arXiv subjects

Jimin Kim

Publications and source records attributed to Jimin Kim.

At least 19 recordsLinked to original sources

arXiv2026

Towards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives

We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by agents that contribute data and participate in model updates under heterogeneous value constraints. The key idea is to credit only those updates that remain admissible after screening them against each principal's value profile. We formulate value-conditioned gradient filtering, online marginal contribution signals, and cumulative revenue settlement within a traversal learning (TL) substrate. TL is especially attractive here because it performs decentralized backpropagation without the quality loss associated with aggregation-centric distributed learning and, we argue, offers a finer attribution substrate than FedAvg-style federated learning by preserving explicit traversal and gradient paths. The framework is positioned against data valuation, federated contribution estimation, personalized federated learning, and pluralistic alignment.

cs.LG↗

arXiv2026

Soohak: A Mathematician-Curated Benchmark for Evaluating Research-level Math Capabilities of LLMs

Following the recent achievement of gold-medal performance on the IMO by frontier LLMs, the community is searching for the next meaningful and challenging target for measuring LLM reasoning. Whereas olympiad-style problems measure step-by-step reasoning alone, research-level problems use such reasoning to advance the frontier of mathematical knowledge itself, emerging as a compelling alternative. Yet research-level math benchmarks remain scarce because such problems are difficult to source (e.g., Riemann Bench and FrontierMath-Tier 4 contain 25 and 50 problems, respectively). To support reliable evaluation of next-generation frontier models, we introduce Soohak, a 439-problem benchmark newly authored from scratch by 64 mathematicians. Soohak comprises two subsets. On the Challenge subset, frontier models including Gemini-3-Pro, GPT-5, and Claude-Opus-4.5 reach 30.4%, 26.4%, and 10.4% respectively, leaving substantial headroom, while leading open-weight models such as Qwen3-235B, GPT-OSS-120B, and Kimi-2.5 remain below 15%. Notably, beyond standard problem solving, Soohak introduces a refusal subset that probes a capability intrinsic to research mathematics: recognizing ill-posed problems and pausing rather than producing confident but unjustified answers. On this subset, no model exceeds 50%, identifying refusal as a new optimization target that current models do not directly address. To prevent contamination, the dataset will be publicly released in late 2026, with model evaluations available upon request in the interim.

cs.CL↗

arXiv2026

New black hole mass calibrations and the fundamental plane of the broad-line region size, luminosity, and velocity

We present a new calibration of the broad-line region (BLR) size-luminosity-velocity relation using a sample of 157 AGNs with reliable Hbeta time-delay (\lag) measurements from Wang & Woo 2024. By incorporating the Eddington ratio as a third parameter, we effectively correct the systematic offset of high-Eddington AGNs in the traditional BLR size-luminosity relation. The resulting three-parameter fit defines a fundamental plane in the 3-D space of the \lag, optical luminosity, and Hbeta velocity, with an intrinsic scatter of 0.21 dex. This tight correlation reflects the coupled effects of gas kinematics, photoionization, and BLR geometry. In turn, we develop a new method to infer \lag\ from the combination of optical luminosity and Hbeta velocity, and derive single-epoch black hole mass estimators by adopting either the full-width-at-half-maximum (FWHM) or line dispersion ($\sigma$) of the Hbeta line profile as the velocity indicator. The derived \lag shows a ~0.1 dex scatter, depending on the choice of calibrations. We show that the previous mass estimates based on the two-parameter size-luminosity relation with a 0.5 slope can be overestimated by up to 0.5 dex, demonstrating that the new mass estimator substantially changes the cosmic black hole mass density and the growth of black hole seeds in the early universe.

astro-ph.GA↗

arXiv2025

DepthFocus: Controllable Depth Estimation for See-Through Scenes

Depth in the real world is rarely singular. Transmissive materials create layered ambiguities that confound conventional perception systems. Existing models remain passive; conventional approaches typically estimate static depth maps anchored to the nearest surface, and even recent multi-head extensions suffer from a representational bottleneck due to fixed feature representations. This stands in contrast to human vision, which actively shifts focus to perceive a desired depth. We introduce \textbf{DepthFocus}, a steerable Vision Transformer that redefines stereo depth estimation as condition-aware control. Instead of extracting fixed features, our model dynamically modulates its computation based on a physical reference depth, integrating dual conditional mechanisms to selectively perceive geometry aligned with the desired focus. Leveraging a newly curated large-scale synthetic dataset, \textbf{DepthFocus} achieves state-of-the-art results across all evaluated benchmarks, including both standard single-layer and complex multi-layered scenarios. While maintaining high precision in opaque regions, our approach effectively resolves depth ambiguities in transparent and reflective scenes by selectively reconstructing geometry at a target distance. This capability enables robust, intent-driven perception that significantly outperforms existing multi-layer methods, marking a substantial step toward active 3D perception. \noindent \textbf{Project page}: \href{https://junhong-3dv.github.io/depthfocus-project/}{\textbf{this https URL}}.

cs.CV↗

arXiv2025

Towards Robust Mathematical Reasoning

Finding the right north-star metrics is highly critical for advancing the mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focus on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks, vetted by a panel of top specialists and that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMO-AnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-Proof Bench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO level problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the gold-level performance at IMO 2025 with Gemini Deep Think (Luong and Lockhart, 2025). Our model achieved 80.0% on IMO-AnswerBench and 65.7% on the advanced IMO-Proof Bench, surpassing the best non-Gemini models by large margins of 6.9% and 42.4% respectively. We also showed that autograders built with Gemini reasoning correlate well with human evaluations and construct IMO-GradingBench, with 1000 human gradings on proofs, to enable further progress in automatic evaluation of long-form answers. We hope that IMO-Bench will help the community towards advancing robust mathematical reasoning and release it at https://imobench.github.io/.

cs.CL↗

arXiv2025

{S\textsuperscript{2}M\textsuperscript{2}}: Scalable Stereo Matching Model for Reliable Depth Estimation

The pursuit of a generalizable stereo matching model, capable of performing well across varying resolutions and disparity ranges without dataset-specific fine-tuning, has revealed a fundamental trade-off. Iterative local search methods achieve high scores on constrained benchmarks, but their core mechanism inherently limits the global consistency required for true generalization. However, global matching architectures, while theoretically more robust, have historically been rendered infeasible by prohibitive computational and memory costs. We resolve this dilemma with {S\textsuperscript{2}M\textsuperscript{2}}: a global matching architecture that achieves state-of-the-art accuracy and high efficiency without relying on cost volume filtering or deep refinement stacks. Our design integrates a multi-resolution transformer for robust long-range correspondence, trained with a novel loss function that concentrates probability on feasible matches. This approach enables a more robust joint estimation of disparity, occlusion, and confidence. {S\textsuperscript{2}M\textsuperscript{2}} establishes a new state of the art on Middlebury v3 and ETH3D benchmarks, significantly outperforming prior methods in most metrics while reconstructing high-quality details with competitive efficiency.

cs.CV↗

arXiv2025

Typhoon Path Prediction Using Functional Data Analysis and Clustering-Based Regression

Accurate prediction of typhoon trajectories is essential for mitigating the impact of these extreme weather events. This study proposes a functional data analysis (FDA) framework for modeling and forecasting typhoon paths using historical trajectory data. Latitude and longitude sequences are represented as smooth functional observations, and a function-on-function regression model is employed to capture the temporal dynamics of typhoon movement. While the baseline model demonstrates reasonable performance for typical bow-shaped trajectories, it exhibits limited accuracy for non-standard paths. To address this limitation, a clustering-based extension is introduced, wherein typhoons are grouped by trajectory shape prior to regression. This two-stage approach improves predictive accuracy by enabling localized modeling adapted to structural variations in the data. The results demonstrate the practical utility of combining FDA with clustering for robust and flexible typhoon trajectory forecasting.

stat.AP↗

arXiv2025

Time-domain decoding of unconventional charge order mechanisms in nonmagnetic and magnetic kagome metals

In kagome lattice materials, quantum interplay between charge, spin, orbital, and lattice degrees of freedom gives rise to a remarkably rich set of emergent phenomena, ranging from unconventional charge order and superconductivity to topological magnetism. While the exact nature of these exotic orders is often challenging to comprehend in static experiments, time-resolved techniques can offer critical insights by disentangling coupled degrees of freedom on the time-axis. In this work, we demonstrate that the nature of charge orders in two representative kagome metals - nonmagnetic ScV6Sn6 and magnetic FeGe - which has been highly controversial in static studies, can be directly deciphered in the time-domain through their fundamentally distinct order parameter dynamics measured via time-resolved X-ray scattering at an X-ray free electron laser. In nonmagnetic ScV6Sn6, the dynamics are characterized by ultrafast melting and coherent amplitudon oscillations, typical of a phonon-coupled charge order. In stark contrast, magnetic FeGe exhibits resilient metastable charge order dynamics, hitherto unobserved in any other charge-ordered system - this unique time-domain behavior directly signifies an unconventional magnetism-interlocked charge order state realized in this kagome magnet. Our results not only provide a model case where unconventional nature of electronic order, hidden in equilibrium, is directly unraveled in the time-domain, but also pave the way for future out-of-equilibrium engineering of novel quantum orders in kagome lattice platforms.

cond-mat.str-el↗

arXiv2025

Modular integration of neural connectomics, dynamics and biomechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans

Computational approaches which emulate in-vivo nervous system are needed to investigate mechanisms of the brain to orchestrate behavior. Such approaches must integrate a series of biophysical models encompassing the nervous system, muscles, biomechanics to allow observing the system in its entirety while supporting model variations. Here we develop modWorm: a modular modeling framework for the nematode C. elegans. modWorm allows for construction of a model as an integrated series of configurable, exchangeable modules each describing specific biophysical processes across different modalities. Utilizing modWorm, we propose a base neuro-mechanical model for C. elegans built upon the complete connectome. The model integrates a series of 7 modules: i) intra-cellular dynamics, ii) electrical and iii) chemical extra-cellular neural dynamics, iv) translation of neural activity to muscle calcium dynamics, v) muscle calcium dynamics to muscle forces, vi) muscle forces to body postures and vii) proprioceptive feedback. We validate the base model by in-silico injection of constant currents into neurons known to be associated with locomotion behaviors and by applying external forces to the body. Applications of in-silico neural stimuli experimentally known to modulate locomotion show that the model can recapitulate natural behavioral responses such as forward and backward locomotion as well as mid-locomotion responses such as avoidance and turns. Furthermore, through in-silico ablation surveys, the model can infer novel neural circuits involved in sensorimotor behaviors. To further dissect mechanisms of locomotion, we utilize modWorm to introduce empirical based model variations and model optimizations to elucidate their effects on simulated locomotion. Our results show that modWorm can be utilized to identify neural circuits which control, mediate and generate natural behavior.

q-bio.QM↗

arXiv2024

Using $k$-means to sort spectra: electronic order mapping from scanning tunneling spectroscopy measurements

Hyperspectral imaging techniques have a unique ability to probe the inhomogeneity of material properties whether driven by compositional variation or other forms of phase segregation. In the doped cuprates, iridates, and related materials, scanning tunneling microscopy/spectroscopy (STM/STS) measurements have found the emergence of pseudogap 'puddles' from the macroscopically Mott insulating phase with increased doping. However, categorizing this hyperspectral data by electronic order is not trivial, and has often been done with ad hoc methods. In this paper we demonstrate the utility of $k$-means, a simple and easy-to-use unsupervised clustering method, as a tool for classifying heterogeneous scanning tunneling spectroscopy data by electronic order for Rh-doped Sr$_2$IrO$_{4}$, a cuprate-like material. Applied to STM data acquired within the Mott phase, $k$-means successfully identified areas of Mott order and of pseudogap order. The unsupervised nature of $k$-means limits avenues for bias, and provides clustered spectral shapes without a priori knowledge of the physics. Additionally, we demonstrate successful use of $k$-means as a preprocessing tool to constrain phenomenological function fitting. Clustering the data allows us to reduce the fitting parameter space, limiting over-fitting. We suggest $k$-means as a fast, simple model for processing hyperspectral data on materials of mixed electronic order.

cond-mat.str-el↗

arXiv2023

Quantum spin nematic phase in a square-lattice iridate

Spin nematic (SN) is a magnetic analog of classical liquid crystals, a fourth state of matter exhibiting characteristics of both liquid and solid. Particularly intriguing is a valence-bond SN, in which spins are quantum entangled to form a multi-polar order without breaking time-reversal symmetry, but its unambiguous experimental realization remains elusive. Here, we establish a SN phase in the square-lattice iridate Sr$_2$IrO$_4$, which approximately realizes a pseudospin one-half Heisenberg antiferromagnet (AF) in the strong spin-orbit coupling limit. Upon cooling, the transition into the SN phase at T$_C$ $\approx$ 263 K is marked by a divergence in the static spin quadrupole susceptibility extracted from our Raman spectra, and concomitant emergence of a collective mode associated with the spontaneous breaking of rotational symmetries. The quadrupolar order persists in the antiferromagnetic (AF) phase below T$_N$ $\approx$ 230 K, and becomes directly observable through its interference with the AF order in resonant x-ray diffraction, which allows us to uniquely determine its spatial structure. Further, we find using resonant inelastic x-ray scattering a complete breakdown of coherent magnon excitations at short-wavelength scales, suggesting a resonating-valence-bond-like quantum entanglement in the AF state. Taken together, our results reveal a quantum order underlying the N\'eel AF that is widely believed to be intimately connected to the mechanism of high temperature superconductivity (HTSC).

cond-mat.str-el↗

arXiv2023

Nature of charge density wave in kagome metal ScV6Sn6

Kagome lattice materials offer a fertile ground to discover novel quantum phases of matter, ranging from unconventional superconductivity and quantum spin liquids to charge orders of various profiles. However, understanding the genuine origin of the quantum phases in kagome materials is often challenging, owing to the intertwined atomic, electronic, and structural degrees of freedom. Here, we combine angle-resolved photoemission spectroscopy, phonon mode calculation, and chemical doping to elucidate the driving mechanism of the root3*root3 charge order in a newly discovered kagome metal ScV6Sn6. In contrast to the case of the archetype kagome system AV3Sb5 (A= K, Rb, Cs), the van Hove singularities in ScV6Sn6 remain intact across the charge order transition, indicating a marginal role of the electronic instability from the V kagome lattice. Instead, we identified a three-dimensional band with dominant planar Sn character opening a large charge order gap of 260 meV and strongly reconstructing the Fermi surface. Our complementary phonon dispersion calculations further emphasize the role of the structural components other than the V kagome lattice by revealing the unstable planar Sn and Sc phonon modes associated to the root3*root3 phase. Finally, in the constructed phase diagram of Sc(V1-xCrx)6Sn6, the charge order remains robust in a wide doping range x = 0 ~ 0.10 against the Fermi level shift up to ~ 120 meV, further making the electronic scenarios such as Fermi surface or saddle point nesting unlikely. Our multimodal investigations demonstrate that the physics of ScV6Sn6 is fundamentally different from the canonical kagome metal AV3Sb5, uncovering a new mechanism to induce symmetry-breaking phase transition in kagome lattice materials.

cond-mat.str-el↗

arXiv2023

Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography

Computed tomography (CT) has been used worldwide as a non-invasive test to assist in diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the effectiveness of learning-based methods, most were developed on the simulated data. However, the real-world scenario differs significantly from the simulation domain, especially when using the multi-slice spiral scanner geometry. This paper proposes a two-stage method for the commercially available multi-slice spiral CT scanners that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our approach makes good use of the high redundancy of multi-slice projections and the volumetric reconstructions while leveraging the over-smoothing problem in conventional cascaded frameworks caused by aggressive denoising. The dedicated design also provides a more explicit interpretation of the data flow. Extensive experiments on various datasets showed that the proposed method could remove up to 70\% of noise without compromised spatial resolution, and subjective evaluations by two experienced radiologists further supported its superior performance against state-of-the-art methods in clinical practice.

eess.IV↗

arXiv2022

Charge order landscape and competition with superconductivity in kagome metals

In kagome metals AV3Sb5 (A = K, Rb, Cs), three-dimensional charge order (3D-CO) is the primary instability that sets the stage for other collective orders to emerge, including unidirectional stripe order, orbital flux order, electronic nematicity, and superconductivity. Here, we use high-resolution angle-resolved photoemission spectroscopy to determine the microscopic structure of three-dimensional charge order (3D-CO) in AV3Sb5 and its interplay with superconductivity. Our approach is based on identifying an unusual splitting of kagome bands induced by 3D-CO, which provides a sensitive way to refine the spatial charge patterns in neighboring kagome planes. We found a marked dependence of the 3D-CO structure on composition and doping. The observed difference between CsV3Sb5 and the other compounds potentially underpins the double-dome superconductivity in CsV3(Sb,Sn)5 and the suppression of Tc in KV3Sb5 and RbV3Sb5. Our results provide fresh insights into the rich phase diagram of AV3Sb5.

cond-mat.str-el↗

arXiv2022

Single crystal growth of iridates without platinum impurities

Iridates have attracted much interest in the last decade for their novel magnetism emerging in the limit of strong spin-orbit coupling and possible unconventional superconductivity. A standard for growing iridate single crystals has been the flux method using platinum crucibles. Here, we show that this widely used method compromises the sample quality by inclusion of platinum impurities. We find that Sr2IrO4 single crystals grown in iridium crucibles show remarkable differences from those grown in platinum crucibles in their sample characterizations using Raman spectroscopy, resistivity, magnetization, optical third harmonic generation, resonant X-ray diffraction, and resonant inelastic X-ray scattering measurements. In particular, we show that several peaks of sizable intensities disappear in the Raman spectra of samples free of platinum impurities, and a significantly larger activation energy is extracted from the resistivity data compared to previously reported values. Furthermore, we find no evidence of the previously reported glide symmetry breaking structural distortions and confirm the I41/acd space group of the lattice symmetry. Although the platinum impurities are not apparent in the magnetic properties and thus went unnoticed in the stoichiometric insulating phase for a long time, their effects can be much more detrimental to transport properties in chemically doped compounds. Therefore, our result suggests using growth methods that avoid platinum impurities for an investigation of intrinsic physical properties of iridates, and possible superconducting phases.

cond-mat.mtrl-sci↗

arXiv2022

Sr$_2$IrO$_4$/Sr$_3$Ir$_2$O$_7$ superlattice for a model 2D quantum Heisenberg antiferromagnet

Spin-orbit entangled pseudospins hold promise for a wide array of exotic magnetism ranging from a Heisenberg antiferromagnet to a Kitaev spin liquid depending on the lattice and bonding geometry, but many of the host materials suffer from lattice distortions and deviate from idealized models in part due to inherent strong pseudospin-lattice coupling. Here, we report on the synthesis of a magnetic superlattice comprising the single ($n$=1) and the double ($n$=2) layer members of the Ruddlesden-Popper series iridates Sr$_{n+1}$Ir$_{n}$O$_{3n+1}$ alternating along the $c$-axis, and provide a comprehensive study of its lattice and magnetic structures using scanning transmission electron microscopy, resonant elastic and inelastic x-ray scattering, third harmonic generation measurements and Raman spectroscopy. The superlattice is free of the structural distortions reported for the parent phases and has a higher point group symmetry, while preserving the magnetic orders and pseudospin dynamics inherited from the parent phases, featuring two magnetic transitions with two symmetry-distinct orders. We infer weaker pseudospin-lattice coupling from the analysis of Raman spectra and attribute it to frustrated magnetic-elastic couplings. Thus, the superlattice expresses a near ideal network of effective spin-one-half moments on a square lattice.

cond-mat.str-el↗

arXiv2021

Twofold van Hove singularity and origin of charge order in topological kagome superconductor CsV3Sb5

The layered vanadium antimonides AV3Sb5 (A = K, Rb, Cs) are a recently discovered family of topological kagome metals with a rich phenomenology of strongly correlated electronic phases including charge order and superconductivity. Understanding how the singularities inherent to the kagome electronic structure are linked to the observed many-body phases is a topic of great interest and relevance. Here, we combine angle-resolved photoemission spectroscopy and density functional theory to reveal multiple kagome-derived van Hove singularities (vHs) coexisting near the Fermi level of CsV3Sb5 and analyze their contribution to electronic symmetry breaking. Intriguingly, the vHs in CsV3Sb5 have two distinct flavors - p-type and m-type - which originate from their pure and mixed sublattice characters, respectively. This twofold vHs is unique property of the kagome lattice, and its flavor critically determines the pairing symmetry and ground states emerging in AV3Sb5 series. We establish that, among the multiple vHs in CsV3Sb5, the m-type vHs of the dxz/dyz kagome band and the p-type vHs of the dxy/dx2-y2 kagome band cross the Fermi level to set the stage for electronic symmetry breaking. The former band exhibits pronounced Fermi surface nesting, while the latter contributes via higher-order vHs. Our work reveals the essential role of kagome-derived vHs for the collective phenomena realized in the AV3Sb5 family, paving the way to a deeper understanding of strongly correlated topological kagome systems.

cond-mat.str-el↗

arXiv2020

Deep Reinforcement Learning for Neural Control

We present a novel methodology for control of neural circuits based on deep reinforcement learning. Our approach achieves aimed behavior by generating external continuous stimulation of existing neural circuits (neuromodulation control) or modulations of neural circuits architecture (connectome control). Both forms of control are challenging due to nonlinear and recurrent complexity of neural activity. To infer candidate control policies, our approach maps neural circuits and their connectome into a grid-world like setting and infers the actions needed to achieve aimed behavior. The actions are inferred by adaptation of deep Q-learning methods known for their robust performance in navigating grid-worlds. We apply our approach to the model of \textit{C. elegans} which simulates the full somatic nervous system with muscles and body. Our framework successfully infers neuropeptidic currents and synaptic architectures for control of chemotaxis. Our findings are consistent with in vivo measurements and provide additional insights into neural control of chemotaxis. We further demonstrate the generality and scalability of our methods by inferring chemotactic neural circuits from scratch.

q-bio.NC↗