RAPS-DA improves RAG robustness to heterogeneous knowledge conflicts by training regime-specific peer specialists with hard routing and a dual-layer token selector for focused supervision.
Confidence- aware multi-teacher knowledge distillation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A quantized INT8 SISR model with extract-refine-upsample design and teacher-guided three-stage training achieves 29.79 dB PSNR and 0.8634 SSIM on the MAI 2026 Quantized 4K challenge under mobile INT8 constraints.
citing papers explorer
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Regime-Aware Peer Specialization for Robust RAG under Heterogeneous Knowledge Conflicts
RAPS-DA improves RAG robustness to heterogeneous knowledge conflicts by training regime-specific peer specialists with hard routing and a dual-layer token selector for focused supervision.
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Efficient INT8 Single-Image Super-Resolution via Deployment-Aware Quantization and Teacher-Guided Training
A quantized INT8 SISR model with extract-refine-upsample design and teacher-guided three-stage training achieves 29.79 dB PSNR and 0.8634 SSIM on the MAI 2026 Quantized 4K challenge under mobile INT8 constraints.