arXiv:2607. 24791v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for applying large language models (LLMs) to enterprise document corpora, yet naive implementations encounter hard limits as corpus scale and query complexity grow.
By Mishca de Costa, Muhammad Saleh Anwar, Dave Mercier, Issam Hammad
arXiv:2606. 20950v2 Announce Type: replace Abstract: Executable evaluation -- checking the consequences of an agent's actions with a program rather than grading its prose -- has become a prominent way to assess tool-using AI agents in software settings.
By Sergei Trashchenkov
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.
By David Akinpelu, Akintonde Abbas, Rereloluwa Alimi, Ayodeji Lana
arXiv:2606. 23533v2 Announce Type: replace Abstract: Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict formatting and structural rules.
By Hung Phan, Aniroop Naladala, Dubey Avanindra, Supryia Chinthavali, Lunga Dalton, Ali Jannesari
arXiv:2607. 18147v1 Announce Type: cross Abstract: Large language models (LLMs) and agentic AI systems have evolved from natural language tasks to using external tools to plan, retrieve, and act in technical domains.
By Daniela Rojas, Abdulwahab Albassam, Aidan G. Leung, Jett Ngo, Ryan Luo, Peter R. Quawas, Junpyung Kim, Kangkai Liang, Mansi Nanavati, Jonathan Mai, Meng-Chi Tsai, Yun-Tong Tsai, Yize Chen, Yuanyuan Shi
arXiv:2307. 07191v3 Announce Type: replace Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.
By Zhixian Wang, Leandro Von Krannichfeldt, Qingsong Wen, Chaoli Zhang, Liang Sun, Shirui Pan, Yi Wang