to unlock ubiquitous intelligent services, yet deploying them in wireless edge-device collaborative environments presents a critical trade-off between inference quality and end-to-end latency. A fundamental mismatch exists between task complexity and resource allocation: offloading simple queries invites prohibitive latency, while on-device models lack the capacity for demanding computations. To address this c…

Automated Novelty Evaluation of Academic Paper: A Collaborative Approach Integrating Human and Large Language Model Knowledge
Wenqing Wu, Chengzhi Zhang, Yi Zhao
https://arxiv.org/abs/2507.11330

Automated Novelty Evaluation of Academic Paper: A Collaborative Approach Integrating Human and Large Language Model Knowledge
Novelty is a crucial criterion in the peer review process for evaluating academic papers. Traditionally, it's judged by experts or measure by unique reference combinations. Both methods have limitations: experts have limited knowledge, and the effectiveness of the combination method is uncertain. Moreover, it's unclear if unique citations truly measure novelty. The large language model (LLM) possesses a wealth of knowledge, while human experts possess judgment abilities that the LLM does not po…

Collaborative Loco-Manipulation for Pick-and-Place Tasks with Dynamic Reward Curriculum
Tianxu An, Flavio De Vincenti, Yuntao Ma, Marco Hutter, Stelian Coros
https://arxiv.org/abs/2509.13239

Collaborative Loco-Manipulation for Pick-and-Place Tasks with Dynamic Reward Curriculum
We present a hierarchical RL pipeline for training one-armed legged robots to perform pick-and-place (P&P) tasks end-to-end -- from approaching the payload to releasing it at a target area -- in both single-robot and cooperative dual-robot settings. We introduce a novel dynamic reward curriculum that enables a single policy to efficiently learn long-horizon P&P operations by progressively guiding the agents through payload-centered sub-objectives. Compared to state-of-the-art approaches for lon…

Recovery of UAV Swarm-enabled Collaborative Beamforming in Low-altitude Wireless Networks under Wind Field Disturbances
Geng Sun, Chenbang Liu, Jiahui Li, Guannan Qu, Shuang Liang, Jiacheng Wang, Changyuan Zhao, Dusit Niyato
https://arxiv.org/abs/2507.08507

Recovery of UAV Swarm-enabled Collaborative Beamforming in Low-altitude Wireless Networks under Wind Field Disturbances
Unmanned aerial vehicle (UAV) swarms utilizing collaborative beamforming (CB) in low-altitude wireless networks (LAWN) demonstrate significant potential for enhanced communication range, energy efficiency, and signal directivity through the formation of virtual antenna arrays (VAA). However, environmental disturbances, particularly wind fields, significantly degrade CB performance by introducing positional errors that disrupt beam patterns, thereby compromising transmission reliability. This pa…

Collaborative Document Editing with Multiple Users and AI Agents
Florian Lehmann, Krystsina Shauchenka, Daniel Buschek
https://arxiv.org/abs/2509.11826 https://

Collaborative Document Editing with Multiple Users and AI Agents
Current AI writing support tools are largely designed for individuals, complicating collaboration when co-writers must leave the shared workspace to use AI and then communicate and reintegrate results. We propose integrating AI agents directly into collaborative writing environments. Our prototype makes AI use transparent and customisable through two new shared objects: agent profiles and tasks. Agent responses appear in the familiar comment feature. In a user study (N=30), 14 teams worked on w…

LLM-based ambiguity detection in natural language instructions for collaborative surgical robots
Ana Davila, Jacinto Colan, Yasuhisa Hasegawa
https://arxiv.org/abs/2507.11525

LLM-based ambiguity detection in natural language instructions for collaborative surgical robots
Ambiguity in natural language instructions poses significant risks in safety-critical human-robot interaction, particularly in domains such as surgery. To address this, we propose a framework that uses Large Language Models (LLMs) for ambiguity detection specifically designed for collaborative surgical scenarios. Our method employs an ensemble of LLM evaluators, each configured with distinct prompting techniques to identify linguistic, contextual, procedural, and critical ambiguities. A chain-o…

Collaborative Trustworthiness for Good Decision Making in Autonomous Systems
Selma Saidi, Omar Laimona, Christoph Schmickler, Dirk Ziegenbein
https://arxiv.org/abs/2507.11135

Collaborative Trustworthiness for Good Decision Making in Autonomous Systems
Autonomous systems are becoming an integral part of many application domains, like in the mobility sector. However, ensuring their safe and correct behaviour in dynamic and complex environments remains a significant challenge, where systems should autonomously make decisions e.g., about manoeuvring. We propose in this paper a general collaborative approach for increasing the level of trustworthiness in the environment of operation and improve reliability and good decision making in autonomous s…

You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception
Hao Si, Ehsan Javanmardi, Manabu Tsukada
https://arxiv.org/abs/2509.09310 https://

You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception
Collaborative perception enables vehicles to overcome individual perception limitations by sharing information, allowing them to see further and through occlusions. In real-world scenarios, models on different vehicles are often heterogeneous due to manufacturer variations. Existing methods for heterogeneous collaborative perception address this challenge by fine-tuning adapters or the entire network to bridge the domain gap. However, these methods are impractical in real-world applications, as…

Collaborative Computing Strategy Based SINS Prediction for Emergency UAVs Network
Bing Li, Haoming Guo, Zhiyuan Ren, Wenchi Cheng, Jialin Hu, Xinke Jian
https://arxiv.org/abs/2508.06864

Collaborative Computing Strategy Based SINS Prediction for Emergency UAVs Network
In emergency scenarios, the dynamic and harsh conditions necessitate timely trajectory adjustments for drones, leading to highly dynamic network topologies and potential task failures. To address these challenges, a collaborative computing strategy based strapdown inertial navigation system (SINS) prediction for emergency UAVs network (EUN) is proposed, where a two-step weighted time expanded graph (WTEG) is constructed to deal with dynamic network topology changes. Furthermore, the task schedu…

Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation
Yifan Liu, Yaokun Liu, Zelin Li, Zhenrui Yue, Gyuseok Lee, Ruichen Yao, Yang Zhang, Dong Wang
https://arxiv.org/abs/2509.10468

Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation
Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenizer, and then large language models (LLMs) are trained to generate the next item via sequence-to-sequence modeling. However, these two stages are optimized for different objectives: semantic reconstruction during tokenizer pretraining versus user interaction modeling during recommender training. This objective misalignment leads to two key limitations: (i) …

GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Jisoo Lee, Raeyoung Chang, Dongwook Kwon, Harmanpreet Singh, Nikhil Verma
https://arxiv.org/abs/2507.13190

GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Multi-agent systems built on language models have shown strong performance on collaborative reasoning tasks. However, existing evaluations focus only on the correctness of the final output, overlooking how inefficient communication and poor coordination contribute to redundant reasoning and higher computational costs. We introduce GEMMAS, a graph-based evaluation framework that analyzes the internal collaboration process by modeling agent interactions as a directed acyclic graph. To capture col…

CoMoE: Collaborative Optimization of Expert Aggregation and Offloading for MoE-based LLMs at Edge
Muqing Li, Ning Li, Xin Yuan, Wenchao Xu, Quan Chen, Song Guo, Haijun Zhang
https://arxiv.org/abs/2508.09208

CoMoE: Collaborative Optimization of Expert Aggregation and Offloading for MoE-based LLMs at Edge
The proliferation of large language models (LLMs) has driven the adoption of Mixture-of-Experts (MoE) architectures as a promising solution to scale model capacity while controlling computational costs. However, deploying MoE models in resource-constrained mobile edge computing environments presents significant challenges due to their large memory footprint and dynamic expert activation patterns. To address these challenges, we propose a novel dynamic resource-aware collaborative optimization f…

Governance and Technological Challenge in Digital Solidarity Economies: A Case Study of a Collaborative Transportation Platform in South Korea
Jeongone Seo, Tawfiq Ammari
https://arxiv.org/abs/2507.04166

Governance and Technological Challenge in Digital Solidarity Economies: A Case Study of a Collaborative Transportation Platform in South Korea
South Korea's City P illustrates how lofty goals of digital solidarity can falter when challenged by local governance realities. Drawing on Hansmann's ownership theory, collaborative governance concepts, and platform cooperativism, we conducted a qualitative case study involving policy documents, independent assessments, and 11 in-depth interviews with residents, officials, and technology developers. Findings reveal a marked disconnect between the initiative's stated emphasis on community co-ow…

Synesthesia of Machines (SoM)-Aided LiDAR Point Cloud Transmission for Collaborative Perception
Ensong Liu, Rongqing Zhang, Xiang Cheng, Jian Tang
https://arxiv.org/abs/2509.06506

Synesthesia of Machines (SoM)-Aided LiDAR Point Cloud Transmission for Collaborative Perception
Collaborative perception enables more accurate and comprehensive scene understanding by learning how to share information between agents, with LiDAR point clouds providing essential precise spatial data. Due to the substantial data volume generated by LiDAR sensors, efficient point cloud transmission is essential for low-latency multi-agent collaboration. In this work, we propose an efficient, robust and applicable LiDAR point cloud transmission system via the Synesthesia of Machines (SoM), ter…

Teacher-Guided Pseudo Supervision and Cross-Modal Alignment for Audio-Visual Video Parsing
Yaru Chen, Ruohao Guo, Liting Gao, Yang Xiang, Qingyu Luo, Zhenbo Li, Wenwu Wang
https://arxiv.org/abs/2509.14097

Teacher-Guided Pseudo Supervision and Cross-Modal Alignment for Audio-Visual Video Parsing
Weakly-supervised audio-visual video parsing (AVVP) seeks to detect audible, visible, and audio-visual events without temporal annotations. Previous work has emphasized refining global predictions through contrastive or collaborative learning, but neglected stable segment-level supervision and class-aware cross-modal alignment. To address this, we propose two strategies: (1) an exponential moving average (EMA)-guided pseudo supervision framework that generates reliable segment-level masks via a…

Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
Tuo Zhang, Ning Li, Xin Yuan, Wenchao Xu, Quan Chen, Song Guo, Haijun Zhang
https://arxiv.org/abs/2508.07329

Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
With the breakthrough progress of large language models (LLMs) in natural language processing and multimodal tasks, efficiently deploying them on resource-constrained edge devices has become a critical challenge. The Mixture of Experts (MoE) architecture enhances model capacity through sparse activation, but faces two major difficulties in practical deployment: (1) The presence of numerous outliers in activation distributions leads to severe degradation in quantization accuracy for both activat…

MAD-Spear: A Conformity-Driven Prompt Injection Attack on Multi-Agent Debate Systems
Yu Cui, Hongyang Du
https://arxiv.org/abs/2507.13038 https://

MAD-Spear: A Conformity-Driven Prompt Injection Attack on Multi-Agent Debate Systems
Multi-agent debate (MAD) systems leverage collaborative interactions among large language models (LLMs) agents to improve reasoning capabilities. While recent studies have focused on increasing the accuracy and scalability of MAD systems, their security vulnerabilities have received limited attention. In this work, we introduce MAD-Spear, a targeted prompt injection attack that compromises a small subset of agents but significantly disrupts the overall MAD process. Manipulated agents produce mu…

SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song, Henrry Peng Zou, Ke Xu, Yuanjie Zhu, Philip S. Yu
https://arxiv.org/abs/2507.13336

SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information. Self-supervised graph learning seeks to harness high-order collaborative filtering signals through unsupervised augmentation on the user-item bipartite graph, primarily leveraging a multi-task learning framework that includes both supervised recommendation loss and self-supervised contrastive loss. However, this separate design introduces additional grap…

Allen: Rethinking MAS Design through Step-Level Policy Autonomy
Qiangong Zhou, Zhiting Wang, Mingyou Yao, Zongyang Liu
https://arxiv.org/abs/2508.11294 https://

Allen: Rethinking MAS Design through Step-Level Policy Autonomy
We introduce a new Multi-Agent System (MAS) - Allen, designed to address two core challenges in current MAS design: (1) improve system's policy autonomy, empowering agents to dynamically adapt their behavioral strategies, and (2) achieving the trade-off between collaborative efficiency, task supervision, and human oversight in complex network topologies. Our core insight is to redefine the basic execution unit in the MAS, allowing agents to autonomously form different patterns by combining th…

MAPGD: Multi-Agent Prompt Gradient Descent for Collaborative Prompt Optimization
Yichen Han, Bojun Liu, Zhengpeng zhou, Guanyu Liu, Zeng Zhang, Yang Yang, Wenli Wang, Isaac N Shi, Yunyan, Lewei He, Tianyu Shi
https://arxiv.org/abs/2509.11361

MAPGD: Multi-Agent Prompt Gradient Descent for Collaborative Prompt Optimization
Prompt engineering is crucial for leveraging large language models (LLMs), but existing methods often rely on a single optimization trajectory, limiting adaptability and efficiency while suffering from narrow perspectives, gradient conflicts, and high computational cost. We propose MAPGD (Multi-Agent Prompt Gradient Descent), a framework integrating multi-agent collaboration with gradient-based optimization. MAPGD features specialized agents for task clarity, example selection, format design, a…

A Dynamic Service Offloading Algorithm Based on Lyapunov Optimization in Edge Computing
Peiyan Yuan, Ming Li, Chenyang Wang, Ledong An, Xiaoyan Zhao, Junna Zhang, Xiangyang Li, Huadong Ma
https://arxiv.org/abs/2509.10475

A Dynamic Service Offloading Algorithm Based on Lyapunov Optimization in Edge Computing
This study investigates the trade-off between system stability and offloading cost in collaborative edge computing. While collaborative offloading among multiple edge servers enhances resource utilization, existing methods often overlook the role of queue stability in overall system performance. To address this, a multi-hop data transmission model is developed, along with a cost model that captures both energy consumption and delay. A time-varying queue model is then introduced to maintain syst…

AI Hasn't Fixed Teamwork, But It Shifted Collaborative Culture: A Longitudinal Study in a Project-Based Software Development Organization (2023-2025)
Qing Xiao, Xinlan Emily Hu, Mark E. Whiting, Arvind Karunakaran, Hong Shen, Hancheng Cao
https://arxiv.org/abs/2509.10956

AI Hasn't Fixed Teamwork, But It Shifted Collaborative Culture: A Longitudinal Study in a Project-Based Software Development Organization (2023-2025)
When AI entered the workplace, many believed it could reshape teamwork as profoundly as it boosted individual productivity. Would AI finally ease the longstanding challenges of team collaboration? Our findings suggested a more complicated reality. We conducted a longitudinal two-wave interview study (2023-2025) with members (N=15) of a project-based software development organization to examine the expectations and use of AI in teamwork. In early 2023, just after the release of ChatGPT, particip…

Force-Based Viscosity and Elasticity Measurements for Material Biomechanical Characterisation with a Collaborative Robotic Arm
Luca Beber, Edoardo Lamon, Giacomo Moretti, Matteo Saveriano, Luca Fambri, Luigi Palopoli, Daniele Fontanelli
https://arxiv.org/abs/2507.11133

Force-Based Viscosity and Elasticity Measurements for Material Biomechanical Characterisation with a Collaborative Robotic Arm
Diagnostic activities, such as ultrasound scans and palpation, are relatively low-cost. They play a crucial role in the early detection of health problems and in assessing their progression. However, they are also error-prone activities, which require highly skilled medical staff. The use of robotic solutions can be key to decreasing the inherent subjectivity of the results and reducing the waiting list. For a robot to perform palpation or ultrasound scans, it must effectively manage physical i…

Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, Tadashi Maeno, Fatih Furkan Akman, Joseph Boudreau, Sankha Dutta, Shengyu Feng, Adolfy Hoisie, Kuan-Chieh Hsu, Raees Khan, Jaehyung Kim, Ozgur O. Kilic, Scott Klasky, Alexei Klimentov, Tatiana Korchuganova, Verena Ingrid Martinez Outschoorn, Paul Nilsson, David K. Park, Norbert Podhorszki, Yihui Ren, John Rembrandt Steele, Fr\'ed\'eric Suter, Sairam Sri Vatsavai, Torre Wenaus,…

Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
The collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimat…

Safeguarding Federated Learning-based Road Condition Classification
Sheng Liu, Panos Papadimitratos
https://arxiv.org/abs/2507.12568 https://

Safeguarding Federated Learning-based Road Condition Classification
Federated Learning (FL) has emerged as a promising solution for privacy-preserving autonomous driving, specifically camera-based Road Condition Classification (RCC) systems, harnessing distributed sensing, computing, and communication resources on board vehicles without sharing sensitive image data. However, the collaborative nature of FL-RCC frameworks introduces new vulnerabilities: Targeted Label Flipping Attacks (TLFAs), in which malicious clients (vehicles) deliberately alter their trainin…

Aesthetic Experience and Educational Value in Co-creating Art with Generative AI: Evidence from a Survey of Young Learners
Chengyuan Zhang, Suzhe Xu
https://arxiv.org/abs/2509.10576

Aesthetic Experience and Educational Value in Co-creating Art with Generative AI: Evidence from a Survey of Young Learners
This study investigates the aesthetic experience and educational value of collaborative artmaking with generative artificial intelligence (AI) among young learners and art students. Based on a survey of 112 participants, we examine how human creators renegotiate their roles, how conventional notions of originality are challenged, how the creative process is transformed, and how aesthetic judgment is formed in human--AI co-creation. Empirically, participants generally view AI as a partner that s…

CrazyMARL: Decentralized Direct Motor Control Policies for Cooperative Aerial Transport of Cable-Suspended Payloads
Viktor Lorentz, Khaled Wahba, Sayantan Auddy, Marc Toussaint, Wolfgang H\"onig
https://arxiv.org/abs/2509.14126

CrazyMARL: Decentralized Direct Motor Control Policies for Cooperative Aerial Transport of Cable-Suspended Payloads
Collaborative transportation of cable-suspended payloads by teams of Unmanned Aerial Vehicles (UAVs) has the potential to enhance payload capacity, adapt to different payload shapes, and provide built-in compliance, making it attractive for applications ranging from disaster relief to precision logistics. However, multi-UAV coordination under disturbances, nonlinear payload dynamics, and slack--taut cable modes remains a challenging control problem. To our knowledge, no prior work has addressed…

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
Selina Heller, Mohamed Ibrahim, David Antony Selby, Sebastian Vollmer
https://arxiv.org/abs/2507.08440

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
Decision conferences are structured, collaborative meetings that bring together experts from various fields to address complex issues and reach a consensus on recommendations for future actions or policies. These conferences often rely on facilitated discussions to ensure productive dialogue and collective agreement. Recently, Large Language Models (LLMs) have shown significant promise in simulating real-world scenarios, particularly through collaborative multi-agent systems that mimic group in…

CARES: Collaborative Agentic Reasoning for Error Detection in Surgery
Chang Han Low, Zhu Zhuo, Ziyue Wang, Jialang Xu, Haofeng Liu, Nazir Sirajudeen, Matthew Boal, Philip J. Edwards, Danail Stoyanov, Nader Francis, Jiehui Zhong, Di Gu, Evangelos B. Mazomenos, Yueming Jin
https://arxiv.org/abs/2508.08764

CARES: Collaborative Agentic Reasoning for Error Detection in Surgery
Robotic-assisted surgery (RAS) introduces complex challenges that current surgical error detection methods struggle to address effectively due to limited training data and methodological constraints. Therefore, we construct MERP (Multi-class Error in Robotic Prostatectomy), a comprehensive dataset for error detection in robotic prostatectomy with frame-level annotations featuring six clinically aligned error categories. In addition, we propose CARES (Collaborative Agentic Reasoning for Error De…

HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning
Liyang Chen, Tianxiang Ma, Jiawei Liu, Bingchuan Li, Zhuowei Chen, Lijie Liu, Xu He, Gen Li, Qian He, Zhiyong Wu
https://arxiv.org/abs/2509.08519

HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning
Human-Centric Video Generation (HCVG) methods seek to synthesize human videos from multimodal inputs, including text, image, and audio. Existing methods struggle to effectively coordinate these heterogeneous modalities due to two challenges: the scarcity of training data with paired triplet conditions and the difficulty of collaborating the sub-tasks of subject preservation and audio-visual sync with multimodal inputs. In this work, we present HuMo, a unified HCVG framework for collaborative mu…

From Misunderstandings to Learning Opportunities: Leveraging Generative AI in Discussion Forums to Support Student Learning
Stanislav Pozdniakov, Jonathan Brazil, Oleksandra Poquet, Stephan Krusche, Santiago Berrezueta-Guzman, Shazia Sadiq, Hassan Khosravi
https://arxiv.org/abs/2508.11150

From Misunderstandings to Learning Opportunities: Leveraging Generative AI in Discussion Forums to Support Student Learning
In the contemporary educational landscape, particularly in large classroom settings, discussion forums have become a crucial tool for promoting interaction and addressing student queries. These forums foster a collaborative learning environment where students engage with both the teaching team and their peers. However, the sheer volume of content generated in these forums poses two significant interconnected challenges: How can we effectively identify common misunderstandings that arise in stud…

From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
Guohao Li, Li Jing, Jia Wu, Xuefei Li, Kai Zhu, Yue He
https://arxiv.org/abs/2507.05715

From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
Most existing multimodal collaborative filtering recommendation (MCFRec) methods rely heavily on ID features and multimodal content to enhance recommendation performance. However, this paper reveals that ID features are effective but have limited benefits in multimodal collaborative filtering recommendation. Therefore, this paper systematically deconstruct the pros and cons of ID features: (i) they provide initial embedding but lack semantic richness, (ii) they provide a unique identifier for e…

Oops!... They Stole it Again: Attacks on Split Learning
Tanveer Khan, Antonis Michalas
https://arxiv.org/abs/2508.10598 https://arxiv.org/pdf/2508.10598

Oops!... They Stole it Again: Attacks on Split Learning
Split Learning (SL) is a collaborative learning approach that improves privacy by keeping data on the client-side while sharing only the intermediate output with a server. However, the distributed nature of SL introduces new security challenges, necessitating a comprehensive exploration of potential attacks. This paper systematically reviews various attacks on SL, classifying them based on factors such as the attacker's role, the type of privacy risks, when data leaks occur, and where vulnerabi…

DBOS Network Sensing: A Web Services Approach to Collaborative Awareness
Sophia Lockton, Jeremy Kepner, Michael Stonebraker, Hayden Jananthan, LaToya Anderson, William Arcand, David Bestor, William Bergeron, Alex Bonn, Daniel Burrill, Chansup Byun, Timothy Davis, Vijay Gadepally, Michael Houle, Matthew Hubbell, Michael Jones, Piotr Luszczek, Peter Michaleas, Lauren Milechin, Chasen Milner, Guillermo Morales, Julie Mullen, Michel Pelletier, Alex Poliakov, Andrew Prout, Albert Reuther, A…

DBOS Network Sensing: A Web Services Approach to Collaborative Awareness
DBOS (DataBase Operating System) is a novel capability that integrates web services, operating system functions, and database features to significantly reduce web-deployment effort while increasing resilience. Integration of high performance network sensing enables DBOS web services to collaboratively create a shared awareness of their network environments to enhance their collective resilience and security. Network sensing is added to DBOS using GraphBLAS hypersparse traffic matrices via two a…

Collaborative Human-Robot Surgery for Mandibular Angle Split Osteotomy: Optical Tracking based Approach
Zhe Han, Huanyu Tian, Tom Vercauteren, Da Liu, Changsheng Li, Xingguang Duan
https://arxiv.org/abs/2507.07794

Collaborative Human-Robot Surgery for Mandibular Angle Split Osteotomy: Optical Tracking based Approach
Mandibular Angle Split Osteotomy (MASO) is a significant procedure in oral and maxillofacial surgery. Despite advances in technique and instrumentation, its success still relies heavily on the surgeon's experience. In this work, a human-robot collaborative system is proposed to perform MASO according to a preoperative plan and under guidance of a surgeon. A task decomposition methodology is used to divide the collaborative surgical procedure into three subtasks: (1) positional control and (2) o…

Families' Vision of Generative AI Agents for Household Safety Against Digital and Physical Threats
Zikai Wen, Lanjing Liu, Yaxing Yao
https://arxiv.org/abs/2508.11030 https:…

Families' Vision of Generative AI Agents for Household Safety Against Digital and Physical Threats
As families face increasingly complex safety challenges in digital and physical environments, generative AI (GenAI) presents new opportunities to support household safety through multiple specialized AI agents. Through a two-phase qualitative study consisting of individual interviews and collaborative sessions with 13 parent-child dyads, we explored families' conceptualizations of GenAI and their envisioned use of AI agents in daily family life. Our findings reveal that families preferred to di…

Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification
Chenfei Xiong, Jingwei Ni, Yu Fan, Vil\'em Zouhar, Donya Rooein, Lorena Calvo-Bartolom\'e, Alexander Hoyle, Zhijing Jin, Mrinmaya Sachan, Markus Leippold, Dirk Hovy, Mennatallah El-Assady, Elliott Ash
https://arxiv.org/abs/2507.05010

Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification
We introduce Co-DETECT (Collaborative Discovery of Edge cases in TExt ClassificaTion), a novel mixed-initiative annotation framework that integrates human expertise with automatic annotation guided by large language models (LLMs). Co-DETECT starts with an initial, sketch-level codebook and dataset provided by a domain expert, then leverages the LLM to annotate the data and identify edge cases that are not well described by the initial codebook. Specifically, Co-DETECT flags challenging examples…

Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation
Qing Xu, Wenting Duan, Zhen Chen
https://arxiv.org/abs/2509.06740 https://

Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation
Histopathology image analysis is critical yet challenged by the demand of segmenting tissue regions and nuclei instances for tumor microenvironment and cellular morphology analysis. Existing studies focused on tissue semantic segmentation or nuclei instance segmentation separately, but ignored the inherent relationship between these two tasks, resulting in insufficient histopathology understanding. To address this issue, we propose a Co-Seg framework for collaborative tissue and nuclei segmenta…

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Shiwei Li, Qunwei Li, Haozhao Wang, Ruixuan Li, Jianbin Lin, Wenliang Zhong
https://arxiv.org/abs/2509.10161

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative model training without sharing local data. Despite its advantages, FL suffers from substantial communication overhead, which can affect training efficiency. Recent efforts have mitigated this issue by quantizing model updates to reduce communication costs. However, most existing methods apply quantization only after local training, introducing quantization errors into the trained parameters a…

Triple-S: A Collaborative Multi-LLM Framework for Solving Long-Horizon Implicative Tasks in Robotics
Zixi Jia, Hongbin Gao, Fashe Li, Jiqiang Liu, Hexiao Li, Qinghua Liu
https://arxiv.org/abs/2508.07421

Triple-S: A Collaborative Multi-LLM Framework for Solving Long-Horizon Implicative Tasks in Robotics
Leveraging Large Language Models (LLMs) to write policy code for controlling robots has gained significant attention. However, in long-horizon implicative tasks, this approach often results in API parameter, comments and sequencing errors, leading to task failure. To address this problem, we propose a collaborative Triple-S framework that involves multiple LLMs. Through In-Context Learning, different LLMs assume specific roles in a closed-loop Simplification-Solution-Summary process, effectivel…

AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
Florian Gr\"otschla, Luis M\"uller, Jan T\"onshoff, Mikhail Galkin, Bryan Perozzi
https://arxiv.org/abs/2507.08616

AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
Large-language models (LLMs) have demonstrated powerful problem-solving capabilities, in particular when organized in multi-agent systems. However, the advent of such systems also raises several questions on the ability of a complex network of agents to effectively self-organize and collaborate. While measuring performance on standard reasoning benchmarks indicates how well multi-agent systems can solve reasoning tasks, it is unclear whether these systems are able to leverage their topology eff…

FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection
Mohammad Hasan Narimani, Mostafa Tavassolipour
https://arxiv.org/abs/2509.10041

FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection
Federated learning (FL) offers an innovative paradigm for collaborative model training across decentralized devices, such as smartphones, balancing enhanced predictive performance with the protection of user privacy in sensitive areas like Internet of Things (IoT) and medical data analysis. Despite its advantages, FL encounters significant challenges related to user privacy protection against potential attacks and the management of communication costs. This paper introduces a novel federated le…

A Multimodal Framework for Understanding Collaborative Design Processes
Maurice Koch, Nelusa Pathmanathan, Daniel Weiskopf, Kuno Kurzhals
https://arxiv.org/abs/2508.06117 https:…

A Multimodal Framework for Understanding Collaborative Design Processes
An essential task in analyzing collaborative design processes, such as those that are part of workshops in design studies, is identifying design outcomes and understanding how the collaboration between participants formed the results and led to decision-making. However, findings are typically restricted to a consolidated textual form based on notes from interviews or observations. A challenge arises from integrating different sources of observations, leading to large amounts and heterogeneity o…

Poison to Detect: Detection of Targeted Overfitting in Federated Learning
Soumia Zohra El Mestari, Maciej Krzysztof Zuziak, Gabriele Lenzini
https://arxiv.org/abs/2509.11974 htt…

Poison to Detect: Detection of Targeted Overfitting in Federated Learning
Federated Learning (FL) enables collaborative model training across decentralised clients while keeping local data private, making it a widely adopted privacy-enhancing technology (PET). Despite its privacy benefits, FL remains vulnerable to privacy attacks, including those targeting specific clients. In this paper, we study an underexplored threat where a dishonest orchestrator intentionally manipulates the aggregation process to induce targeted overfitting in the local models of specific clie…

APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares
Kejia Fan, Jianheng Tang, Zhirui Yang, Feijiang Han, Jiaxu Li, Run He, Yajiang Huang, Anfeng Liu, Houbing Herbert Song, Yunhuai Liu, Huiping Zhuang
https://arxiv.org/abs/2508.10732

APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares
Personalized Federated Learning (PFL) has presented a significant challenge to deliver personalized models to individual clients through collaborative training. Existing PFL methods are often vulnerable to non-IID data, which severely hinders collective generalization and then compromises the subsequent personalization efforts. In this paper, to address this non-IID issue in PFL, we propose an Analytic Personalized Federated Learning (APFL) approach via dual-stream least squares. In our APFL, w…

Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation
Yubo Li, Weiyi Song
https://arxiv.org/abs/2509.12179 https://arxiv.org/pd…

Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation
Current AI alignment through RLHF follows a single directional paradigm that AI conforms to human preferences while treating human cognition as fixed. We propose a shift to co-alignment through Bidirectional Cognitive Alignment (BiCA), where humans and AI mutually adapt. BiCA uses learnable protocols, representation mapping, and KL-budget constraints for controlled co-evolution. In collaborative navigation, BiCA achieved 85.5% success versus 70.3% baseline, with 230% better mutual adaptation an…

ORQ: Complex Analytics on Private Data with Strong Security Guarantees
Eli Baum, Sam Buxbaum, Nitin Mathai, Muhammad Faisal, Vasiliki Kalavri, Mayank Varia, John Liagouris
https://arxiv.org/abs/2509.10793

ORQ: Complex Analytics on Private Data with Strong Security Guarantees
We present ORQ, a system that enables collaborative analysis of large private datasets using cryptographically secure multi-party computation (MPC). ORQ protects data against semi-honest or malicious parties and can efficiently evaluate relational queries with multi-way joins and aggregations that have been considered notoriously expensive under MPC. To do so, ORQ eliminates the quadratic cost of secure joins by leveraging the fact that, in practice, the structure of many real queries allows us…

Collaborative Exploration with a Marsupial Ground-Aerial Robot Team through Task-Driven Map Compression
Angelos Zacharia, Mihir Dharmadhikari, Kostas Alexis
https://arxiv.org/abs/2509.07655

Collaborative Exploration with a Marsupial Ground-Aerial Robot Team through Task-Driven Map Compression
Efficient exploration of unknown environments is crucial for autonomous robots, especially in confined and large-scale scenarios with limited communication. To address this challenge, we propose a collaborative exploration framework for a marsupial ground-aerial robot team that leverages the complementary capabilities of both platforms. The framework employs a graph-based path planning algorithm to guide exploration and deploy the aerial robot in areas where its expected gain significantly exce…

RefactorCoderQA: Benchmarking LLMs for Multi-Domain Coding Question Solutions in Cloud and Edge Deployment
Shadikur Rahman, Aroosa Hameed, Gautam Srivastava, Syed Muhammad Danish
https://arxiv.org/abs/2509.10436

RefactorCoderQA: Benchmarking LLMs for Multi-Domain Coding Question Solutions in Cloud and Edge Deployment
To optimize the reasoning and problem-solving capabilities of Large Language Models (LLMs), we propose a novel cloud-edge collaborative architecture that enables a structured, multi-agent prompting framework. This framework comprises three specialized components: GuideLLM, a lightweight model deployed at the edge to provide methodological guidance; SolverLLM, a more powerful model hosted in the cloud responsible for generating code solutions; and JudgeLLM, an automated evaluator for assessing s…

Generalizable Federated Learning using Client Adaptive Focal Modulation
Tajamul Ashraf, Iqra Altaf Gillani
https://arxiv.org/abs/2508.10840 https://arxiv.o…

Generalizable Federated Learning using Client Adaptive Focal Modulation
Federated learning (FL) has proven essential for privacy-preserving, collaborative training across distributed clients. Our prior work, TransFed, introduced a robust transformer-based FL framework that leverages a learn-to-adapt hypernetwork to generate personalized focal modulation layers per client, outperforming traditional methods in non-IID and cross-domain settings. In this extended version, we propose AdaptFED, where we deepen the investigation of focal modulation in generalizable FL by …

Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression
Xian Qin, Xue Yang, Xiaohu Tang
https://arxiv.org/abs/2509.11870 https://

Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression
Federated Learning (FL) allows collaborative model training across distributed clients without sharing raw data, thus preserving privacy. However, the system remains vulnerable to privacy leakage from gradient updates and Byzantine attacks from malicious clients. Existing solutions face a critical trade-off among privacy preservation, Byzantine robustness, and computational efficiency. We propose a novel scheme that effectively balances these competing objectives by integrating homomorphic encr…

Beyond Gaze Overlap: Analyzing Joint Visual Attention Dynamics Using Egocentric Data
Kumushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna
https://arxiv.org/abs/2509.12419

Beyond Gaze Overlap: Analyzing Joint Visual Attention Dynamics Using Egocentric Data
Joint visual attention (JVA) provides informative cues on human behavior during social interactions. The ubiquity of egocentric eye-trackers and large-scale datasets on everyday interactions offer research opportunities in identifying JVA in multi-user environments. We propose a novel approach utilizing spatiotemporal tubes centered on attention rendered by individual gaze and detect JVA using deep-learning-based feature mapping. Our results reveal object-focused collaborative tasks to yield hi…

Faster and Better: Reinforced Collaborative Distillation and Self-Learning for Infrared-Visible Image Fusion
Yuhao Wang, Lingjuan Miao, Zhiqiang Zhou, Yajun Qiao, Lei Zhang
https://arxiv.org/abs/2509.02424

Faster and Better: Reinforced Collaborative Distillation and Self-Learning for Infrared-Visible Image Fusion
Infrared and visible image fusion plays a critical role in enhancing scene perception by combining complementary information from different modalities. Despite recent advances, achieving high-quality image fusion with lightweight models remains a significant challenge. To bridge this gap, we propose a novel collaborative distillation and self-learning framework for image fusion driven by reinforcement learning. Unlike conventional distillation, this approach not only enables the student model t…

Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation
Albert Yu, Chengshu Li, Luca Macesanu, Arnav Balaji, Ruchira Ray, Raymond Mooney, Roberto Mart\'in-Mart\'in
https://arxiv.org/abs/2508.05535

Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation
Effective robotic systems for long-horizon human-robot collaboration must adapt to a wide range of human partners, whose physical behavior, willingness to assist, and understanding of the robot's capabilities may change over time. This demands a tightly coupled communication loop that grants both agents the flexibility to propose, accept, or decline requests as they coordinate toward completing the task effectively. We apply a Mixed-Initiative dialog paradigm to Collaborative human-roBot teamin…

Tensor Invariant Data-Assisted Control and Dynamic Decomposition of Multibody Systems
Mostafa Eslami, Maryam Babazadeh
https://arxiv.org/abs/2509.11688 https://

Tensor Invariant Data-Assisted Control and Dynamic Decomposition of Multibody Systems
The control of robotic systems in complex, shared collaborative workspaces presents significant challenges in achieving robust performance and safety when learning from experienced or simulated data is employed in the pipeline. A primary bottleneck is the reliance on coordinate-dependent models, which leads to profound data inefficiency by failing to generalize physical interactions across different frames of reference. This forces learning algorithms to rediscover fundamental physical principl…