Agreement Between Large Language Models and Humans in Research Proposal Review — Data and Code This repository contains the data and code required to reproduce the analyses, statistical results, and figures presented in the associated manuscript. Files are organized by function and described below. All research proposa...
Christopher A. Gorski· Zenodo (CERN European Organi...· 0 citations
Conversational language model systems persist user constraints, preferences, identity, commitments, and assigned roles across turns. The dominant memory architecture stores this alongside ordinary episodic content in a vector index and retrieves the top-k items by similarity to the active query. We argue that this desi...
Oghenenefe Abeke, Rasheed Mohammad, H. Mahmoud· International Journal of Dat...· 0 citations
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
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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
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