arXiv subjects

Junhao Liu

Publications and source records attributed to Junhao Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

A programmable stellarator-tokamak hybrid for million-scale magnetic-configuration discovery

Tokamaks and stellarators are the leading magnetic-confinement concepts for fusion, but they rely on complementary design principles. Tokamaks use simple axisymmetric coils and plasma current, whereas stellarators use externally generated three-dimensional fields for steady-state operation. Here, we propose a programmable stellarator--tokamak hybrid that uses a fixed set of simple planar coils to access a broad magnetic-configuration space. The device adds 288 dipole-field coils to a tokamak-like coil set, with only six independent coil geometries required by symmetry. By programming coil currents, the same hardware generates more than 1.66 million optimized stellarator configurations spanning quasi-axisymmetry, quasi-helical symmetry, and quasi-isodynamicity, as well as tokamak-relevant three-dimensional perturbations. Representative configurations exhibit nested magnetic surfaces, low neoclassical transport, and favorable energetic-particle confinement. This approach enables rapid magnetic-configuration discovery without hardware redesign.

physics.plasm-ph↗

arXiv2026

FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling

Single-cell ATAC-seq (scATAC-seq) enables high-resolution mapping of chromatin accessibility, yet privacy regulations and data size constraints hinder multi-institutional sharing. Federated learning (FL) offers a privacy-preserving alternative, but faces three fundamental barriers in scATAC-seq analysis: ultra-high dimensionality, extreme sparsity, and severe cross-institutional heterogeneity. We propose FL-Sailer, the first FL framework designed for scATAC-seq data. FL-Sailer integrates two key innovations: (i) adaptive leverage score sampling, which selects biologically interpretable features while reducing dimensionality by 80%, and (ii) an invariant VAE architecture, which disentangles biological signals from technical confounders via mutual information minimization. We provide a convergence guarantee, showing that FL-Sailer converges to an approximate solution of the original high-dimensional problem with bounded error. Extensive experiments on synthetic and real epigenomic datasets demonstrate that FL-Sailer not only enables previously infeasible multi-institutional collaborations but also surpasses centralized methods by leveraging adaptive sampling as an implicit regularizer to suppress technical noise. Our work establishes that federated learning, when tailored to domain-specific challenges, can become a superior paradigm for collaborative epigenomic research.

cs.LG↗

arXiv2026

Phase-Stable Self-Modulation for GHz Continuous-Wave Ultrafast X-Ray Free-Electron Lasers

High-brightness femtosecond-to-attosecond pulses are indispensable for probing electron dynamics on their fundamental temporal scales. X-ray free-electron lasers (XFELs) at high repetition rates will facilitate high-statistics measurements and time-resolved studies that were previously inaccessible. Although energy recovery linacs (ERLs) are well suited for high-repetition-rate operation, their relatively low peak current poses a major challenge for generating intense ultrashort X-ray pulses. Here, we propose a completely laser-free scheme that fundamentally overcomes this bottleneck through a continuous, phase-stable self-modulation process. By interacting with its own coherently emitted terahertz radiation within a helical wiggler, the electron bunch naturally accumulates a robust, few-cycle energy modulation in its core, even when starting with the intrinsically low peak current typical of ERLs. A downstream dispersion chicane subsequently converts this energy modulation into an isolated, exceptionally sharp current spike. Start-to-end simulations based on a 1~GeV ERL light source demonstrate the feasibility of generating isolated soft X-ray pulses with an average peak power exceeding 4~GW and a pulse duration of about 1~fs at an unprecedented 1.3~GHz repetition rate. The proposed scheme offers a highly practical pathway for advancing ultrafast X-ray generation into the true continuous-wave regime, with transformative implications for the development of next-generation coherent light sources.

physics.acc-ph↗

arXiv2026

Revitalizing Black-Box Interpretability: Actionable Interpretability for LLMs via Proxy Models

Post-hoc explanations provide transparency and are essential for guiding model optimization, such as prompt engineering and data sanitation. However, applying model-agnostic techniques to Large Language Models (LLMs) is hindered by prohibitive computational costs, rendering these tools dormant for real-world applications. To revitalize model-agnostic interpretability, we propose a budget-friendly proxy framework that leverages efficient models to approximate the decision boundaries of expensive LLMs. We introduce a screen-and-apply mechanism to statistically verify local alignment before deployment. Our empirical evaluation confirms that proxy explanations achieve over 90% fidelity with only 11% of the oracle's cost. Building on this foundation, we demonstrate the actionable utility of our framework in prompt compression and poisoned example removal. Results show that reliable proxy explanations effectively guide optimization, transforming interpretability from a passive observation tool into a scalable primitive for LLM development. Additionally, we open-source code and datasets to facilitate future research.

cs.LG↗

arXiv2026

WASD: Locating Critical Neurons as Sufficient Conditions for Explaining and Controlling LLM Behavior

Precise behavioral control of large language models (LLMs) is critical for complex applications. However, existing methods often incur high training costs, lack natural language controllability, or compromise semantic coherence. To bridge this gap, we propose WASD (unWeaving Actionable Sufficient Directives), a novel framework that explains model behavior by identifying sufficient neural conditions for token generation. Our method represents candidate conditions as neuron-activation predicates and iteratively searches for a minimal set that guarantees the current output under input perturbations. Experiments on SST-2 and CounterFact with the Gemma-2-2B model demonstrate that our approach produces explanations that are more stable, accurate, and concise than conventional attribution graphs. Moreover, through a case study on controlling cross-lingual output generation, we validated the practical effectiveness of WASD in controlling model behavior.

cs.CL↗

arXiv2026

Mott-Derived Local Moments and Kondo Hybridization in a d-electron Kagome lattice

Unlike canonical Kondo lattices in f-electron systems, where localized f orbitalsnaturally provide local moments, d-electron Kondo lattices require a distinct mechanism for local-moment formation. However, the study of d-electron Kondo lattices in bulk materials remains far from settled, particularly with regard to the microscopic origin of the local moments. Here, we report a microscopic mechanism for this process in the bilayer kagome metal CsCr6Sb6, where strong correlations drive a Mott splitting of the kagome flat band to supply the requisite local moments. By combining STM/STS and ARPES, we resolve a spectroscopic hierarchy between high-energy correlation effects and low temperature hybridization. Low-temperature STS reveals a robust asymmetric suppression of the density of states near EF that is well captured phenomenologically by a Fano-type lineshape, while ARPES detects a sharp quasiparticlepeak near EF. These low-energy signatures evolveon the same temperature scale and disappear upon warming, consistent with the onset of Kondo hybridization. At the same time, STS resolves symmetric humps at approximately +-50 mV and ARPES identifies a weakly dispersive feature around 50 meV below EF; unlike the near-EF hybridization signatures, these features persist to substantially higher temperatures. This separation of energy and temperature scales supports a two-stage picture in which a kagome flat band first undergoes correlation-driven splitting into lower and upper Hubbard bands, and the occupied lower Hubbard band supplies the local moments that later hybridize with itinerant electrons at lower temperature. Our results therefore move beyond the phenomenology of a kagome Kondo lattice candidate and instead provide a microscopic spectroscopic picture linking Mottness to Kondo hybridization in a frustrated d-electron system.

cond-mat.str-el↗

arXiv2026

Beyond Attribution: Unified Concept-Level Explanations

There is an increasing need to integrate model-agnostic explanation techniques with concept-based approaches, as the former can explain models across different architectures while the latter makes explanations more faithful and understandable to end-users. However, existing concept-based model-agnostic explanation methods are limited in scope, mainly focusing on attribution-based explanations while neglecting diverse forms like sufficient conditions and counterfactuals, thus narrowing their utility. To bridge this gap, we propose a general framework UnCLE to elevate existing local model-agnostic techniques to provide concept-based explanations. Our key insight is that we can uniformly extend existing local model-agnostic methods to provide unified concept-based explanations with large pre-trained model perturbation. We have instantiated UnCLE to provide concept-based explanations in three forms: attributions, sufficient conditions, and counterfactuals, and applied it to popular text, image, and multimodal models. Our evaluation results demonstrate that UnCLE provides explanations more faithful than state-of-the-art concept-based explanation methods, and provides richer explanation forms that satisfy various user needs.

cs.LG↗

arXiv2026

SC-Arena: A Natural Language Benchmark for Single-Cell Reasoning with Knowledge-Augmented Evaluation

Large language models (LLMs) are increasingly applied in scientific research, offering new capabilities for knowledge discovery and reasoning. In single-cell biology, however, evaluation practices for both general and specialized LLMs remain inadequate: existing benchmarks are fragmented across tasks, adopt formats such as multiple-choice classification that diverge from real-world usage, and rely on metrics lacking interpretability and biological grounding. We present SC-ARENA, a natural language evaluation framework tailored to single-cell foundation models. SC-ARENA formalizes a virtual cell abstraction that unifies evaluation targets by representing both intrinsic attributes and gene-level interactions. Within this paradigm, we define five natural language tasks (cell type annotation, captioning, generation, perturbation prediction, and scientific QA) that probe core reasoning capabilities in cellular biology. To overcome the limitations of brittle string-matching metrics, we introduce knowledge-augmented evaluation, which incorporates external ontologies, marker databases, and scientific literature to support biologically faithful and interpretable judgments. Experiments and analysis across both general-purpose and domain-specialized LLMs demonstrate that (i) under the Virtual Cell unified evaluation paradigm, current models achieve uneven performance on biologically complex tasks, particularly those demanding mechanistic or causal understanding; and (ii) our knowledge-augmented evaluation framework ensures biological correctness, provides interpretable, evidence-grounded rationales, and achieves high discriminative capacity, overcoming the brittleness and opacity of conventional metrics. SC-Arena thus provides a unified and interpretable framework for assessing LLMs in single-cell biology, pointing toward the development of biology-aligned, generalizable foundation models.

cs.AI↗

arXiv2026

Digging into the Interior of Hot Cores with ALMA (DIHCA). VI. The Formation of Low-mass Multiple Systems in High-mass Cluster-forming Regions

Most stars form in multiple systems, with profound implications in numerous astronomical phenomena intrinsically linked to multiplicity. However, our knowledge about the process on how multiple stellar systems form is incomplete and biased toward nearby molecular clouds forming only low-mass stars, which are unrepresentative of the stellar population in the Galaxy. Most stars form within dense cores in clusters alongside high-mass stars (>8 M$_{\odot}$), as likely the Sun did. Here we report deep ALMA 1.33 mm dust continuum observations at ~160 au spatial resolution, revealing 72 low-mass multiple systems embedded in 23 high-mass cluster-forming regions, as part of the Digging into the Interior of Hot Cores with ALMA (DIHCA) survey. We find that the companion separation distribution presents a distinct peak at ~1200 au, in contrast to the one at ~4000 au observed in nearby low-mass regions. The shorter fragmentation scale can be explained by considering the higher pressure exerted by the surrounding medium, which is higher than the one in low-mass regions, due to the larger turbulence and densities involved. Because the peak of the companion separation distribution occurs at much larger scales than the expected disk sizes, we argue that the observed fragmentation is produced by turbulent core fragmentation. Contrary as predicted, the multiplicity fraction remains constant as the stellar density increases. We propose that in the extremely dense environments where high-mass stars form, dynamical interactions play an important role in disrupting weakly bound systems.

astro-ph.GA↗

arXiv2026

Focus-LIME: Surgical Interpretation of Long-Context Large Language Models via Proxy-Based Neighborhood Selection

As Large Language Models (LLMs) scale to handle massive context windows, achieving surgical feature-level interpretation is essential for high-stakes tasks like legal auditing and code debugging. However, existing local model-agnostic explanation methods face a critical dilemma in these scenarios: feature-based methods suffer from attribution dilution due to high feature dimensionality, thus failing to provide faithful explanations. In this paper, we propose Focus-LIME, a coarse-to-fine framework designed to restore the tractability of surgical interpretation. Focus-LIME utilizes a proxy model to curate the perturbation neighborhood, allowing the target model to perform fine-grained attribution exclusively within the optimized context. Empirical evaluations on long-context benchmarks demonstrate that our method makes surgical explanations practicable and provides faithful explanations to users.

cs.CL↗

arXiv2026

Magnetic threads and gravity: ALMA Observations of IRDC G14.225-0.506

During the star formation process, the interplay between gravity, turbulence, and B-fields is significant, with B-fields apparently serving a regulatory function. However, the extent to which B-fields are decisive relative to turbulence and gravity remains uncertain. This study aims to ascertain the role of B-fields in the fragmentation of molecular clouds. We examine the B-field observed with ALMA at core scales towards the infrared dark cloud G14.225-0.506, focusing on 3 regions with shared physical conditions, and juxtapose it with prior observations at the Hub-filament system scale. Our findings indicate a similar B-field strength and fragmentation level between the 2 hubs. However, distinct B-field morphologies are identified across the 3 regions where polarized emission is detected. In the region N, the large-scale B-field, which is perpendicular to the filamentary structure, persists at smaller scales in the southern half but becomes distorted near the more massive condensations in the northern half. Notably, these condensations exhibit signs of impending collapse, as evidenced by supercritical mass-to-flux values. In the region S, the B-field is considerably inhomogeneous among the detected condensations, and we do not observe a direct correlation between the field morphology and the condensation density. Lastly, in an isolated dust clump located within a southern filament of the northern hub, the B-field aligns parallel to the elongated emission, suggesting a transition in the field geometry. The B-field shows a clear evolution with spatial scales. We propose that the most massive condensations detected in the northern Hub are undergoing gravitational collapse, as revealed by the relative significance of the magnetic field and gravitational potential and mass-to-flux ratio. The distortion of the B-field could be a response to the flow of material due to the collapse.

astro-ph.GA↗

arXiv2025

Deep Andromeda JCMT-SCUBA2 Observations. The Submillimeter Maps and Giant Molecular Clouds

We have carried out unprecedentedly deep, nearly confusion-limited JCMT-SCUBA2 mapping observations on the nearest spiral galaxy, M31 (Andromeda). The 850 $μ$m image with a $\sim$50 pc resolution yields a comprehensive catalog of 383 giant molecular clouds (GMCs) that are associated with the spiral arms. In addition, it unveiled a population of 189 compact inter-arm GMCs in M31, which are mostly unresolved or marginally resolved. The masses of all these GMCs are in the range of 2$\times$10$^4$ -- 6$\times$10$^6$ $M_{\odot}$; the sizes are in the range of 30--130 pc. They follow a mass-size correlation, $M$ $\propto$ $R_{c}$$^{2.5}$. The inter-arm GMCs are systematically less massive, more diffuse, colder, and have lower star-forming efficiency (SFE) than on-arm GMCs. Moreover, within individual spatially resolved on-arm and off-arm M31 GMCs, the SFE is considerably lower than the SFE in molecular clouds in main sequence and green valley galaxies. Follow-up investigations on M31 GMCs may provide clues for how star formation may be quenched in galactic environments. Finally, we reconstrained the dust opacity spectral index $β$ in the M31 galaxy by combining our new JCMT observations with archival Herschel and Planck data and found that the radial variation of $β$ may not be as large as was proposed by previous studies.

astro-ph.GA↗

arXiv2025

Lattice design of a storage-ring-based light source for generating high-power fully coherent EUV radiation

We present the physical design and systematic optimization of a high-performance storage ring tailored for the generation of high-power coherent radiation, with particular emphasis on the extreme ultraviolet (EUV) regime. The proposed ring adopts a Double Bend Achromat (DBA) lattice configuration and integrates 12 superconducting wigglers to significantly enhance radiation damping and minimize the natural emittance. And a bypass line is adopted to generate high power coherent radiation. Comprehensive linear and nonlinear beam dynamics analyses have been conducted to ensure beam stability and robustness across the operational parameter space. The optimized design achieves a natural emittance of approximately 0.8 nm and a longitudinal damping time of around 1.4 ms, enabling the efficient buildup of coherent radiation. Three-dimensional numerical simulations, incorporating the previously proposed angular dispersion-induced microbunching (ADM) mechanism, further confirm the system's capability to generate high-power EUV coherent radiation, with output powers reaching the order of several hundred watts. These results underscore the strong potential of the proposed design for applications in coherent photon science and EUV lithography.

physics.acc-ph↗

arXiv2025

Magnetic Fields in Massive Star-forming Regions (MagMaR). VI. Magnetic Field Dragging in the Filamentary High-mass Star-forming Region G35.20--0.74N due to Gravity

We investigate the magnetic field orientation and strength in the massive star-forming region G35.20-0.74N (G35), using polarized dust emission data obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) as part of the Magnetic fields in Massive star-forming Regions (MagMaR) survey. The G35 region shows a filamentary structure (a length of $\sim$0.1 pc) with six bright cores located along the filament's long axis. Magnetic field strengths across the G35 region range from 0.2 to 4.4 mG with a mean value of 0.8 $\pm$ 0.4 mG. The mass-to-flux ratio ($λ$) varies from 0.1 to 6.0 the critical value. The highest values are found locally around cores, whereas the remains of the filament are subcritical. A H$^{13}$CO$^+$ (3--2) velocity gradient of 29 km s$^{-1}$ pc$^{-1}$ is evident along the filament's long axis, aligned with the magnetic field direction. At larger scales ($\sim$0.1 pc), the magnetic field lines appear roughly perpendicular to the filament's long axis, in contrast to the smaller-scale structure ($\sim$0.003 pc) traced by ALMA. The magnetic field lines could be dragged along the filament as a result of the gas motion induced by the gravitational potential of the filament. Six cores in the filament have similar spacings between 0.02--0.04 pc. The initial filament fragmentation could have produced a core spacing of 0.06 pc, following filament fragmentation theory, and the current core spacing is the result of cores comoving with the gas along the filament. This core migration could occur in a few 10$^4$ years, consistent with high-mass star formation time scales.

astro-ph.GA↗

arXiv2025

Attosecond Waveform Synthesis through Echo-enabled Harmonic Generation Free-electron Lasers

Attosecond pulse trains (APTs) are indispensable for probing electron dynamics at their intrinsic timescales. High harmonic generation (HHG) has long been a successful and widely used technique in producing extreme ultraviolet APTs. While in the soft X-ray regime, HHG suffers from low conversion efficiency and lacking flexibility in the waveform and spectrum control. Here in this study, based on the waveform synthesis of the echo-enabled harmonic generation (EEHG) free-electron laser (FEL), we propose a novel method that can generate soft X-ray APTs with high temporal and spectral tunability. EEHG scheme is well-known in forming phase-locked high harmonic bunching combs down to the soft X-ray regime. And We can facilitate successive FEL lasing in several different harmonic numbers and then perform the coherent waveform synthesis to generate soft X-ray APTs. Three-dimensional simulation results indicate that reproducible generation of coherent APTs in the soft X-ray regime with peak power of 3.5 GW and micropulse duration of 160 as can be achieved by five-harmonic synthesis. And the micropulse duration together with the spectral components of the APTs can be adjusted easily according to the programmable high harmonic bunching combs in the EEHG scheme. This method has the potential to establish a robust platform for attosecond sciences in the soft X-ray regime, which can enable unprecedented studies of electron dynamics in physical and chemical reactions, biological systems and quantum materials.

physics.acc-ph↗

arXiv2025

Detection of a septuple stellar system in formation via disk fragmentation

Stellar multiple systems play a pivotal role in cluster dynamics and stellar evolution, leading to intense astronomical phenomena like X-ray binaries, gamma-ray bursts, Type Ia supernova, and stellar mergers, which are prime sources of gravitational waves. However, their origin remains poorly understood. Here we report the discovery of a septuple protostellar system embedded in a Keplerian disk within the high-mass star-forming region NGC\,6334IN, with close separations of 181-461 AU. The stability analysis reveals that the disk surrounding the septuple system is dynamically unstable, indicating that the septuple system formed via disk fragmentation. Previous studies have typically found only 2--3 members forming via disk fragmentation in both low- and high-mass star-forming regions. Our findings provide compelling observational evidence that the fragmentation of a gravitationally unstable disk is a viable mechanism for the formation of extreme high-order multiplicity, confirming what was previously only a theoretical concept. The results shed new light on the formation of extreme high-order multiplicity in cluster environments.

astro-ph.GA↗

arXiv2025

Impact of gravity on changing magnetic field orientations in a sample of massive protostellar clusters observed with ALMA

The magnetic field is integral to our understanding of the formation and dynamical evolution of molecular clouds and star formation within. We present a polarimetric survey of 17 massive protostellar cluster forming clumps, covered in 34 pointings in the 230-GHz window using the Atacama Large Millimeter/submillimeter Array (ALMA). The two array configurations, C43-1 and C43-4, probe linearly polarized dust emission, hence the plane-of-the-sky orientation of magnetic fields, at resolutions of 1\arcsec\ and 0\arcsec.4 that correspond to approximately 0.01pc core and $10^3$ au envelope scales, respectively. The relative orientations (ROs) of the magnetic field probed at two spatial scales are analyzed for the entire protostellar cluster sample and for a subset of objects in NGC 6334. We found a bimodal distribution of ROs with peaks at 0° (parallel) and 90°(orthogonal) for the entire sample combined as well as for NGC 6334. We investigate the physical origin of this bimodal distribution through a projected Rayleigh statistic (PRS) analysis in relation to column densities and local gravity in NGC 6334. We found an excess of parallel magnetic fields at column densities $> 10^{23}$ \cmm. The underlying cause of the RO distribution of the magnetic field is gravitational collapse at higher gas densities, which drags and reorients the magnetic field as shown in the alignment between the magnetic field and the direction of gravitational forces. The distribution of ROs observed here is consistent with the evolution of relative orientations of an initially sub-Alvénic cloud that becomes magnetically super-critical and super-Alvénic as the cloud collapses to form stars.

astro-ph.GA↗

arXiv2025

From Thinking to Output: Chain-of-Thought and Text Generation Characteristics in Reasoning Language Models

Recently, there have been notable advancements in large language models (LLMs), demonstrating their growing abilities in complex reasoning. However, existing research largely overlooks a thorough and systematic comparison of these models' reasoning processes and outputs, particularly regarding their self-reflection pattern (also termed "Aha moment") and the interconnections across diverse domains. This paper proposes a novel framework for analyzing the reasoning characteristics of four cutting-edge large reasoning models (GPT-o1, DeepSeek-R1, Kimi-k1.5, and Grok-3) using keywords statistic and LLM-as-a-judge paradigm. Our approach connects their internal thinking processes with their final outputs. A diverse dataset consists of real-world scenario-based questions covering logical deduction, causal inference, and multi-step problem-solving. Additionally, a set of metrics is put forward to assess both the coherence of reasoning and the accuracy of the outputs. The research results uncover various patterns of how these models balance exploration and exploitation, deal with problems, and reach conclusions during the reasoning process. Through quantitative and qualitative comparisons, disparities among these models are identified in aspects such as the depth of reasoning, the reliance on intermediate steps, and the degree of similarity between their thinking processes and output patterns and those of GPT-o1. This work offers valuable insights into the trade-off between computational efficiency and reasoning robustness and provides practical recommendations for enhancing model design and evaluation in practical applications. We publicly release our project at: https://github.com/ChangWenhan/FromThinking2Output

cs.CL↗