arXiv:2606.18272v1 Announce Type: cross Abstract: This paper presents an autonomous agentic resource negotiation framework designed to enable zero-touch network slicing in 6G architectures using Large Language Model (LLM) agents. While LLMs offer powerful reasoning capabilities, we demonstrate that such agents inherently suffer from anchoring bias, rigidly adhering to initial heuristic proposals and causing severe network over-provisioning. To systematically mitigate this cognitive bias, we propose a novel randomized anchoring strategy modeled via a Truncated 3-Parameter Weibull distribution.
The rapid advancement and deployment of Large Language Models (LLMs) are highlighting inherent biases that need mitigation for their effective integration into critical infrastructure like 6G networks.
This research addresses a fundamental limitation of LLM-based autonomous agents, crucial for reliable and efficient operation of future interconnected systems, preventing significant resource waste.
The ability to mitigate anchoring bias could accelerate the adoption of LLM-based agents in sensitive applications, moving from theoretical capability to practical, robust deployment with reduced operational flaws.
- · Telecommunications companies
- · AI agent developers
- · Network infrastructure providers
- · Providers of non-adaptive heuristic systems
- · Networks reliant on static resource allocation
LLM-based agents become more trustworthy and efficient in managing complex systems.
Accelerated deployment of autonomous, 'zero-touch' networks, reducing operational costs and human intervention.
New standards and protocols emerging for bias mitigation in AI-driven critical infrastructure.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI