CAT-GS: Balanced Multimodal Learning via Calibrated Gating and Fusion Surgery

The paper introduces CAT‑GS, a training controller that stabilizes multimodal neural networks by addressing three failure modes: modality imbalance, unstable gating, and fusion interference. CAT‑GS calibrates teacher-derived reliability, applies a margin‑thresholded gating policy, caps gradient budgets, and uses fusion‑only PCGrad, all without altering model architectures or losses. Experiments on audio‑visual, tri‑modal, synthetic, and cross‑domain benchmarks show that CAT‑GS matches or surpasses strong imbalance‑aware baselines while producing smoother gating and fewer conflicting fusion gradients.

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• multimodal
• benchmarks