arXiv subjects

Gunn Kim

Publications and source records attributed to Gunn Kim.

At least 19 recordsLinked to original sources

arXiv2026

Pole-Zero Geometry, Model Reduction, and Identifiability in Sensory Adaptation

Sensory adaptation provides a concrete setting in which low-order system identification can fail qualitatively. We show that one fixed higher-order adaptive system composed entirely of real first-order relaxation modes can be reduced to opposite sides of the second-order pole boundary: low-frequency moment matching gives $\rho_{\rm moment}=4.50$, whereas finite-window fitting gives $\rho_{\rm window}=3.31$, and the inferred pole class changes further with sampling protocol. Thus the real-versus-complex classification of a reduced model is not itself reduction invariant. We then use the general two-state spectrum to connect stochastic identifiability to adaptation: for nontrivial coupling and one-state observation, cross diffusion drops out of the scalar spectrum when the hidden state has no self-relaxation. In the adaptive model, this condition is precisely the integral-memory limit that produces exact adaptation, while leaky memory restores spectral sensitivity. For the exact-adaptation model, the Gaussian path-space irreversibility nevertheless depends on the hidden cross-diffusion channel. Hence $\{H,S_x\}$ does not determine the irreversibility rate. Independently, for a specified all-even reduced two-state drift with $\rho<4$, the drift-only lower bound is $\sigma \ge \tau_x^{-1}(4/\rho-1)$. Published \textit{E.~coli} and \textit{C.~elegans} responses provide biological examples of these limits. The distinction established here between transfer-function invariants, reduction-dependent properties, and hidden-state quantities provides a concrete framework for evaluating the limitations of low-dimensional models of adaptive biological dynamics.

cond-mat.stat-mech↗

arXiv2026

Dynamical phase selection controls compute scaling in looped transformers

A looped transformer performs inference by iterating a weight-tied map, making its computation a dynamical process whose cost is set by the resulting inference dynamics. Here we show that networks with identical architecture and objective, trained to identical accuracy, nevertheless realize distinct dynamical phases depending strongly on initialization, and that the bifurcation defining each phase determines how test-time compute scales. The phases are distinguished by their bifurcation mechanisms, including a saddle-node fold and a Neimark-Sacker-type transition to bounded nonstationary motion. In the fold phase, a one-dimensional normal-form reduction predicts both the relaxation-time and spectral-gap amplitudes from local derivatives of the trained map, yielding the parameter-free relation $\tau(\varepsilon)[1-\lambda_{\max}(-\varepsilon)]\to\pi$. Composed with a regular distribution of problem difficulty, the same critical slowing down produces the workload-level tail $P(\tau>N)\sim N^{-2}$. In the Neimark--Sacker phase, the fold scaling law disappears rather than merely changing its prefactor. Thus, test-time compute is not determined by architecture alone. It is governed by the dynamical phase of the solution found by training.

cond-mat.dis-nn↗

arXiv2026

Path-Measure Dynamics of Attention-Driven World Models: A Nonlocal Onsager--Machlup Approach

Attention enables a world model to condition on its entire history, providing long-term memory that facilitates long-range predictions. While the local Onsager--Machlup theory in our companion paper assumes a temporally local predictive action, we investigate the conditions under which this locality holds. We derive the predictive path measure for latent dynamics that become non-Markovian due to attention-induced memory, demonstrating that this measure is the projection of a hidden linear Markov augmentation. Eliminating the auxiliary field results in a nonlocal Onsager--Machlup action, where memory manifests as a nonlocal quadratic form rather than a force. These kernels are completely monotone and exactly match a hidden Markov embedding with a finite relaxation spectrum; otherwise, the dynamics remain fundamentally nonlocal. By expanding the action in terms of the scale-separation parameter $\epsilon=\tau_{\text{mem}}/\tau_{\text{dyn}}$, we show that the leading order recovers the local action of the companion paper, establishing locality as the short-memory limit of a nonlocal theory. We verify the reversible sector of this expansion term by term against an exactly solvable vector linear model.

cond-mat.stat-mech↗

arXiv2026

A Path-Space Formulation of Prediction in World Models: From a Single Action to Prediction, Planning, and Irreversibility

We propose a path-space formulation of prediction in AI world models. Rather than sequences of one-step conditional distributions, we argue that a world model implicitly defines a probability measure over future trajectories. In the local regime where latent dynamics admit an effective Markovian description, this path measure takes the Onsager-Machlup form. Within this framework, prediction (most probable trajectory), planning (constrained optimization), and uncertainty (fluctuations) emerge as operations on a single action functional. We decompose the latent dynamics into reversible and irreversible components and introduce operational measures of entropy production from model rollouts. In controlled small-scale attention-based models, we find that attention asymmetry is acquired during training in proportion to the irreversibility of the data. Symmetrizing the learned attention suppresses entropy production and selectively degrades long-horizon prediction of irreversible dynamics while preserving relaxational prediction. These results suggest that irreversibility may serve as a computational resource for predictive world models. More generally, the fundamental predictive object is a distribution over future paths rather than states.

cs.LG↗

arXiv2026

Continuous PT-Symmetry Breaking as a Design Variable for Giant Altermagnetic Spin Splitting

Magnetic point-group analysis classifies altermagnets but returns only a binary symmetry verdict, leaving spin-splitting energy (SSE) inaccessible without spin-polarized density functional theory (DFT). This binary ceiling is not fundamental. Sublattice symmetry breaking is promoted here to a continuous, DFT-free scalar -- the Motif Symmetry-Breaking Index (MSBI) -- that quantifies $\mathcal{PT}$-symmetry breaking between antiparallel magnetic motifs directly from crystal coordinates. SHAP analysis of an XGBoost surrogate trained on 3,851 DFT-labeled binary structures identifies three dominant descriptors: MSBI (symmetry-breaking axis), motif packing fraction MPF (superexchange axis), and the $p/d$ electron ratio (covalency axis), each mapping onto a directly tunable experimental handle. A controlled VO--CrSb comparison within the same P$6_3$/mmc host lattice demonstrates that composition alone boosts SSE sevenfold. Bayesian optimization over this three-axis space, followed by independent DFT validation, recovers $\alpha$-NiS (SSE $= 0.823$\,eV) as cross-validation against an independent symmetry-based prediction and identifies three previously unrecognized high-SSE candidates -- square-planar FeS (1.297\,eV), octahedral CoS (1.103\,eV), and FeAs (1.089\,eV) -- all matching or exceeding CrSb. Square-planar Fe--S is proposed as a transferable coordination motif for giant altermagnetic spin splitting, advancing altermagnet design from symmetry classification to continuous quantitative optimization.

cond-mat.mtrl-sci↗

arXiv2026

Non-Equilibrium Stochastic Dynamics as a Unified Framework for Insight and Repetitive Learning: A Kramers Escape Approach to Continual Learning

Continual learning in artificial neural networks is fundamentally limited by the stability--plasticity dilemma: systems that retain prior knowledge tend to resist acquiring new knowledge, and vice versa. Existing approaches, most notably elastic weight consolidation~(EWC), address this empirically without a physical account of why plasticity eventually collapses as tasks accumulate. Separately, the distinction between sudden insight and gradual skill acquisition through repetitive practice has lacked a unified theoretical description. Here, we show that both problems admit a common resolution within non-equilibrium statistical physics. We model the state of a learning system as a particle evolving under Langevin dynamics on a double-well energy landscape, with the noise amplitude governed by a time-dependent effective temperature $T(t)$. The probability density obeys a Fokker--Planck equation, and transitions between metastable states are governed by the Kramers escape rate $k = (\omega_0\omega_b/2\pi)\,e^{-\Delta E/T}$. We make two contributions. First, we identify the EWC penalty term as an energy barrier whose height grows linearly with the number of accumulated tasks, yielding an exponential collapse of the transition rate predicted analytically and confirmed numerically. Second, we show that insight and repetitive learning correspond to two qualitatively distinct temperature protocols within the same Fokker--Planck equation: insight events produce transient spikes in $T(t)$ that drive rapid barrier crossing, whereas repetitive practice operates at a modestly elevated but fixed temperature, achieving transitions through sustained stochastic diffusion. These results establish a physically grounded framework for understanding plasticity and its failure in continual learning systems, and suggest principled design criteria for adaptive noise schedules in artificial intelligence.

cond-mat.stat-mech↗

arXiv2026

Symmetry selection rules for the intrinsic nonlinear thermal Hall effect in altermagnets: Role of quantum metric and $C_{2}$ rotational symmetry

We establish symmetry-based selection rules for the intrinsic nonlinear thermal Hall effect driven by the quantum metric in altermagnets. We show that a nonvanishing nonlinear thermal Hall conductivity $\kappa_{xyy}$ requires three conditions: (i) a nontrivial quantum metric, (ii) breaking of mirror symmetry $M_{x}$, and (iii) breaking of twofold rotational symmetry $C_{2}$. Using tight-binding models on a square lattice, we demonstrate that $d$-wave altermagnets naturally break $C_{2}$ through parity-mixing orbital hybridizations, while $g$-wave systems preserve $C_{2}$, forcing the response to vanish identically. Step-by-step Taylor expansions and explicit unitary matrix proofs establish these results. Our framework provides predictive power for material selection and lays the groundwork for nonlinear spin-caloritronic devices.

cond-mat.mes-hall↗

arXiv2026

Critical Scaling and Metabolic Regulation in a Ginzburg--Landau Theory of Cognitive Dynamics

We formulate a phenomenological effective field theory in which biological intelligence emerges as a macroscopic order parameter sustained by continuous metabolic flux. By modeling cognition as a coarse-grained neural activity field governed by a variational free energy, we derive closed-form expressions for information capacity and structural susceptibility using a Gaussian maximum entropy approximation. The theory predicts a universal algebraic divergence of the susceptibility, $\chi \sim K^{-3/2}$, as the structural stiffness $K$ approaches the instability threshold. The exponent $\gamma = 3/2$ is consistent with the mean-field branching process universality class, thereby providing a theoretical rationale for the observed avalanche size exponent $\tau \approx 3/2$ in cortical dynamics without invoking microscopic equivalence. We identify adult cognition as a metabolically pinned non-equilibrium steady state maintained near the critical regime $\Gamma \equiv K/\alpha \approx 1$ by continuous metabolic regulation, while pathological decline corresponds to a delocalization transition triggered by the violation of structural stability conditions. The framework generates concrete, falsifiable predictions for attention scaling, altered states of consciousness, and transcranial magnetic stimulation responses, each of which can be tested against existing neuroimaging and electrophysiological datasets.

cond-mat.stat-mech↗

arXiv2026

Topological Reorganization and Coordination-Controlled Crossover in Synchronization Onset on Regular Lattices

The transition to global synchronization in coupled dynamical systems is governed by the interplay between coupling strength and structural topology. Although abrupt, first-order-like synchronization transitions have been extensively reported in heterogeneous networks, it is unclear whether comparable accelerated onset behavior can emerge purely from coordination geometry in spatially homogeneous, regular lattices. In this study, we investigate large-scale ($N=10^5$) stochastic Stuart-Landau oscillator networks defined on regular lattices with controlled coordination number. Using topological data analysis (TDA), simplicial-complex characterization, and optimal-transport-based geometric diagnostics, we identify a coordination-controlled crossover in synchronization onset dynamics at approximately $z_{c} \approx 7$ within the class of regular lattices considered. Low-coordination lattices ($z < z_{c}$) exhibit persistent $H_2$ topological features in the dynamical amplitude field that correlate with delayed coherence and surface-limited propagation. In contrast, higher-coordination lattices ($z > z_{c}$) display rapid fragmentation of these features, reduced interface roughness, and predominantly positive Ricci curvature. This is consistent with enhanced path redundancy and improved transport efficiency. In this regime, the global order parameter exhibits accelerated exponential-like growth during the onset stage. Throughout this work, abrupt synchronization refers specifically to this exponential onset behavior rather than to thermodynamic first-order hysteresis. Our results demonstrate that increasing coordination density induces a qualitative reorganization of higher-order topological structure that strongly correlates with synchronization efficiency in regular lattice systems.

cond-mat.stat-mech↗

arXiv2026

Thermodynamic Optimization of Sensory Adaptation via Game-Theoretic Path Integrals

Biological sensory systems, from \textit{E.~coli} chemotaxis to sensory neurons in \textit{C.~elegans}, achieve reliable adaptation over wide dynamic ranges despite operating in strongly noisy and overdamped regimes. Here, we present a field-theoretic framework in which sensory adaptation emerges from a variational free-energy principle, formulated as a stochastic differential game between an organism and its environment. Using an Onsager--Machlup path-integral formalism, we show that the resulting adaptive dynamics are mathematically equivalent to a class of model reference adaptive control schemes and can be interpreted as a dynamic renormalization of the system's Green's function. Within this framework, the phasic overshoot commonly observed in sensory responses arises naturally from an effective inertia ($m^* \approx \tau \gamma$) generated by memory-dissipation coupling, rather than from biochemical fine-tuning. Quantitative fits to experimental data across species yield $R^2 > 0.88$, and indicate that adaptive sensory processing operates within a narrow thermodynamically optimal regime bounded by signal-to-noise and stability constraints.

cond-mat.stat-mech↗

arXiv2026

Entropy-Generated Attention Beyond Softmax and Entmax: Kaniadakis and Reciprocal-Symmetric Abe Operators

We derive two attention operators from generalized statistical entropies. Kaniadakis entropy yields an exact full-support normalization whose weights and low-score sensitivities decay algebraically, rather than exponentially as in Softmax or by exact truncation as in entmax. Classical Abe entropy yields an implicit reciprocal-symmetric operator. With $q=e^\epsilon$, the involution $q\leftrightarrow q^{-1}$ removes every odd correction about Softmax; we obtain the normalized second- and fourth-order terms, including the deformation of the normalization multiplier. These stationary laws follow from a Fisher-metric Lagrangian on the probability simplex, whose Shannon sector recovers scaled dot-product Softmax. We also give a tangent-gradient test for deciding whether changing the entropy changes the attention profile or only its scale. R\'enyi and two-parameter Sharma--Mittal entropies retain the Tsallis--entmax inverse-gradient shape, but their global moments make the effective temperature input dependent when the external temperature is fixed. Distinguishing profile-shape equivalence from fixed-parameter operator equivalence separates new normalization shapes from adaptive rescalings and organizes the operators by support, tail behavior, and realization complexity.

cs.LG↗

arXiv2025

Observation of Macroscopic Nonlocal Voltage at Room Temperature

Electrons in conductors suffer frequent scatterings with defects and phonons, and the diffusive macroscopic behaviors are determined by an external electric field through Ohms law. If electrons are no longer diffusive, the Ohmic description collapses. In devices composed of thin chalcogenides and YBa2Cu3O7, we observe a transition from an Ohmic conductor to a nonlocal conductor below a certain temperature. The nonlocal conductor is characterized by significant nonlocal voltages (~0.1 V) across macroscopic regions (~1 mm) that are conventionally considered to be equipotential. Nonlinear responses are an additional characteristic. Negative local resistances in a vicinal geometry support macroscopic hydrodynamic flow as the underlying mechanism, implying electron momentum conservation over incredibly long distances. This new conduction state, observable at room temperature, opens the field of nonlocal electronics and low-dissipation applications.

cond-mat.str-el↗

arXiv2025

Selective Hydrogen Molecule Dissociation on Ca2N Monolayer

Developing efficient hydrogen storage and conversion technologies is essential for sustainable energy. This study investigates the catalytic potential of a dicalcium nitride (Ca2N) monolayer for hydrogen dissociation using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. We find that atomic hydrogen preferentially adsorbs at Ca-centered hollow sites (labeled A sites), while molecular hydrogen adsorption is limited to bridge sites (labeled B sites). AIMD simulations reveal that H2 dissociation at B sites inhibits further adsorption, suggesting a mechanism of controlled H2 dissociation. The current findings emphasize the potential of pristine Ca2N as a catalyst for H2 dissociation-related processes and motivate future investigations of its activity in hydrogen evolution reactions.

cond-mat.mes-hall↗

arXiv2025

Dynamic Modulation of Electronic and Optical Properties in GaN Bilayers by Interlayer Sliding

In this study, we present a first-principles investigation of the electronic and optical properties of gallium nitride (GaN) bilayers, focusing on the influence of interlayer sliding and spacing. In contrast to the earlier studies on discrete stacking configurations, we explore the dynamic evolution of the properties during transitions between stable stacking arrangements. Using density functional theory calculations, we systematically analyze the impact of these structural variations on the electronic band structure and optical absorption spectra of GaN bilayers. The analysis includes both high-symmetry stacking configurations (AA', AB', and AC') and intermediate states generated by controlled in-plane atomic displacements, thereby providing a comprehensive understanding of the property changes associated with interlayer sliding. The findings of this study provide valuable insights into the potential for tuning the electronic and optical response of two-dimensional GaN for applications in nanoscale photonic and electronic devices, where precise control over interlayer interactions and stacking is crucial.

cond-mat.mes-hall↗

arXiv2025

CNN-based TEM image denoising from first principles

Transmission electron microscope (TEM) images are often corrupted by noise, hindering their interpretation. To address this issue, we propose a deep learning-based approach using simulated images. Using density functional theory calculations with a set of pseudo-atomic orbital basis sets, we generate highly accurate ground truth images. We introduce four types of noise into these simulations to create realistic training datasets. Each type of noise is then used to train a separate convolutional neural network (CNN) model. Our results show that these CNNs are effective in reducing noise, even when applied to images with different noise levels than those used during training. However, we observe limitations in some cases, particularly in preserving the integrity of circular shapes and avoiding visible artifacts between image patches. To overcome these challenges, we propose alternative training strategies and future research directions. This study provides a valuable framework for training deep learning models for TEM image denoising.

cond-mat.mtrl-sci↗

arXiv2023

An unconventional platform for two-dimensional Kagome flat bands on semiconductor surfaces

In condensed matter physics, the Kagome lattice and its inherent flat bands have attracted considerable attention for their potential to host a variety of exotic physical phenomena. Despite extensive efforts to fabricate thin films of Kagome materials aimed at modulating the flat bands through electrostatic gating or strain manipulation, progress has been limited. Here, we report the observation of a novel $d$-orbital hybridized Kagome-derived flat band in Ag/Si(111) $\sqrt{3}\times\sqrt{3}$ as revealed by angle-resolved photoemission spectroscopy. Our findings indicate that silver atoms on a silicon substrate form a Kagome-like structure, where a delicate balance in the hopping parameters of the in-plane $d$-orbitals leads to destructive interference, resulting in a flat band. These results not only introduce a new platform for Kagome physics but also illuminate the potential for integrating metal-semiconductor interfaces into Kagome-related research, thereby opening a new avenue for exploring ideal two-dimensional Kagome systems.

cond-mat.mtrl-sci↗

arXiv2021

Self-consistent gravity model for inferring node mass in flow networks

The gravity model, inspired by Newton's law of universal gravitation, has long served as a primary tool for interpreting trade flows between countries, using a country's economic `mass' as a key determinant. Despite its wide application, the definition of `mass' within this model remains ambiguous. It is often approximated using indicators like GDP, which may not accurately reflect a country's true trade potential. Here, we introduce a data-driven, self-consistent numerical approach that redefines `mass' from a static proxy to a dynamic attribute inferred directly from flow data. We infer mass distribution and interaction nature through our method, mirroring Newton's approach to understanding gravity. Our methodology accurately identifies predefined embeddings and reconstructs system attributes when applied to synthetic flow data, demonstrating its strong predictive power and adaptability. Further application to real-world trade networks yields critical insights, revealing the spatial spectrum of trade flows and the economic mass of countries, two key features unexplored in depth by existing models. Our methodology not only enables accurate reconstruction of the original flow but also allows for a deep understanding of the unique capabilities of each node within the network. This study marks a significant shift in the understanding and application of the gravity model, providing a more comprehensive tool for analyzing complex systems and uncovering new insights into various fields, including global trade, traffic engineering, epidemic disease prevention, and infrastructure design.

physics.data-an↗

arXiv2020

Adsorption properties of dopamine derivatives using carbon nanotubes: A first-principles study

Detecting dopamine is of great biological importance because the molecule plays many roles in the human body. For instance, the lack of dopamine release is the cause of Parkinson's disease. Although many researchers have carried out experiments on dopamine detection using carbon nanotubes (CNTs), there are only a few theoretical studies on this topic. We study the adsorption properties of dopamine and its derivatives, L-DOPA and dopamine o-quinone, adsorbed on a semiconducting (10, 0) CNT, using density functional theory calculations. Our computational simulations reveal that localized states originating from dopamine o-quinone appear in the bandgap of the (10, 0) CNT, but those originating from dopamine and L-DOPA do not appear in the gap. Therefore, dopamine o-quinone is expected to be detectable using an external electric field but dopamine and L-DOPA should be difficult to detect.

physics.app-ph↗