LEAD uses online adaptive mechanisms including Potential-Scaled Instability and symmetric efficiency rewards based on correct rollouts to achieve higher accuracy-efficiency scores with substantially shorter reasoning outputs than base models on math benchmarks.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Excess risk decomposes into independent alignment (trace of inverse average Hessian times gradient covariance) and curvature terms, so both flatness and gradient alignment are required; SAGE achieves this and sets new SOTA on DomainBed.
A CTM-GNN model with EnSRF assimilation and flow-weighted transition matrix fuses floating car data and camera observations to deliver physically consistent, network-wide traffic volume estimates and forecasts, demonstrated with improved accuracy in Manhattan.
citing papers explorer
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LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models
LEAD uses online adaptive mechanisms including Potential-Scaled Instability and symmetric efficiency rewards based on correct rollouts to achieve higher accuracy-efficiency scores with substantially shorter reasoning outputs than base models on math benchmarks.
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Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning
Excess risk decomposes into independent alignment (trace of inverse average Hessian times gradient covariance) and curvature terms, so both flatness and gradient alignment are required; SAGE achieves this and sets new SOTA on DomainBed.
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Harnessing Floating Car Data, Traffic Camera Observations, and Network Flow Analysis for Traffic Volume Estimation
A CTM-GNN model with EnSRF assimilation and flow-weighted transition matrix fuses floating car data and camera observations to deliver physically consistent, network-wide traffic volume estimates and forecasts, demonstrated with improved accuracy in Manhattan.