# 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`?

## Links

- arXiv: https://arxiv.org/abs/2607.02703