AdaGATE improves evidence F1 scores on HotpotQA for multi-hop RAG under clean, redundant, and noisy conditions by framing selection as gap-aware token-constrained repair, outperforming baselines while using 2.6x fewer tokens.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
NWCAD uses a two-stream setup with a two-stage gate to prevent accuracy drops on baseline-correct items under non-informative contexts while retaining gains from helpful contexts.
TASR provides a training-free predicate that stops iterative retrieval on repeated normalized answers plus calibrated logit margin above 0.25, retaining 94.8% of fixed-k=5 F1 at 62.6% of the calls across 32 configurations.
citing papers explorer
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AdaGATE: Adaptive Gap-Aware Token-Efficient Evidence Assembly for Multi-Hop Retrieval-Augmented Generation
AdaGATE improves evidence F1 scores on HotpotQA for multi-hop RAG under clean, redundant, and noisy conditions by framing selection as gap-aware token-constrained repair, outperforming baselines while using 2.6x fewer tokens.
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No-Worse Context-Aware Decoding: Preventing Neutral Regression in Context-Conditioned Generation
NWCAD uses a two-stream setup with a two-stage gate to prevent accuracy drops on baseline-correct items under non-informative contexts while retaining gains from helpful contexts.
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TASR: Training-Free Adaptive Stopping for Iterative Retrieval
TASR provides a training-free predicate that stops iterative retrieval on repeated normalized answers plus calibrated logit margin above 0.25, retaining 94.8% of fixed-k=5 F1 at 62.6% of the calls across 32 configurations.