NvRec profiles multiple API recommendation models on tail-API performance and applies majority voting with reliability filters to raise true accept rates while controlling rejection of uncertain outputs.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.SE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Continuous latent-vector compression improves BLEU scores on repository-level code tasks by up to 28.3% at 4x compression while cutting inference latency.
citing papers explorer
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Tail-aware N-version Machine Learning Models for Reliable API Recommendation
NvRec profiles multiple API recommendation models on tail-API performance and applies majority voting with reliability filters to raise true accept rates while controlling rejection of uncertain outputs.
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On the Effectiveness of Context Compression for Repository-Level Tasks: An Empirical Investigation
Continuous latent-vector compression improves BLEU scores on repository-level code tasks by up to 28.3% at 4x compression while cutting inference latency.