Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks
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
Record details
Published: 5 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Agents & autonomy · Environment
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (5 June 2026), “Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks,” evidence record 1376, https://ethics.ai/record/1376 (originally published by arXiv).
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