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

---
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
