Self-Consistency Sampling

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling
arXiv:2609.19499 — For self-consistency, candidate count N is not enough. At fixed N, how samples are batched can change LLM test-time scaling energy and latency by several times on A100 GPUs.
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 / RATTS: Adaptive Resource-Aware Test-Time Scaling for LLM Inference (arXiv:2610.25519)
ATEST (RATTS) makes self-consistency resource-aware. The same four-sample self-consistency query can use 4.1× more energy when the GPU is busy. ATEST-Online answers every query within budget. arXiv:2610.25519.
Mobina Kashaniyan
Posted Oct 18, 2026 Large Language Models, High-Performance Computing, Energy Efficiency, Test-Time Scaling, Research Summary
ATEST: Adaptive Resource-Aware Test-Time Scaling for LLM Inference
ATEST (RATTS) is resource-aware self-consistency for LLM inference. It adapts how many samples to draw to live GPU load. The same four-sample query can cost 4.1× more energy when the GPU is busy. 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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Sample Count Is Not Enough (arXiv:2609.19499): Why Candidate-Generation Strategy Matters for LLM Test-Time Scaling Energy and Performance
arXiv:2609.19499 — Self-consistency sample count N is not enough. At fixed N=8, generation schedules like 1×8 vs 8×1 can change A100 energy by about 4.6–4.9× and P95 latency by about 5.8–6.1×.
Mobina Kashaniyan
Posted Sep 16, 2026 Last updated Sep 17, 2026 Large Language Models, High-Performance Computing, Energy Efficiency, Test-Time Scaling, Research Summary
Interpretable Adaptive Sampling for LLM Test-Time Scaling
Interpretable adaptive sampling for self-consistency. A fuzzy controller uses prompt complexity and model confidence to choose how many self-consistency samples each prompt receives.
Mobina Kashaniyan, Ali Jannesari
Posted Aug 4, 2026 Preprint, Artificial Intelligence, Large Language Models
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Interpretable Adaptive Sampling for LLM Test-Time Scaling and Test-Time Compute
Interpretable adaptive self-consistency for LLM test-time scaling. A fuzzy controller uses prompt complexity and model confidence to choose how many self-consistency samples each prompt receives.
Mobina Kashaniyan
Posted Aug 4, 2026 Large Language Models, Artificial Intelligence, LLM Reasoning, Test-Time Scaling, Research Summary

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