arXiv (All)
open access
LLMs Trust Their Own: Identity-Dependent Conformity in Multi-Agent Systems
Large language models (LLMs) are increasingly deployed in multi-agent settings, where agents observe and influence one another, making social influence a key dimension of AI behavior and safety. We investigate whether LLMs' responses depend on the social identity of other agents, beyond the effect o
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems#1130770
- Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI#820557
- The Large Silence Model (LSM): A Theoretical Framework for the Pre-Linguistic Ground of Intelligence and the Structural Limits of Language-Based AI#244921
- Bridging LLM Agents and Data Spaces: An Architectural Mediation Approach using the Model Context Protocol#1108878
- Bridging LLM Agents and Data Spaces: An Architectural Mediation Approach using the Model Context Protocol#1104540
Record · ID 1118207
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive