Hardware-verifiable Recursive Admissibility for Autonomous Aerospace and Mechanical Systems: A Sexa Framework for Constraint-safe State Selection, Fault Detection, Digital-twin Reconstruction, Recursive Dimensional Compression, and Engineering-grade Computational Validation

Jered Mcclain, Erydir Ceisiwr, SEXA Institute of Technology and Interdomain Galactic Advisors, United States of America

ABSTRACT

Autonomousaero space and mech anical systems require supervisory architecture scapable of determining whether candidate operating states remain mutually consistent before execution. This paper evaluates a hardware-verifiable recursive admissibility architecture derived from the SEXA computationalframework using a controlled mechanical digital twin. Ten state variables are evaluated through six sequential Γadmissibility stages against nominal trajectories and ten controlled fault classes. Parameters were calibrated on separate nominal trajectories and frozen before held-out testing. Thefinal benchmark comprised 1,230,000 mechanical-state evaluations and achieved 97.57% classification accuracy, 95.70% fault sensitivity, 99.59% nominal-state acceptance, and a 4.30% false-admission rate. An independent scalar-threshold baseline produced an 85.55% false-admission rate. The results support recursive admissibility as a reproducible computational engineering constraint architecture under the synthetic conditions tested while preserving explicit boundaries between computational validation and physical hardware or flight validation.

Keywords

Recursive Admissibility, Mechanical Digital Twin, Fault Detection, Autonomous Aerospace Systems, Hardware Verification.

Integrated Evaluation of a Socratic Conversational Agent for Introductory Programming: A Human-Centered Design Study at the University of Costa Rica

Hansell Solís Ramírez and Gustavo López, University of Costa Rica, Costa Rica

ABSTRACT

In programming, large language models that deliver complete solutions endanger students’ reasoning instead of supporting it. This paper reports the human-centered design and integrated evaluation of a Socratic conversational agent for introductory programming at the University of Costa Rica. Twenty students participated by solving a timed Python task and five evaluations were triangulated: perceived usability (SUS), cognitive load (NASA-TLX), interaction logs(411 prompts), an audit of fidelity to the system prompt (374 responses), and a focus group. The agent proved learnable and accepted (SUS = 55.75; moderate load).

Keywords

Socratic tutoring, Pedagogical scaffolding, Large language models, Usability, Cognitive load.