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636 papers
This work proposes a model-based approach that optimizes model parameters, and evaluates first- and best-improvement algorithms, simulated annealing (SA), threshold accepting (TA), and two novel algorithms utilizing directional derivatives (dd) to guide exchanges.
This paper presents an empirical evaluation of Large Language Models (LLMs) for automated model-based test generation, compared with a state-of-the-art model-based testing tool (GraphWalker) and its built-in algorithms (random and quick random for edge and vertex coverage settings).
The work identifies five distinct stages of the documentation review process: self review, technical review, editorial review, play testing, and post-publication feedback, and draws on practitioners with distinct expertise to address quality across content, presentation, and user experience.
It is found that functional correctness alone is insufficient to assess the operational reliability of LLM-generated software before deployment in continuously running environments, and aging trends can also emerge in manually developed implementations.
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The positive results of ISIT counseling on psychological distress symptoms and reducing perinatal grief during follow-ups conducted immediately post-intervention and 3 months after the intervention suggest that it will be useful for managing the complications of spontaneous abortion.
A novel basis expansion approach is proposed to fit a variable‐domain functional regression model, which assumes the functional nature of the covariate (daily steps) and incorporates the sample data into the model without alignment to enrich the interpretability of the model.
AI-assisted development tools enable software engineers to generate implementations at substantially higher speed and volume than in traditional workflows, yet relatively little is known about how existing guardrails evolve in response.
This work presents the first characterization study of open-source satellite software, examining its ecosystem and development practices, revealing a highly heterogeneous and task-specialized ecosystem with 66 programming languages and diverse implementation strategies.
RTL-Obliger is presented, a neuro-symbolic framework that infers implicit security obligations of register-transfer-level RTL in a functionality-preserving two-stage generation and raises mean all-pass rates.
PLCBENCH is presented, to the authors' knowledge, the first real-PLC hardware-in-the-loop (HIL) framework for characterizing this cyber-to-physical capability and its boundaries and it combines vendor-native interaction, commercial PLC execution, closed-loop reduced-order process simulation, and independent outcome verification.
An entity-only external interface with task-conditioned relation materialization during inference with two-layer index separates global routing from local entity focus is proposed and evaluated on DeepSeek-V4-Flash and SWE-bench Verified.
Analysis of patterns of knowledge and use of medicinal plants and biomedical medicines among migrants in an urban setting contributes to understanding how medicinal plant knowledge and therapeutic practices are maintained or transformed in urban migrant contexts.
In his latest book, Professor Eugene Fitzgerald examines the forces that turn breakthroughs into value — and why innovation resists simple formulas.
MIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
Our latest voice model has improved precision and lower latency to make voice interactions more fluid, natural and precise.
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