How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?
arXiv:2606.26346v1 Announce Type: new Abstract: Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall. This is a critical gap for a sector that requires live data retrieval, specialized regulatory and market knowledge, and multi-step quantitative reasoning under real-world constraints. We present an empirical study of tool-augmented LL
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Computer Science > Artificial Intelligence arXiv:2606.26346 (cs) [Submitted on 24 Jun 2026] Title:How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks? Authors:David Akinpelu, Akintonde Abbas, Rereloluwa Alimi, Ayodeji Lana View a PDF of the paper titled How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?, by David Akinpelu and 2 other authors View PDF HTML (experimental) Abstract:Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall.
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