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Jihwan Kim

Jihwan Kim contributes to research discovery and scholarly infrastructure.

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Computer Vision cond-mat.stat-mech Machine Learning physics.chem-ph quant-ph

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preprint2026arXiv

DP-Muon: Differentially Private Optimization via Matrix-Orthogonalized Momentum

We study differentially private (DP) training with Muon, a matrix-valued optimizer that updates hidden-layer weights using momentum followed by Newton--Schulz orthogonalization. While DP-SGD is well understood, the interaction between per-example clipping, Gaussian noise, momentum, and nonlinear orthogonalization in Muon has not been systematically analyzed. We formulate DP-Muon, a private Muon procedure that clips per-example matrix gradients, adds Gaussian noise to the clipped lot average, and then applies momentum and Newton--Schulz orthogonalization as post-processing. We prove that DP-Muon inherits the privacy guarantee certified by the corresponding same-lot subsampled Gaussian accountant, with no additional privacy cost from Muon-specific post-processing. On the optimization side, we establish finite-horizon and vanishing stationarity guarantees under per-matrix clipping, with bounds that separate optimization error, clipping residual, privacy noise, and Newton--Schulz approximation error. We further show that the DP-induced bias in Muon arises not in the linear momentum buffer itself, but after the nonlinear Newton--Schulz map, where Gaussian noise induces a matrix-valued heat-smoothing bias. This motivates DP-MuonBC, a bias-corrected variant that removes the leading output-level bias term while preserving the same privacy guarantee. Experiments on E2E and DART show that Muon-style matrix updates improve private fine-tuning, and that DP-MuonBC further improves utility without increasing the privacy budget.

preprint2026arXiv

LiteFrame: Efficient Vision Encoders Unlock Frame Scaling in Video LLMs

The fundamental challenge in scaling Video Large Language Models (Video LLMs) to long-form video lies in managing the explosion of visual-token context length. Existing strategies predominantly focus on "post-hoc" token reduction -- reducing visual tokens after feature extraction to alleviate the LLM's computational overhead. While these methods effectively reduce the number of visual tokens, we observe that the primary latency bottleneck then shifts from the LLM to the expensive per-frame processing of the vision encoder. To address this, we introduce LiteFrame, a strong, yet highly efficient video encoder backbone for Video LLMs. To train LiteFrame, we propose Compressed Token Distillation (CTD), a novel training framework that teaches a compact student vision encoder to directly predict information-dense, spatio-temporally compressed representations produced by a large teacher vision model, effectively bypassing redundant computation. When coupled with further Language Model Adaptation (LMA), this approach results in a new latency-accuracy Pareto frontier -- compared with InternVL3-8B, LiteFrame provides a 35% reduction in end-to-end latency while processing 8$\times$ more frames and improves average video understanding accuracy across multiple benchmarks. Our results demonstrate a new potential path to unlocking longer-form video understanding under fixed compute budgets.

preprint2025arXiv

Single-shot detection limits of quantum illumination with multi-qudit states

Quantum illumination is a protocol for detecting a low-reflectivity target by using two-mode entangled states composed of signal and idler modes, which can outperform unentangled states. We study multi-qudit states for single-shot detection limits of quantum illumination under white noise environment. Using three-qubit states, we obtain that the performance is enhanced by the entanglement between signal and idler qubits, whereas it is degraded by the entanglement between signal qubits. The similar behaviors are also observed for three-qutrit, four-qubit, and four-ququart states. In particular, the optimal state is not a maximally entangled multipartite state but a combination of a maximally entangled bipartite state. Moreover, we show that quantum correlation can explain the quantum advantage of three-qubit, three-qutrit, and four-qubit states, with exception of a four-ququart state.

preprint2022arXiv

Janus van der Waals equations for real molecules with two-sided phase transitions

We obtain families of generalised van der Waals equations characterised by an even number $n=2,4,6$ and a continuous free parameter which is tunable for a critical compressibility factor. Each equation features two adjacent critical points which have a common critical temperature $T_{c}$ and arbitrarily close two critical densities. The critical phase transitions are naturally two-sided: the critical exponents are $α_{\scriptscriptstyle{P}}=γ_{\scriptscriptstyle{P}}=\frac{2}{3}$, $β_{\scriptscriptstyle{P}}=δ^{-1}=\frac{1}{3}$ for $T>T_{c}$ and $α_{\scriptscriptstyle{P}}=γ_{\scriptscriptstyle{P}}=\frac{n}{n+1}$, $β_{\scriptscriptstyle{P}}=δ^{-1}=\frac{1}{n+1}$ for $T<T_{c}$. In contrast with the original van der Waals equation, our novel equations all reduce consistently to the classical ideal gas law in low density limit. We test our formulas against NIST data for eleven major molecules and show agreements better than the original van der Waals equation, not only near to the critical points but also in low density regions.

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