[Submitted on 6 Aug 2026]
Title:Summarize, Judge, Refine: Decoupled Content Understanding and Policy Learning for Multimodal Content Moderation
View a PDF of the paper titled Summarize, Judge, Refine: Decoupled Content Understanding and Policy Learning for Multimodal Content Moderation, by Zeeshan Ahmed and 2 other authors
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Abstract:Content moderation systems traditionally entangle multimodal understanding with policy-specific classification, requiring full pipeline retraining for every policy change and suffering from label scarcity since multimedia cannot be meaningfully augmented. We propose Summarize-Judge-Refine (SJR), a two-model architecture that decouples these concerns via a natural language interface: a multimodal Content Model produces structured text summaries, and a text-only Policy Model classifies them against policy definitions. An iterative co-training loop refines the Content Model via GRPO to produce policy-relevant summaries, while text-space augmentation generates adversarial summary variants---an augmentation pathway impossible on raw multimedia---enabling few-shot policy bootstrap. Every decision is grounded in a human-readable summary, providing interpretability as a structural byproduct. On misleading advertisement detection, SJR achieves +23.6\% relative non-misleading F1 over a zero-shot chain-of-thought baseline, outperforming end-to-end SFT, STaR/RFT, and RLFT. Notably, a variant trained on zero real violating examples---with all positive-class data synthetically generated---matches the full-data model within 0.2\% relative on violating F1, demonstrating that new policies can launch without any real violation data.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2609.22094 [cs.CL]
(or arXiv:2609.22094v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.22094
arXiv-issued DOI via DataCite
Submission history
From: Zeeshan Ahmed [view email] [v1] Thu, 6 Aug 2026 23:28:56 UTC (36 KB)
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