While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large language models (LLMs) and vision language models (VLMs), opens the possibility of teams that infer mission-relevant semantics and subtasks given high-level natural language spec...
Zachary Ravichandran, Fernando Cladera, Ankit Prabhu et al.· 0 citations
Agentic artificial intelligence is a candidate enabler of Level-4 autonomy in sixth-generation (6G) networks, but agents reasoning over a shared memory inherit its distortions. We study cross-domain radio access network (RAN)--edge orchestration in which a RAN agent minimizing energy and an edge agent minimizing latenc...
Hatim Chergui, Farhad Rezazadeh, Miguel Catalan Cid et al.· 0 citations
Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to date focus on gene-level information. We present one of the first systematic frameworks to generate genetic variant-level embeddings across the entire human genome. Using curated...
Hongqian Niu, Jordan Bryan, Jacob Williams et al.· 0 citations
A novel instrumental variable estimator is developed that accommodates multivariate outcomes, sparse networks, and multidimensional latent homophily and is shown to be $\sqrt{N}$-consistent and asymptotically normal under sparsity conditions that relax dense-network assumptions prevalent in the peer effect literature.
Shanjukta Nath, Jiwon Hong, Jae Ho Chang et al.· 0 citations
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Cross-chain bridges enable asset and state transfers across heterogeneous blockchains, but their complex cross-domain interactions introduce new attack surfaces that are difficult to monitor using traditional single-chain analysis methods. Existing approaches often focus on isolated on-chain behaviors and fail to captu...
Dan Lin, Shunfeng Lu, Ziyan Liu et al.· 0 citations
Large Language Models (LLMs) have been widely adopted in commercial code completion engines, significantly enhancing coding efficiency and productivity. However, even functionally correct LLM-generated code may exhibit non-functional quality issues that violate coding standards and best practices, such as poor style an...
Liang Lu, Yuan Jiang, Christoph Treude· 0 citations
Addressing the challenge of ensuring safety in ever-changing and unpredictable environments, particularly in the swiftly advancing realm of autonomous driving in today's 5G wireless communication world, we present Navigation Secure (NavSecure). This vision-based navigation framework merges the strengths of world models...
Hong Ding, Ziming Wang, Yi Ding et al.· 0 citations
In professional life sciences workflows, scientists routinely interpret visual artifacts (gel blots, microscopy images, plasmid maps, flow cytometry plots, molecular structures, ...) to inform research decisions. We introduce VIALS, a visual question-answering benchmark with 161 such interpretation tasks, spanning the...
Elaine Lau, Thanuka Udumulla, Lee Izhaki-Tavor et al.· 0 citations
It is suggested that voting-based alignment cannot deliver fair or transparent AI by aggregation alone; at minimum, each stage of the moral AI elicitation pipeline should be audited and disclosed.
Taenyun Kim, E. Bogucka, Daniele Quercia· 0 citations
Biological mechanisms morphologically and functionally correspondent to output-weight interconnections are identified, supporting the in-principle neural realizability of SIDPP and motivating Conjecture T: human neural systems may realize a functional architecture relevantly similar to that of the Transformer.
Marco Giunti, Fabrizia Giulia Garavaglia· 0 citations
Reinforcement learning (RL) is commonly employed to enhance the performance of autonomous systems, including the Autonomous Internet of Things (AIoT). However, the trial-and-error nature of RL, when conducted in real-world environments, is costly and hazardous in some scenarios. Consequently, the majority of RL researc...
Rongping Zhou, Omid Tavallaie, Shuaijun Chen et al.· 0 citations
The main objective of this paper is to propose a general framework for prediction based on different sources of multimodal data in the healthcare domain. We evaluated whether frozen medical large language model (LLM) representations can serve as a shared embedding space for multimodal primary diagnosis category predict...
Chengyuan Liu, Xinyue Zhang, Yao Li et al.· 0 citations
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
