# A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks arXiv: https://arxiv.org/abs/2510.19973 · HTML: https://mlsanity.com/paper/2510.19973/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. - Builds on 20 of its 85 references that are in the map. - First posted on arXiv 22 Oct 2025. ## Placed in - LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/) ## Builds on (most cited first, 20 of 20) - [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021) - [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025) - [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024) - [Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence](https://mlsanity.com/paper/2307.02757/) (2023) - [Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach](https://mlsanity.com/paper/2503.15764/) (2025) - [Unmasking Conversational Bias in AI Multiagent Systems](https://mlsanity.com/paper/2501.14844/) (2025) - [On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration](https://mlsanity.com/paper/2211.03088/) (2022) - [Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking](https://mlsanity.com/paper/2502.16866/) (2025) - [A Survey of Inductive Reasoning for Large Language Models](https://mlsanity.com/paper/2510.10182/) (2025) - [LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs](https://mlsanity.com/paper/2412.19811/) (2024) - [End-to-End Edge AI Service Provisioning Framework in 6G ORAN](https://mlsanity.com/paper/2503.11933/) (2025) - [MindScope: Exploring cognitive biases in large language models through Multi-Agent Systems](https://mlsanity.com/paper/2410.04452/) (2024) - [Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System](https://mlsanity.com/paper/2502.07254/) (2025) - [LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach](https://mlsanity.com/paper/2505.12247/) (2025) - [Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol](https://mlsanity.com/paper/2506.13068/) (2025) - [Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN](https://mlsanity.com/paper/2510.05255/) (2025) - [Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG](https://mlsanity.com/paper/2509.14435/) (2025) - [Toward an Unbiased Collective Memory for Efficient LLM-Based Agentic 6G Cross-Domain Management](https://mlsanity.com/paper/2509.26200/) (2025) ## Built on by (most cited first, 4 of 4) - [LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk](https://mlsanity.com/paper/2511.19175/) (2025) - [Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G](https://mlsanity.com/paper/2603.09141/) (2026) - [Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks](https://mlsanity.com/paper/2606.18272/) (2026) - [PhysAI-Bench: A Benchmark for LLM-Based Agentic Decision-Making in Autonomous UAV-Centric Physical AI](https://mlsanity.com/paper/2609.23695/) (2026) ## Abstract (arXiv) The path to higher network autonomy in 6G lies beyond the mere optimization of key performance indicators (KPIs), requiring systems that perceive and reason over the network environment as it is. This can be achieved through agentic AI, where large language model (LLM)-powered agents utilize multimodal telemetry, memory, and cross-domain negotiation to achieve multi-objective goals. However, deploying such agents introduces cognitive biases inherited from human design, which can severely distort reasoning and actuation. This paper provides a comprehensive tutorial on well-known cognitive biases, detailing their taxonomy, mathematical formulation, emergence in telecom systems, and tailored mitigation strategies. We validate these concepts through two distinct use-cases in 6G management. First, we tackle anchoring bias in inter-slice resource negotiation. To overcome the prohibitive execution delays of cloud-based LLMs, this use-case deploys a locally hosted 1B-parameter model on an RTX A4000 GPU, successfully achieving sub-second inference latencies compatible with near-real-time operations. By replacing fixed heuristic anchors with a Truncated Weibull randomized anchor strategy, the agents dismantle rigid biases, intelligently consume SLA slack, and dynamically double the system-wide energy savings (peaking at 25%) without violating strict latency limits. Second, we mitigate temporal and confirmation biases in RAN-Edge cross-domain negotiation by designing an unbiased collective memory. By integrating semantic/temporal decay and an inflection bonus that actively highlights past negotiation failures, agents are prevented from over-relying on recent data or repeating past mistakes. Grounding decisions in this richer, debiased historical context yields highly robust agreements, achieving a × 5 latency reduction and roughly 40% higher energy savings compared to memoryless baselines.