arXiv subjects

Jiaxuan Li

Publications and source records attributed to Jiaxuan Li.

At least 19 recordsLinked to original sources

arXiv2026

Assignment-Free Swarm Navigation via Adaptive Target-Field Planning and Mean-Shift Exploration

Mean-shift exploration enables assignment-free swarm shape assembly, but obstacle-constrained navigation additionally requires coordinated adaptation of the desired morphology and its physical execution. This paper presents an environment-adaptive navigation framework that integrates target-field planning, execution regulation, and morphology-preserving mean-shift exploration. A feasibility-aware graph search plans the position, orientation, and morphology of an unlabeled target field without specifying robot-specific goals. An execution-regulated reference manager adjusts field progression and morphology switching using swarm entering, coverage, and lag metrics. A moving-frame controller separates prescribed rigid motion from local mean-shift redistribution and incorporates neighbor interactions and obstacle responses. Simulations and hardware experiments demonstrate that the proposed framework coordinates morphology transitions and collective motion for assignment-free swarm navigation through cluttered environments.

eess.SY↗

arXiv2026

Feeling of Knowing in Large Language Models

Post-training updates change what large language models (LLMs) can answer, but may leave their answerability judgments misaligned. We study this knowledge-judgment desynchronization through functional feeling of knowing (FoK), which supports selective generation, retrieval triggering, and model routing without implying subjective experience. We propose two trainable channels: a knowledge channel that changes answerability and a FoK channel that judges the current knowledge state. Three stages train them: on-policy supervised fine-tuning (OP-SFT) updates knowledge from corrected on-policy answers; knowledge-delta FoK training (K$Δ$-FT) adapts judgments across intermediate knowledge states; and FoK-guided policy optimization (FGPO) trains the subsequent answer or explanation. Factual-QA experiments, cross-dataset evaluation, and three sequential updates show that explicit FoK alignment improves judgment quality on updated knowledge. MMLU and ARC-Easy separately assess capability preservation.

cs.AI↗

arXiv2026

(LRDs)$^2$: The Low-ReDshift Little Red Dots Survey. II. DESI DR1 Sample

JWST has revealed a substantial population of "Little Red Dots" (LRDs) at $z>4$, challenging conventional AGN frameworks. However, the low-redshift regime remains largely unexplored. In the second paper of the (LRDs)$^2$ series, we present a systematic selection from DESI DR1 and identify 27 LRDs at $z=0.2-0.9$, yielding a number density lower limit of $7.5\times10^{-10}$ cMpc$^{-3}$. We conducted near-IR spectroscopic follow-up observations for 21 of them, revealing their full SED shapes and emission lines. These low-$z$ LRDs share the hallmark properties of their high-$z$ counterparts: compact morphology, V-shaped UV-optical continua, broad Balmer emission with extreme decrements (median H$α$/H$β\sim15$), frequent Balmer absorption (70%), and blackbody-like optical-to-near-IR continua. All have low metallicity, occupy the same regions in the BPT diagram as high-$z$ LRDs, and have softer ionizing spectra than typical AGNs. The consistency between low-$z$ and high-$z$ LRD properties indicates the same physical processes at work. The correlation between broad-line Balmer luminosity and $L_{5100}$ deviates from that of local type-1 AGNs, limiting the direct application of local BH mass calibrations. Ionized [O III] outflows are ubiquitous (78%). One LRD at $z=0.196$, J1717+3807, shows robust long-term variability in $i$ and WISE bands. The optical-to-NIR continua of LRDs reveal a wide range of temperatures $\sim2000-4700$ K (peak $0.6-1.4$ $μ$m), with a subset showing cooler and larger envelopes than those at high $z$. Low-$z$ LRDs serve not only as proximate laboratories for probing the nature of LRDs, but also trace the cosmic evolution of this population from the cosmic dawn to the present day.

astro-ph.GA↗

arXiv2026

Thermal Conductivity and Temperature-Induced Band Gap Renormalization in Crystalline and Amorphous Ga$_2$O$_3$

The lattice thermal conductivity (LTC) and electron-phonon interactions in crystalline and amorphous gallium oxide are herein determined by coupling a machine-learned interatomic potential, namely the moment tensor potential (MTP) model, to first-principles calculations. Crystalline $β$-Ga$_2$O$_3$ exhibits a substantial band gap renormalization (BGR) of $\sim$0.45 eV at 700 K, with $\sim$0.2 eV caused by zero-point BGR. The computed temperature dependence of BGR induced by classical nuclear motion in $β$-Ga$_2$O$_3$ is stronger than that in amorphous Ga$_2$O$_3$, with the difference in BGR reaching $\sim$0.18 eV at 900 K. Thermal transport calculations reveal that the LTC of amorphous Ga$_2$O$_3$ remains near $0.9$ W$\cdot$ m$^{-1}$$\cdot$K$^{-1}$ for temperatures between 300 K and 700 K, which is approximately an order of magnitude lower than that of crystalline $β$-Ga$_2$O$_3$. Overall, the presented framework provides a computationally tractable and reliable route for predicting properties of semiconductors (both crystalline and amorphous) under operating conditions relevant to microelectronics and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2026

The Merian Survey: A Statistical Census of Bright Satellites of Sub-Milky Way-Mass Galaxies

Satellite populations provide sensitive tests of the galaxy-halo connection, but studies have largely focused on Milky Way (MW)-mass host galaxies or a small number of nearby dwarf galaxy hosts. Here we present a statistical census of bright, star-forming satellite galaxies around sub-MW-mass hosts using the first data release of the Merian Survey. Our sample consists of 357 hosts with stellar mass $10^{9.5} 3$. Satellite abundance increases with host stellar mass across the sub-MW regime, extending the observed trend from dwarf to MW-mass galaxies and broadly agreeing with predictions from the TNG50 simulation. The radial distribution of satellites around sub-MW-mass hosts is similar to that around MW-mass hosts and broadly consistent with TNG50. Together, these results extend our empirical view of satellite populations across the previously sparsely sampled transition between dwarf and MW-mass hosts.

astro-ph.GA↗

arXiv2026

Verbalized and Internal Probabilities Are Coupled in Large Language Models

Large language models carry an internal notion of uncertainty in their sampling distribution, i.e., the probabilities they place on generating one answer rather than another. They can also be asked to state a confidence, in words or as a number: a verbalized uncertainty. Prior work suggests that internal probabilities track relative frequencies in the training data, and that verbalized probabilities track explicit probabilistic assertions in the training data. However, we do not know whether these two readouts are aligned, except when frequencies and probabilistic assertions in the training data happen to align. This limits our understanding of when we can use verbalized uncertainties as a proxy for either training data frequencies, or a model's internal distribution. We resolve this gap by systematically exploring how LLMs probability readouts are impacted by training and in-context data, via intervening on the underlying uncertainty sources in the data. We find that both internal and verbalized probability readouts are impacted by both distributional and asserted uncertainty in the training data. Further, we find that verbalized and internal probabilities are aligned beyond what would be expected by independently tracking the same uncertainty sources, suggesting that verbalized probabilities can be used to probe a model's internal distribution.

cs.CL↗

arXiv2026

Galaxies Below the Fundamental Metallicity Relation Are Morphologically Disturbed: Merian H$α$ Morphologies and DESI Metallicities

Empirical results have revealed a connection between a galaxy's metallicity, mass, and star formation rate, known as the fundamental metallicity relation (FMR), in which metallicity increases with stellar mass and decreases with SFR. Physical interpretations of the FMR suggest that actively star-forming galaxies are fueled by pristine gas, likely obtained via IGM accretion or minor mergers. Here we demonstrate the value of galaxy morphology as an additional axis in the FMR. Using medium-band imaging from the Merian Survey, we construct spatially resolved maps of stellar continuum and H$α$ emission for a large sample of galaxies with DESI DR1 spectra, reaching stellar masses down to $10^8$ M$_\odot$. We measure non-parametric morphological parameters from the emission maps and derive gas-phase oxygen abundances from the spectra using strong-line calibrations. We fit a series of simple linear models to explore correlations among the metallicity, stellar mass, sSFR, and morphological parameters. We find that galaxies that are more metal-poor than predicted by the FMR appear particularly disturbed. At fixed stellar mass and sSFR, metallicity is inversely correlated with the asymmetry of both the continuum and H$α$ morphology. The observed trends -- and the existence of particularly metal-poor, morphologically disturbed, highly star-forming galaxies -- are consistent with external burst triggering. Our results indicate that a galaxy's morphology is linked to its metallicity and star formation history, providing an additional observational probe of the physical drivers of elevated star-formation in low-mass galaxies.

astro-ph.GA↗

arXiv2026

From starlight to dark matter: a stochastic interpolation approach to map dark matter from stellar density

The dark matter halo profile in galaxies holds key information about the nature of dark matter and galaxy formation. Constraining the dark matter profile of galaxies beyond the Milky Way traditionally requires expensive spectroscopic observations for kinematic information. In this paper, we explore a conditional generative model framework to map the dark matter profile of Milky Way-mass galaxies from stellar density profiles, more easily obtainable through large photometric imaging surveys. As a proof of concept, we train the model to learn a stochastic bridge between instrument systematics-free baryonic stellar distributions and underlying dark matter density maps from the DREAMS hydrodynamics simulation suite. We recover 2D dark matter density profiles with a typical accuracy of $\sim0.1$ dex ($\sim1.5$% of the truth in log scale). The stochastic sampling procedure provides uncertainty estimates of the predicted dark matter map, with typical values $\sim0.1$ dex. Out-of-domain tests with Milky Way-mass galaxies from IllustrisTNG and FIRE simulations show that, while the model can qualitatively be generalized to TNG50 galaxies from IllustrisTNG, the model is sensitive to the galaxy formation model, with $\sim0.2$-$0.4$ dex over-prediction for the inner profiles ($r\lesssim5$ kpc) of the FIRE test galaxies. Future work will explore training with additional suites of simulations and/or conditioning on additional information, such as multi-band images. Our results are a first step towards using generative models as a flexible, uncertainty-aware framework for turning forthcoming data from large imaging surveys into spatially resolved dark matter maps.

astro-ph.GA↗

arXiv2026

Rubin LSST DP2 unveils almost-dark galaxies in the Virgo Cluster

Galaxies with the faintest surface brightness are currently known only in the Local Group. Similar objects should exist beyond our vicinity and are crucial for understanding galaxy evolution, structure, and dark matter content, yet surveys have not reached the depth required to detect them systematically. We present a population of seven almost-dark galaxies identified in Data Preview 2 of the Rubin Legacy Survey of Space and Time. They surround M49 in the vicinity of the Virgo Cluster, and exhibit central surface brightnesses in the range of $26.9 - 28.5 \, \mathrm{mag \, arcsec^{-2}}$ in the $g$-band, with half-light radii of $0.6 - 4.6 \, \mathrm{kpc}$ at the distance of Virgo, and stellar masses of $10^6 - 10^7 \, \mathrm{M_\odot}$. Their characteristics are analogous to those of the faintest and low-mass galaxies identified among satellite galaxies And XXI, And XXIII, and And XXV in the Local Group. This discovery demonstrates the power of the forthcoming Rubin LSST 10-year survey to uncover extremely faint galaxies at scale, promising the large statistical samples needed to constrain the faint-end luminosity function and the nature of dark matter.

astro-ph.GA↗

arXiv2026

Wearable Multimodal Human-Machine Interface for Integrated Hand Intentions Decoding in Dynamic Teleoperation

Under ubiquitous teleoperation environments with optically challenging conditions, an interface for tele-operated grasping that combines wearability with precise decoding of hand intentions (hand pose, gestures, and grasping force) is essential. Yet, existing interfaces often fall short in meeting these demands, compromising either the diversity of multiple intentions decoding or wearability. To address this, we developed a novel Multiple Intentions Decoding Human-Machine Interface (MI-DHMI) that integrates high-throughput surface electromyography (sEMG) sensors with hand-mounted and forearm-mounted inertial measurement units (IMUs). The developed interface is supported by a unified framework for simultaneous multiple intentions decoding. By employing multimodal deep learning and hardware design with a low noise floor, the decoding framework selectively focuses on the sEMG components that are genuinely associated with finger movements. This effectively reduces decoding errors caused by sEMG variability during unconstrained upper-limb motions, thereby significantly enhancing robustness. Even under unconstrained wrist and forearm motion, the interface achieves a gesture recognition accuracy exceeding 97%, grasping force estimation with $R^2 = 0.95$, and hand pose decoding consistent with the actual hand pose, outperforming baseline devices and algorithms. Ablation studies further validate the effectiveness of the proposed decoding framework. Finally, two online experiments were conducted to validate the device, demonstrating its superior performance in high-stability tasks, including a pouring task and object grasping. The developed interface provides a new solution of a fully wearable, multiple intentions decoding system, offering effective support for ubiquitous teleoperation and contributing to the advancement of human-machine interaction research.

cs.RO↗

arXiv2026

Data-Centric Neuromotor Interfaces for Portable Human-Machine Interaction

Dexterous human-machine interaction requires intuitive and expressive interfaces that can be efficiently deployed on constrained edge devices. Flexible material-based neuromotor interfaces hold considerable promise, as they decode human movement intention into natural control. Although emerging flexible electronic skins enable wearable high-fidelity data acquisition, practical deployment inevitably involves trade-offs between computational resources and portability. We present a data-centric paradigm where physiological features yield fundamental separability, providing sufficient discriminative cues for recognition. A wireless, high-bandwidth system developed for collecting various electrophysiological signals, when integrated with muscle-specific electrodes, forms a surface electromyography-based interface. Exploiting highly separable data, a 2,210-parameter model achieves 94.36% accuracy across 34 gestures and can be rapidly deployed on edge devices, establishing a new thousand-parameter benchmark for dexterous decoding. The underlying data-algorithm interactions in the data-centric paradigm are further clarified, demonstrating its feasibility in real-world scenarios. This study provides a principled and validated pathway for practical deployment of reliable neuromotor interfaces.

cs.RO↗

arXiv2026

Do VLMs Share Safety Neurons Across Modalities?

Vision-language models (VLMs) can comply with harmful requests delivered through images, even when their LLM backbones would refuse the same content in text. While prior work characterizes these jailbreaks empirically or at the representation level, how visual inputs perturb safety pathways at the neuron level remains uncharted. We close this gap with a causal, neuron-level analysis of safety mechanisms in 10 VLMs. We propose a two-stage detection pipeline with iterative ablation that accounts for self-repair, and introduce two modality-isolated benchmarks, ViSafe-Detect and ViSafe-Eval, which decouple visual and textual safety signals. Our analysis reveals: (i) Text safety in VLMs is localizable: $\sim$88 neurons ($<$0.01%) whose targeted ablation substantially reduces refusal. (ii) Text safety neurons constitute the dominant refusal pathway: ablating them is the only intervention that consistently and substantially reduces refusal across all models. (iii) Visual safety is high-dimensional and diffuse at the single-neuron level: text safety concentrates in $\sim$5 subspace directions while visual safety requires $\geq$50. This gap holds across architectures, explaining why current alignment has not closed the visual safety gap. Project page is at: https://jiaxuan-li.github.io/vlm-safety-neuron/ Warning: this paper may include examples of harmful content.

cs.LG↗

arXiv2026

ELVES-Dwarf. II. A Systematic Search for Satellite Systems of Dwarf Galaxies in the Local Volume

We present the Exploration of Local VolumE Satellites of Dwarf Galaxies (ELVES-Dwarf) survey, a systematic census of satellite systems around dwarf hosts in the Local Volume. Our final sample comprises 39 predominantly isolated hosts with stellar masses $10^{7} 10^5\,M_\odot$ around the 39 hosts. Above our fiducial completeness threshold of $M_\star\gtrsim10^{5.7}\,M_\odot$ and within the projected virial radius, 21 hosts have no confirmed satellites, 10 have one, six have two, and two have four, revealing substantial host-to-host scatter in satellite abundance. Overall, the observed satellite abundances and stellar mass functions are broadly consistent with predictions from the cosmological simulation TNG50 and galaxy formation models calibrated using Milky Way satellites. The projected radial distribution of the satellites is also consistent with theoretical expectations and with satellite populations around Milky Way-mass hosts. In contrast, the quenched fraction of satellites around dwarf hosts is substantially lower than around Milky Way-mass hosts, suggesting that environmental quenching is less efficient in dwarf halos. ELVES-Dwarf provides the first large, homogeneous, distance-confirmed sample of satellites around dwarf hosts and establishes a foundation for understanding galaxy formation and evolution in less-dense environments.

astro-ph.GA↗

arXiv2026

Multimodal Adaptive Control for Safe Robotic Craniotomy Under Partial Observability

Autonomous robotic craniotomy requires continuous regulation of tool-tissue interactions to mitigate mechanical overload and thermal damage while maintaining surgical efficiency. However, this process is inherently partially observable due to unknown, time-varying tissue properties and the inability to directly measure cutting temperatures under physical occlusion. To address these challenges, we propose RL-MACRO, a cybernetic closed-loop intelligence framework that couples multimodal perception, adaptive decision-making, and robotic execution. This framework empowers the surgical robot to autonomously perceive inaccessible states from partial sensory feedback and dynamically optimize its behaviors under uncertain environment. A CNN-LSTM observer first fuses force and sound feedback to reconstruct the hidden temperature state (R^2=0.939, MAE = 1.717 deg C). This reconstructed temperature, alongside multi-sensor features, forms the belief state for an offline Implicit Q-Learning (IQL) policy. A novel dual-head Actor dynamically coordinates the feed rate, spindle speed, and cutting depth to optimize efficiency within strict safety bounds. These decisions are seamlessly translated into spatial motions via online trajectory re-planning and velocity servoing. Experiments on bovine ribs and six ex vivo goat skulls validate the system's robust perception, adaptive recovery from force/temperature excursions, and smooth execution on irregular surfaces, establishing a data-driven cybernetic paradigm for safe and efficient autonomous bone cutting.

cs.RO↗

arXiv2026

DistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation

Cross-modal alignment of visual and textual representations is fundamental to multimodal medical image understanding, yet remains hindered by uncertainty in both modalities under real-world clinical conditions. Existing vision-language segmentation methods rely on deterministic cross-modal matching, which overlooks aleatoric uncertainty from ambiguous boundaries and epistemic uncertainty from limited training data, leading to fragile performance under domain shift. To address this issue, we propose DistMedVL, a probabilistic vision-language framework that introduces a lightweight Probabilistic Cross-Modal Adapter (PCM-Adapter) upon frozen encoders to explicitly model representational uncertainty. Specifically, the PCM-Adapter comprises two sequential modules for progressive probabilistic alignment. We first devise a Mahalanobis Alignment Module (MAM) that models textual tokens as Gaussian distributions and computes patch-text compatibility via Mahalanobis distance, yielding variance-conditioned matching that downweights unreliable feature dimensions. Moreover, we devise a Distribution Flow Module (DFM) that estimates modality-wise confidence parameters and performs vision-guided refinement of textual distributions, accommodating distributional variation across imaging modalities. Extensive experiments across eight medical segmentation benchmarks demonstrate that DistMedVL outperforms state-of-the-art methods with only 6.3M trainable parameters, exhibiting superior data efficiency, perturbation robustness and cross-dataset generalization.

cs.CV↗

arXiv2026

Agentic Economic Modeling

We introduce Agentic Economic Modeling (AEM), a framework that aligns synthetic LLM choices with small-sample human evidence for econometric inference. AEM first generates task-conditioned synthetic choices via LLMs, then learns a bias-correction mapping from task features and raw LLM choices to human-aligned choices, upon which standard econometric estimators perform inference to recover demand elasticities and treatment effects. We validate AEM in two experiments. In a large scale conjoint study, using only 10% of the original data to fit the correction model lowers the error of the demand-parameter estimates, while uncorrected LLM choices increase the errors. In a regional field experiment, a mixture model calibrated on 10% of geographic regions estimates a treatment effect of -65$\pm$10 bps on the hold-out regions, closely matching the full human experiment (-60$\pm$8 bps). These results demonstrate AEM's potential to improve RCT efficiency and represent a step toward LLM-based counterfactual generation.

econ.EM↗

arXiv2026

Zangetsu: A Candidate of Isolated, Quiescent, and Backsplash Ultra-Diffuse Galaxy in the COSMOS Field

Deep imaging surveys have changed our view of the low surface brightness (LSB) Universe. The ``renaissance'' of the LSB galaxy population, as a prime example of this recent development, continues to challenge our understanding of galaxy formation. Here, we report the serendipitous discovery of Zangetsu, an isolated, quiescent, and distorted ultra-diffuse galaxy (UDG) candidate in the COSMOS field, using images from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Zangetsu exhibits an extremely low central surface brightness ($\mathrm{μ_{0,g}}=26.60\pm0.01$ mag arcsec$^{-2}$), a very shallow inner surface brightness profile ($\mathrm{n}_{\rm Sersic}=0.40\pm0.01$), and a large angular size ($\mathrm{R_e}\approx 10.44$ arcsec). Surprisingly, Zangetsu also has a quiescent stellar population ($\mathrm{g-i}=0.96$), an unusually elongated shape ($\mathrm{b/a}\sim 0.25$), and mild morphological asymmetry, making it a rare case among known UDGs. Surface brightness fluctuation analysis of HSC and Hubble Space Telescope (HST) images only provides a distance lower limit of $D>25.4$ Mpc (thus $\mathrm{R_e}>1.38$ kpc). However, Zangetsu remains an extreme outlier in the luminosity-size relation of known LSB galaxies, suggesting that it could be an exceptionally large and/or diffuse system. Classic internal or external UDG formation mechanisms alone struggle to explain such a system. A backsplash origin may account for its isolation and quiescent nature. This finding also raises the possibility that current works may overlook similarly extreme, elongated systems that could further our understanding of the LSB Universe.

astro-ph.GA↗

arXiv2026

An Integrated Hardware-Software Design for Low-Data Spatial Defect Detection in Robotic Visual Inspection with Hybrid Optoelectronic Neural Networks

To address data overload and inefficient shape-level annotation in robotic visual inspection, this paper proposes a hardware-software integrated optoelectronic architecture. A non-imaging, low-data paradigm is established to minimize annotation dependency. First, a sensor-in-the-loop strategy reconfigures a Digital Micromirror Device (DMD) as a physical optical convolutional layer, enabling photonic-domain feature extraction that unifies sensing hardware and processing software. To suppress data volume at the source, a block-based compressed sensing strategy encodes spatial information into low-dimensional temporal signals, drastically reducing redundancy. Subsequently, to bypass laborious manual defect shape annotation, natural language descriptions guide the network to align with highly generalizable features from Contrastive Language-Image Pre-training (CLIP), steering the attention maps of the optoelectronic neural network toward defect shapes. Furthermore, a Localization Accuracy for Attention (LAA) metric is proposed to quantify shape-level defect localization performance. Experiments on transparent material defect detection validate the system's effectiveness. Parametric analysis reveals how measurement matrices, compression ratios, and block sizes affect accuracy. Results show that, compared to traditional imaging, the proposed architecture maintains equivalent accuracy while reducing data volume by 90% for Vision Transformers and computational workload by 60% for Convolutional Neural Networks. This low-data paradigm offers an efficient solution for industrial automation scenarios involving massive data streams, high acquisition costs, or constrained edge resources.

cs.RO↗