Paper: LLMoxie: Exploring Agentic AI for Scientific Software Development

type: paper title: "LLMoxie: Exploring Agentic AI for Scientific Software Development" authors: Landung Setiawan, Anant Mittal, Cordero Core, Anshul Tambay, Carlos Garcia Jurado Suarez, David A. C. Beck, Andrew J. Connolly, Vani Mandava year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.02703 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-02 updated_at: 2026-07-02 status: queued relevance: high topics:

agent-evaluation
coding-agent
planning
reasoning
workflow-agent methods:

benchmarks:

models:

datasets:

cs.SE
cs.AI
cs.DC
cs.MA related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 18 collection_queries: agentic-ai, coding-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

matched queries: agentic-ai, coding-agent
inferred topics: agent-evaluation, coding-agent, planning, reasoning, workflow-agent
arXiv categories: cs.SE, cs.AI, cs.DC, cs.MA
collection score: 18

Review Checklist

Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
Does it include a benchmark, dataset, code, or reproducible experimental setup?
Should it be promoted from queued to skimmed or summarized?

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