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arXiv Machine Learning By Aleesha Zainab, Asifullah Khan, Muhammad Ahmed Khalid, Faheem Ullah Khan

A Channel-Boosted Multi-Agent System with Iterative Consultation for Document Sensitivity Classification

Read the original on arXiv Machine Learning →

The paper introduces a Channel‑Boosted Multi‑Agent System (CB‑MAS), instantiated as IC‑MAS, to classify document sensitivity without the input‑length truncation problem of standard transformers. IC‑MAS uses a Channel Critic Agent to adaptively weight two first‑window encoders and Consultation Agents to exchange belief states, achieving 90.72% accuracy and 91.23% F1‑score while reducing computation by ~54% compared to a fixed‑round baseline. The authors provide explainability via LIME/SHAP, multi‑agent evaluation, and a transparent discussion of limitations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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