arXiv subjects

Minho Jang

Publications and source records attributed to Minho Jang.

3 recordsLinked to original sources

arXiv2026

Nonlinear Model Predictive Control for Guidance Law with Target Input Estimation

This paper presents a look angle-based nonlinear model predictive control guidance (MPCG) method for missiles equipped with strapdown seekers. Conventional proportional navigation guidance (PNG) requires line-of-sight (LOS) rate measurements, which are not directly available in strapdown systems. MPCG instead employs look angles and their derivatives as state variables, eliminating body-rate coupling and associated parasitic feedback. The guidance problem is formulated as a continuous-time optimal control problem (OCP), discretized via the Legendre-Gauss-Radau pseudo-spectral method (LGRPM), and solved as a nonlinear program (NLP) incorporating explicit field-of-view (FOV) and acceleration constraints. Target acceleration at the first step of the prediction horizon is estimated using an adaptive extended Kalman filter (AEKF) integrated with an interacting multiple model (IMM) framework. Simulation results under single-maneuver scenarios, which include pitch and yaw plane weaving as well as barrel-roll maneuvers, demonstrate that MPCG achieves reliable interception while satisfying operational constraints, outperforming pure PNG (PPNG) in stability and resilience. This indicates that MPCG offers a practical and effective solution for modern missile guidance systems constrained by seeker measurement limitations.

eess.SY↗

arXiv2026

SelFusion: Self-distillation for Diffusion Language Models

Diffusion language models (DLMs) alleviate the inherent latency bottleneck of autoregressive (AR) large language models (LLMs), but their degraded generation quality limits practical applicability. Although knowledge distillation (KD) can be a promising direction for improving performance, we empirically find that naively applying conventional KD yields only marginal gains, or even degrades generation quality. Based on these observations, we propose a novel self-distillation framework for DLMs, namely SelFusion. To enable effective KD without an external teacher model, SelFusion performs two forward passes with different masking levels, defining the hard mode with a larger masking probability and the easy mode with a smaller masking probability. However, the easy mode is not always more accurate than the hard mode and can be overconfident on incorrect tokens. Thus, we introduce bidirectional KD between the two modes, which can dynamically determine the distillation direction based on token-level correctness. Experimental results on instruction-following tasks show that the proposed self-distillation substantially outperforms other KD methods with external LLM and DLM teachers. In many configurations, the student trained with SelFusion even surpasses the performance of the LLM teacher, providing a practical path toward improving DLM generation quality. Source code can be found at https://github.com/scai-research/SelFusion_official

cs.CL↗

arXiv2026

KoALa-Bench: Evaluating Large Audio Language Models on Korean Speech Understanding and Faithfulness

Recent advances in large audio language models (LALMs) have enabled multilingual speech understanding. However, benchmarks for evaluating LALMs remain scarce for non-English languages, with Korean being one such underexplored case. In this paper, we introduce KoALa-Bench, a comprehensive benchmark for evaluating Korean speech understanding and speech faithfulness of LALMs. In particular, KoALa-Bench comprises six tasks. Four tasks evaluate fundamental speech understanding capabilities, including automatic speech recognition, speech translation, speech question answering, and speech instruction following, while the remaining two tasks evaluate speech faithfulness, motivated by our observation that several LALMs often fail to fully leverage the speech modality. Furthermore, to reflect Korea-specific knowledge, our benchmark incorporates listening questions from the Korean college scholastic ability test as well as content covering Korean cultural domains. We conduct extensive experiments across six models, including both white-box and black-box ones. Our benchmark, evaluation code, and leaderboard are publicly available at https://ksbench.github.io/Korean-Benchmark/.

cs.CL↗