{
  "id": 7625,
  "url": "https://arxiv.org/abs/2603.05839v1",
  "title": "Evaluating LLM Alignment With Human Trust Models",
  "summary": "Trust plays a pivotal role in enabling effective cooperation, reducing uncertainty, and guiding decision-making in both human interactions and multi-agent systems. Although it is significant, there is limited understanding of how large language models (LLMs) internally conceptualize and reason about trust. This work presents a white-box analysis of trust representation in EleutherAI/gpt-j-6B, using contrastive prompting to generate embedding vectors within the activation space of the LLM for dia",
  "authors": "Anushka Debnath, Stephen Cranefield, Bastin Tony Roy Savarimuthu, Emiliano Lorini",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-06T02:49:49.000Z",
  "fetched_at": "2026-07-14T16:33:21.050Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7625",
  "original_url": "https://arxiv.org/abs/2603.05839v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}