LLMoxie: Exploring Agentic AI for Scientific Software Development
In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents. Layered on top, an open-source RSE-Plugins ecosystem encodes accumulated RSE knowledge as a Plugin-Agent-Skill hierarchy spanning scientific Python practice, domain-specific knowledge, a six-phase resea
Record details
Published: 2 July 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
arXiv · 2 July 2026
Language Models as Measurement Apparatus for Culture
arXiv cs.CL (ethics-relevant NLP) · 2 July 2026
Automated Data Readiness for Scientific AI
arXiv · 2 July 2026
Understanding Agent-Based Patching of Compiler Missed Optimizations
arXiv · 2 July 2026
Copewell: A Multi-Agent Swarm Architecture for Equitable Mental Wellness Support
arXiv · 2 July 2026
CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation
arXiv · 2 July 2026
How to cite this record
ethics.ai (2 July 2026), “LLMoxie: Exploring Agentic AI for Scientific Software Development,” evidence record 303, https://ethics.ai/record/303 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.