Starfield uses a traffic-derived Riemannian metric on the satellite shell to select demand-aware ISLs, yielding up to 30% fewer hops and 15% better stretch than grid topologies in Starlink Phase 1 simulations.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Learned static functions combine per-key ML-predicted prefix codes with classic static function storage to compress static key-value mappings beyond zero-order entropy limits.
MDCNS is a multi-source negative sampling framework for sequential recommendation that uses peer and teacher models plus divergence and consensus mechanisms to improve diversity and avoid local optima.
LLM-generated code matches human-written code in overall readability but exhibits different issue patterns, and prompt engineering has limited impact on improving it.
CDPR uses an intuition pathway for cross-modal consensus and a reasoning pathway for quantifying and mitigating inconsistencies to improve multimodal intent recognition.
citing papers explorer
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Starfield: Demand-Aware Satellite Topology Design for Low-Earth Orbit Mega Constellations
Starfield uses a traffic-derived Riemannian metric on the satellite shell to select demand-aware ISLs, yielding up to 30% fewer hops and 15% better stretch than grid topologies in Starlink Phase 1 simulations.
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Learned Static Function Data Structures
Learned static functions combine per-key ML-predicted prefix codes with classic static function storage to compress static key-value mappings beyond zero-order entropy limits.
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Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation
MDCNS is a multi-source negative sampling framework for sequential recommendation that uses peer and teacher models plus divergence and consensus mechanisms to improve diversity and avoid local optima.
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The Readability Spectrum: Patterns, Issues, and Prompt Effects in LLM-Generated Code
LLM-generated code matches human-written code in overall readability but exhibits different issue patterns, and prompt engineering has limited impact on improving it.
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Mitigating Multimodal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition
CDPR uses an intuition pathway for cross-modal consensus and a reasoning pathway for quantifying and mitigating inconsistencies to improve multimodal intent recognition.