# Paper: LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing

---
type: paper
title: "LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing"
authors: Ahmadreza Eslaminia, Chuhan Cai, Cameron Smith, Ruo-Syuan Mei, Shichen Li, Rajiv Malhotra, Klara Nahrstedt, Chenhui Shao
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2605.03328
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-05-05
updated_at: 2026-05-05
status: queued
relevance: high
topics:
  - agent-evaluation
  - computer-use
  - planning
methods:
  -
benchmarks:
  -
models:
  -
datasets:
  - cs.LG
  - cs.AI
related_concepts:
  -
related_jobs:
  -
related_experiments:
  -
related_projects:
  -
collection_score: 13
collection_queries: planning-agent
---

## One-line Takeaway

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

## Why Collected

- matched queries: planning-agent
- inferred topics: agent-evaluation, computer-use, planning
- arXiv categories: cs.LG, cs.AI
- collection score: 13

## 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/2605.03328