agent/papers/items/2026-2605-03328-llm-adam-a-generalizable-llm-agent-framework-for-pre-print-anomaly-detection-in-.md

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

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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