agent/papers/items/2026-2606-28467-an-agentic-ai-pipeline-for-appliance-level-energy-anomaly-detection-and-llm-driv.md

Paper: An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations

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type: paper title: An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations authors: Dihia Falouz, Aida Douaibia, Amine Bechar, Youssef Elmir, Abbes Amira, Adel Oulefki year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.28467 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-26 updated_at: 2026-06-26 status: queued relevance: high topics:

• agent-evaluation
• memory
• rag
• reasoning
• tool-use
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.LG
• cs.AI
• cs.CL related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 19 collection_queries: agentic-ai, rag-agent

One-line Takeaway

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

Why Collected

• matched queries: agentic-ai, rag-agent
• inferred topics: agent-evaluation, memory, rag, reasoning, tool-use, workflow-agent
• arXiv categories: cs.LG, cs.AI, cs.CL
• collection score: 19

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/2606.28467