arXiv subjects

Seungki Hong

Publications and source records attributed to Seungki Hong.

4 recordsLinked to original sources

arXiv2026

Knowledge Integration Decay in Search-Augmented Reasoning of Large Language Models

Modern Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks by employing search-augmented reasoning to incorporate external knowledge into long chains of thought. However, we identify a critical yet underexplored bottleneck in this paradigm, termed Knowledge Integration Decay (KID). Specifically, we observe that as the length of reasoning generated before search grows, models increasingly fail to integrate retrieved evidence into subsequent reasoning steps, limiting performance even when relevant information is available. To address this, we propose Self-Anchored Knowledge Encoding (SAKE), a training-free inference-time strategy designed to stabilize knowledge utilization. By anchoring retrieved knowledge at both the beginning and end of the reasoning process, SAKE prevents it from being overshadowed by prior context, thereby preserving its semantic integrity. Extensive experiments on multi-hop QA and complex reasoning benchmarks demonstrate that SAKE significantly mitigates KID and improves performance, offering a lightweight yet effective solution for knowledge integration in agentic LLMs.

cs.CL↗

arXiv2026

Snowball: A Scalable All-to-All Ising Machine with Dual-Mode Markov Chain Monte Carlo Spin Selection and Asynchronous Spin Updates for Fast Combinatorial Optimization

Ising machines have emerged as accelerators for combinatorial optimization. To enable practical deployment, this work aims to reduce time-to-solution by addressing three challenges: (1) hardware topology, (2) spin selection and update algorithms, and (3) scalable coupling-coefficient precision. Restricted topologies require minor embedding; naive parallel updates can oscillate or stall; and limited precision can preclude feasible mappings or degrade solution quality. This work presents Snowball, a digital, scalable, all-to-all coupled Ising machine that integrates dual-mode Markov chain Monte Carlo spin selection with asynchronous spin updates to promote convergence and reduce time-to-solution. The digital architecture supports wide, configurable coupling precision, unlike many analog realizations at high bit widths. A prototype on an AMD Alveo U250 accelerator card achieves an 8$\times$ reduction in time-to-solution relative to a state-of-the-art Ising machine on the same benchmark instance.

cs.LG↗

arXiv2025

DSAC: Low-Cost RowHammer Mitigation Using In-DRAM Stochastic and Approximate Counting Algorithm

This paper provides the fundamental mechanisms of two types of row activation-induced bit flips and proposes in-DRAM protection techniques. RowBleed occurs when a victim row experiences charge leakage due to transistor's threshold voltage lowering induced by long activation of a neighboring aggressor row. Therefore, this paper proposes Time-Weighted Counting for RowBleed mitigation, which assigns greater counter weights to rows that are activated for longer durations. On the other hand, RowHammer occurs when a victim row experiences electron injection due to frequent activation of a neighboring aggressor row. Similarly, Extended RowHammer, the phenomenon where victim rows are two rows beyond aggressor rows, is also caused by electron injection due to frequent activation of a neighboring aggressor row. Consequently, accurate detection of aggressor rows is crucial. Therefore, this paper proposes RowHammer mitigation algorithm named DSAC (in-DRAM Stochastic and Approximate Counting algorithm), which utilizes a replacement probability that adjusts based on the count of the old row. This paper introduces a RowHammer protection index called Maximum Disturbance, which measures the maximum accumulated number of row activations within an observation period. The experimental results demonstrate that DSAC can achieve 133x lower Maximum Disturbance than the state-of-the-art counter-based algorithm.

cs.CR↗

arXiv2021

Identification of Collapsed Carbon Nanotubes in High-Strength Fibres Spun from Compositionally Polydisperse Aerogels

Carbon Nanotubes (CNTs) of sufficiently large diameter and a few layers self-collapse into flat ribbons at atmospheric pressure, forming bundles of stacked CNTs that maximize packing and thus CNT interaction. Their improved stress transfer by shear makes collapsed CNTs ideal building blocks in macroscopic fibers of CNTs with high-performance longitudinal properties, particularly high tensile properties as reinforcing fibres. This work introduces cross-sectional transmission electron microscopy of FIB-milled samples as a way to univocally identify collapsed CNTs and to determine the full population of different CNTs in macroscopic fibers produced by spinning from floating catalyst chemical vapour deposition. We show that close proximity in bundles is a major driver for collapse and that CNT stoutness (number of layers/diameter), which dominates the collapse onset, is controlled by the growth promotor. Despite differences in decomposition route, different C precursors lead to similar distributions of the ratio layers/diameter. The synthesis conditions in this study give a maximum fraction of collapsed CNTs of 70$\%$ when using selenium as promotor, corresponding to an average of $0.25 layer/nm$.

physics.app-ph↗