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Empirical assessment of ChatGPT's answering capabilities in natural science and engineering
arXiv:2309.10048v2 Announce Type: replace Abstract: ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to greatly impact society, research, and education. An essential step to understand ChatGPT's expected impact is to study its domain-specific answering capabilities.
Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering
Computer Science > Software Engineering [Submitted on 20 Jan 2026] Title:Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering View PDF HTML (experimental)Abstract:LLM-based Multi-Agent (LLM-MA) systems are increasingly applied to automate complex software engineering tasks such as requirements engineering, code generation, and testing.
Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation
Announce Type: replace Abstract: Structural modeling is a fundamental component of computational engineering science, in which even minor physical inconsistencies or specification violations may invalidate downstream simulations. The potential of large language models (LLMs) for automatic generation of modeling code has been demonstrated. However, non-executable or physically inconsistent outputs remain prevalent under stringent engineering constraints.
Going supersonic! NASA's X-59 jet breaks sound barrier for the 1st time
NASA's X-59 jet breaks sound barrier for the 1st time NASA's X-59 jet has finally gone supersonic. The X-59, a long-nosed demonstrator designed to help develop the tech required for "quiet" supersonic flight, notched the milestone on Friday (June 5), more than six months after getting off the ground for the first time. "The X-59’s first supersonic flight is a testament to America's enduring leadership in science, engineering and aerospace innovation," Michael Kratsios, assistant to the...
EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
arXiv:2604.17406v3 Announce Type: replace Abstract: The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. While the scientific method is inherently iterative, existing agent frameworks are predominantly static, narrowly scoped, and lack the capacity to learn from trial and error. To bridge this gap, we present EvoMaster, a foundational evolving agent framework engineered specifically for Agentic Science at Scale.
Optimal Wiener-Filter Solutions for Denoising of Graph Signals on Directed Graphs
Electrical Engineering and Systems Science > Signal Processing [Submitted on 5 Jun 2026] Title:Optimal Wiener-Filter Solutions for Denoising of Graph Signals on Directed Graphs View PDF HTML (experimental)Abstract:Graph signal processing has opened new avenues to the canonical denoising problem in interesting settings.
ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring
Electrical Engineering and Systems Science > Image and Video Processing [Submitted on 4 Jun 2026] Title:ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring View PDF HTML (experimental)Abstract:We introduce ErA (Error-Aware Deep Unrolling Network), an end-to-end frame work for single-image defocus deblurring.
Efficient Multi-Agent Optimization of Optical Power in S+C+L-Band Systems
Electrical Engineering and Systems Science > Systems and Control [Submitted on 4 Jun 2026] Title:Efficient Multi-Agent Optimization of Optical Power in S+C+L-Band Systems View PDF HTML (experimental)Abstract:We propose an AI Agent tailored for link power management in multi-band systems. In S+C+L band span-level study, the agent efficiently solves various optimization objectives.
Unsupervised Learning Based Focal Stack Camera Depth Estimation
Electrical Engineering and Systems Science > Image and Video Processing [Submitted on 14 Mar 2022 (v1), last revised 3 Jun 2026 (this version, v3)] Title:Unsupervised Learning Based Focal Stack Camera Depth Estimation View PDFAbstract:We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.
MPC for nonlinear systems: a comparative review of discretization methods
Electrical Engineering and Systems Science > Systems and Control [Submitted on 4 Jun 2026] Title:MPC for nonlinear systems: a comparative review of discretization methods View PDF HTML (experimental)Abstract:This work provides a comparative review of three different numerical methods generally used to discretize continuous-time non-linear equations appearing in model predictive control problems: direct multiple shooting, direct collocation and successive linearizations. An overview of the...