Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races
arXiv:2608.01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma. Each company can develop slowly and safely, or move faster while taking a risk that may remove its final reward. We use this repeated game to study strategic safety behaviour among large language model (LLM) agents in races with two to five players. However, a valid action does not show that an agent understands the game. We therefore place an audit gate before behavioural interpretatio
Record details
Published: 4 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Agents & autonomy · Transparency
Retrieved: 4 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Hybrid AI for Explainable and Accurate Conversational Agents in eGovernment
arXiv cs.CY · 4 August 2026
WeClawArena: An Auditable Sandbox and Benchmark for Cross-User Agents Collaboration and Security in Human-Centered Agent Networks
arXiv · 4 August 2026
WeClawArena: An Auditable Sandbox and Benchmark for Cross-User Agents Collaboration and Security in Human-Centered Agent Networks
HuggingFace Daily Papers · 3 August 2026
Accountability Asymmetry and Structural Trust in Autonomous AI Systems
arXiv · 4 August 2026
CARE-Bench: Benchmarking Patient-Facing LLM Triage
arXiv cs.AI · 4 August 2026
Agentic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce
arXiv cs.AI · 3 August 2026
How to cite this record
ethics.ai (4 August 2026), “Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races,” evidence record 15833, https://ethics.ai/record/15833 (originally published by arXiv cs.CY).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.