agent/papers/items/2026-2606-18272-mitigating-anchoring-bias-in-llm-based-agents-for-energy-efficient-6g-autonomous.md

Paper: Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks

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type: paper title: Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks authors: Hatim Chergui, Claudia Carballo González, Farhad Rezazadeh, Merouane Debbah year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.18272 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-05 updated_at: 2026-06-18 status: queued relevance: high topics:

• agent-safety
• computer-use
• multi-agent
• reasoning methods:

benchmarks:

models:

datasets:

• cs.NI
• cs.AI
• eess.SY related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 13 collection_queries: autonomous-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: autonomous-agent-llm
• inferred topics: agent-safety, computer-use, multi-agent, reasoning
• arXiv categories: cs.NI, cs.AI, eess.SY
• collection score: 13

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.18272