A Channel-Boosted Multi-Agent System with Iterative Consultation for Document Sensitivity Classification
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Computer Science > Computation and Language
arXiv:2609.22212 (cs)
[Submitted on 2 Sep 2026]
Title:A Channel-Boosted Multi-Agent System with Iterative Consultation for Document Sensitivity Classification
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Abstract:Organizations in critical national infrastructure sectors must assess heterogeneous documents for sensitivity before routing or storage. Manual assessment is slow, inconsistent, and unscalable. Extending our prior leakage-controlled benchmark, BERT established the top single-encoder baseline (89.14% accuracy, 89.33% F1-score under 5-fold cross-validation on the Strategic 16K corpus). However, transformer baselines suffer from a structural limitation: fixed input length truncation discards evidence beyond the retained window-precisely where sensitive cables tend to be longest. We present Channel-Boosted MAS (CB-MAS) and instantiate it as IC-MAS (Iterative Consultation Multi-Agent System) to solve this without long-context computational costs. A Channel Critic Agent learns document-adaptive trust weights governing Gated Channel Boosting between two first-window encoders, while paired Consultation Agents iteratively exchange belief states to reconcile evidence from the beginning and end of long documents. IC-MAS holds computation constant regardless of document length by reconciling fixed windows in a compact representation space. Ablation studies show critic-controlled Channel Boosting provides the bulk of accuracy gains, while consultation recovers recall without precision collapse. Critic-Controlled Gated Channel Boosting with Max-Pool fusion and Blackboard Adaptive Consultation achieves 90.72% accuracy, 91.23% F1-score, 92.01% sensitive recall, and 90.46% sensitive precision, using about 54% less average computation than a fixed-round baseline. Gains over the single-encoder baseline are statistically significant (McNemar's test, p less than 0.000001; paired t-test). We include LIME/SHAP explainability, multi-agent evaluation, and an honest accounting of limitations.
| Comments: | 30 pages , 12 figures |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA) |
| MSC classes: | 68T50 (Primary) 68T42, 68P27 (Secondary) |
| Cite as: | arXiv:2609.22212 [cs.CL] |
| (or arXiv:2609.22212v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.22212
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled A Channel-Boosted Multi-Agent System with Iterative Consultation for Document Sensitivity Classification, by Aleesha Zainab and 3 other authors
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