Best-of-N

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling
arXiv:2609.19499 — For self-consistency, RASC, ESC, and best-of-N, candidate count N is not enough. Batching the same N can change A100 energy and latency by several times.
Mobina Kashaniyan, Ali Jannesari
Posted Nov 1, 2026 Conference Paper, Workshop Paper, Preprint, Large Language Models, High-Performance Computing, Energy Efficiency
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ATEST: Adaptive Resource-Aware Test-Time Scaling for LLM Inference
ATEST (RATTS) is resource-aware self-consistency. Unlike fixed self-consistency, RASC, ESC, and Adaptive-Consistency, it also uses live GPU cost. arXiv:2610.25519.
Mobina Kashaniyan, Ali Jannesari
Posted Oct 18, 2026 Preprint, Large Language Models, High-Performance Computing, Energy Efficiency, Test-Time Scaling
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Interpretable Adaptive Sampling for LLM Test-Time Scaling
Interpretable adaptive self-consistency. A fuzzy controller sets the sample budget, compared with best-of-N, self-certainty, majority voting, and compute-optimal test-time scaling.
Mobina Kashaniyan, Ali Jannesari
Posted Aug 4, 2026 Preprint, Artificial Intelligence, Large Language Models
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© 2026 Mobina Kashaniyan.