arXiv subjects

Taewoong Yoon

Publications and source records attributed to Taewoong Yoon.

4 recordsLinked to original sources

arXiv2026

Is Discrete Difficulty Sufficient? Leveraging Continuous Difficulty for Efficient Self-Consistency in LLMs

Self-Consistency (SC) is a decoding strategy that samples diverse reasoning paths and selects the most consistent answer, demonstrating strong performance on complex reasoning problems. However, the excessive token consumption incurred by generating multiple reasoning paths has been identified as a major limitation of SC. To improve computational efficiency, several studies have proposed strategies that adjust the number of reasoning paths or allocate resources differentially according to problem difficulty. Nevertheless, most existing methods categorize difficulty into a few fixed levels, failing to fully capture the continuously varying nature of reasoning complexity. In this work, we propose Flexible Self-Consistency (FSC), which estimates problem difficulty as a continuous signal and dynamically adjusts the number of generated reasoning paths accordingly. FSC predicts the output entropy of an input question using a pre-trained probe and leverages it as an indicator of model uncertainty to flexibly control the sampling budget. Experimental results show that, across various models and benchmarks, FSC maintains accuracy comparable to SC while achieving token savings of up to 76%.

cs.CL↗

arXiv2026

Mesoscopic Spin Coherence in a Disordered Dark Electron Spin Ensemble

Harnessing dipolar spin environments as controllable quantum resources is a central challenge in solid-state quantum technologies. Here, we report the observation of a coherent mesoscopic spin state in a disordered ensemble of substitutional nitrogen (P1) centers in diamond. An iterative Hartmann-Hahn protocol transfers polarization from dense nitrogen-vacancy (NV) centers to a P1 ensemble, yielding a 740-fold enhancement over room-temperature thermal equilibrium as revealed by differential readout. The resulting mesoscopic P1 spin ensemble exhibits collective Rabi oscillations and long-lived spin-lock and Hahn-echo coherences. We identify a crossover in the saturation polarization arising from the competition between coherent driving and local disorder, providing a quantitative measure of the system's intrinsic disorder. These results establish a foundation for utilizing dark electron spin ensembles as robust resources for quantum sensing and quantum many-body simulation.

quant-ph↗

arXiv2026

Breaking the Pre-Sampling Barrier: Activation-Informed Difficulty-Aware Self-Consistency

Self-Consistency (SC) is an effective decoding strategy that improves the reasoning performance of Large Language Models (LLMs) by generating multiple chain-of-thought reasoning paths and selecting the final answer via majority voting. However, it suffers from substantial inference costs because it requires a large number of samples. To mitigate this issue, Difficulty-Adaptive Self-Consistency (DSC) was proposed to reduce unnecessary token usage for easy problems by adjusting the number of samples according to problem difficulty. However, DSC requires additional model calls and pre-sampling to estimate difficulty, and this process is repeated when applying to each dataset, leading to significant computational overhead. In this work, we propose Activation-Informed Difficulty-Aware Self-Consistency (ACTSC) to address these limitations. ACTSC leverages internal difficulty signals reflected in the feed-forward network neuron activations to construct a lightweight difficulty estimation probe, without any additional token generation or model calls. The probe dynamically adjusts the number of samples for SC and can be applied to new datasets without requiring pre-sampling for difficulty estimation. To validate its effectiveness, we conduct experiments on five benchmarks. Experimental results show that ACTSC effectively reduces inference costs while maintaining accuracy relative to existing methods.

cs.CL↗

arXiv2024

Design optimization of semiconductor manufacturing equipment using a novel multi-fidelity surrogate modeling approach

Careful design of semiconductor manufacturing equipment is crucial for ensuring the performance, yield, and reliability of semiconductor devices. Despite this, numerical optimization methods are seldom applied to optimize the design of such equipment due to the difficulty of obtaining accurate simulation models. In this paper, we address a practical and industrially relevant electrostatic chuck (ESC) design optimization problem by proposing a novel multi-fidelity surrogate modeling approach. The optimization aims to improve the temperature uniformity of the wafer during the etching process by adjusting seven parameters associated with the coolant path and embossing. Our approach combines low-fidelity (LF) and high-fidelity (HF) simulation data to efficiently predict spatial-field quantities, even with a limited number of data points. We use proper orthogonal decomposition (POD) to project the spatially interpolated HF and LF field data onto a shared latent space, followed by the construction of a multi-fidelity kriging model to predict the latent variables of the HF output field. In the ESC design problem, with hundreds or fewer data, our approach achieves a more than 10% reduction in prediction error compared to using kriging models with only HF or LF data. Additionally, in the ESC optimization problem, our proposed method yields better solutions with improvements in all of the quantities of interest, while requiring 20% less data generation cost compared to the HF surrogate modeling approach.

cs.CE↗