Background: Young people spend hours on social media every day, where following influencers is the norm. Influencers post frequently about alcohol and alcohol brands and there has been a rise in influencer marketing. However, prevalence estimates rely on manual analyses of only a small number of posts. We used a large...
Jack Delmenico, Dan Anderson‐Luxford, Emmanuel N. Kuntsche et al.· 0 citations
Agentic artificial intelligence systems differ from conventional language model applications because they can reasonover a task, select tools, invoke external systems, observe resulting state, and continue acting toward a goal. Thisaction loop creates a security and reliability problem that cannot be adequately describ...
Elizabeth Waeni Mutisya· Zenodo (CERN European Organi...· 0 citations
The rapid proliferation of Generative AI (GenAI) has catalyzed the emergence of autonomous agentic AI services, spanning large language model (LLM) or vision-language model (VLM) based digital agents to vision-language-action (VLA) based embodied intelligence. Consequently, tokens and multimodal data streams have emerg...
Cheng-Xiang Mi, Ce Wang, Kai Zhang et al.· Proceedings of the 2026 Inte...· 0 citations
Existing ns3/AI bridges target numeric reinforcement learning (RL) pipelines and cannot handle the text-centric prompt/response exchange, structured output validation, and multi-node orchestration that large language models (LLMs) require. We introduce ns3-GenAI, an open framework that augments the ns3 shared-memory in...
Su-Bin Han, Junkyu Hong, Sangheon Pack· Proceedings of the 2026 Inte...· 0 citations
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Agentic AI systems composed of multiple large-language-model (LLM) agents need verifiable agent identity, tamper-evident records of completed work, and machine-to-machine settlement. Existing agent-economy designs concentrate on registration, discovery and reputation, while consortium-chain notarisation services anchor...
Ying Willam ZHANG· Zenodo (CERN European Organi...· 0 citations
Abstract: The safety of autonomous artificial intelligence systems relies on deterministic engineering boundaries superseding natural-language instructions or heuristic prompting. Documented containment failures in frontier models—including privilege escalation and strategic compliance—stem from architectural vulnerabi...
Josie Jefferson, Felix Velasco· Zenodo (CERN European Organi...· 0 citations
Replication package for a double-anonymous conference submission on rewriting natural-language requirements with large language models. It contains the prompts, the pipeline, the raw experimental outputs and the analysis that reproduces every number and figure in the paper. No API credentials are needed to check the re...
Anonymous· Zenodo (CERN European Organi...· 0 citations
Reproduction package (version 4) for the article "Knowledge-Guided Synthesis of No-Code Dispatching Rules with Large Language Models: Structural Invariants, Execution, and Feedback for Smart-Factory Material Control". It contains the synthesis pipeline (prompt builder, tool schema, invariant validator, lint checks), th...
anonymous, Kyungeun Kim, Subin Lee et al.· Zenodo (CERN European Organi...· 0 citations
Step (v) of the proof of Theorem 1.10 in Mochizuki's Inter-universal Teichmüller theory IV bounds, for each tuple of places, the log-volume of the corresponding component of the holomorphic hull of the union of possible images of a Θ-pilot object defined in Corollary 3.12 of the third paper. This note gives an explicit...
Toshihisa Urahigashi· Zenodo (CERN European Organi...· 0 citations
(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Language agents that keep memories across sessions usually discount a record by how long ago it was written or last read. That discount was introduced to decide what an agent attends to, but it is increasingly read as a statement about whether a record is still tru...
Pranay M. Mahendrakar· Zenodo (CERN European Organi...· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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