# Multi-Adapter PPO: A Cross-Attention Enhanced Wavelength Selection Framework for LIBS Quantitative Analysis

arXiv: https://arxiv.org/abs/2606.17476 · HTML: https://mlsanity.com/paper/2606.17476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [A Review of Feature Selection Methods Based on Mutual Information](https://mlsanity.com/paper/1509.07577/) (2015)
- [Learning to Decide with Just Enough: Information-Theoretic Context Summarization for CMDPs](https://mlsanity.com/paper/2510.01620/) (2025)
- [R3D: Regional-guided Residual Radar Diffusion](https://mlsanity.com/paper/2601.06465/) (2026)
- [Revisiting the Scale Loss Function and Gaussian-Shape Convolution for Infrared Small Target Detection](https://mlsanity.com/paper/2604.09991/) (2026)
- [Golden RPG: Confidence-Adaptive Region-Aware Noise for Compositional Text-to-Image Generation](https://mlsanity.com/paper/2604.25314/) (2026)

## Built on by (most cited first, 1 of 1)

- [A Standard Processing Pipeline for High-accuracy Measurement of Few-shot Regression on Laser Induced Breakdown Spectroscopy](https://mlsanity.com/paper/2606.21960/) (2026)

## Abstract (arXiv)

Laser-induced breakdown spectroscopy (LIBS) quantitative analysis faces critical challenges in wavelength selection due to high-dimensional spectral data and the fundamental trade-off between prediction accuracy and feature efficiency. This paper presents a novel Multi-Adapter PPO framework that transforms wavelength selection into a reinforcement learning problem, leveraging cross-attention mechanisms and multiple specialized adapters to capture complex spectral relationships. Our approach outperforms traditional Particle Swarm Optimization (PSO) by an average of 28.4% in comprehensive score and 45.2% in prediction accuracy across steel and coal datasets. The proposed method demonstrates superior performance in balancing prediction accuracy with feature efficiency, achieving state-of-the-art results in LIBS quantitative analysis while maintaining interpretability and computational efficiency. We released our code and dataset here: https://github.com/Hflying/MAPPO