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

arXiv: https://arxiv.org/abs/2605.03328 · HTML: https://mlsanity.com/paper/2605.03328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 5 May 2026.

## Builds on (most cited first, 11 of 11)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing](https://mlsanity.com/paper/2406.00031/) (2024)
- [L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models](https://mlsanity.com/paper/2309.17446/) (2023)
- [LLM-3D Print: Large Language Models To Monitor and Control 3D Printing](https://mlsanity.com/paper/2408.14307/) (2024)
- [Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension](https://mlsanity.com/paper/2309.02465/) (2023)
- [In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: a critical review](https://mlsanity.com/paper/2404.13673/) (2024)
- [Needle in a Haystack: Detecting Subtle Malicious Edits to Additive Manufacturing G-code Files](https://mlsanity.com/paper/2111.12746/) (2021)
- [Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models](https://mlsanity.com/paper/2505.13828/) (2025)
- [Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding](https://mlsanity.com/paper/2604.13465/) (2026)
- [QoS-QoE Translation with Large Language Model](https://mlsanity.com/paper/2604.08703/) (2026)

## Abstract (arXiv)

Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse industries. Fused filament fabrication (FFF) has extended AM to laboratories, classrooms, and small production environments, but this accessibility shifts process-planning responsibility to users who may lack manufacturing expertise. A syntactically valid slicer profile can still encode thermally or geometrically harmful settings, and subtle G-code edits can alter extrusion, cooling, or adhesion before a print begins. Pre-print G-code screening catches accidental or adversarial machine-program errors before material or machine time is wasted. This paper proposes LLM-ADAM as a generalizable LLM framework for pre-print anomaly detection in AM. The framework decomposes the task into three roles: Extractor-LLM maps a G-code file to a structured process-parameter schema; Reference-LLM converts printer and material documentation into aligned operating ranges; and Judge-LLM interprets a deterministic deviation table and G-code evidence to decide whether a part is non-defective or belongs to an anomaly class. Printers, materials, and LLM backbones are interchangeable test conditions, not fixed assumptions. We evaluate the framework on an N=200 FFF G-code corpus spanning two desktop printer families, two materials, and five classes including non-defective, under-extrusion, over-extrusion, warping, and stringing. The best framework configuration reaches 87.5% accuracy, compared with 59.5% for the strongest engineered single-LLM baseline. The results show that structured decomposition, rather than backbone strength alone, is the dominant source of improvement, with defect classes identified at or near ceiling for leading configurations while residual errors concentrate on conservative false alarms for non-defective samples.