arXiv subjects

Zhuoyang Li

Publications and source records attributed to Zhuoyang Li.

At least 19 recordsLinked to original sources

arXiv2026

D3-Gym: Constructing Real-World Verifiable Environments for Data-Driven Discovery

Despite recent progress in language models and agents for scientific data-driven discovery, advancing their capabilities is held back by the absence of verifiable environments representing real-world scientific tasks. To fill this gap, we introduce D3-Gym, the first automatically constructed dataset with verifiable environments for scientific Data-Driven Discovery. D3-Gym comprises 565 tasks from 239 real scientific repositories across four disciplines, each with a natural language instruction, an executable environment with pre-installed dependencies, dataset previews, a reference solution, and an automatically synthesized evaluation script. Our evaluation scripts achieve 87.5% agreement with human-annotated gold standards and strong alignment in domain-specific evaluation logic. Training on trajectories sampled from D3-Gym yields consistent gains across Qwen3 models on ScienceAgentBench, boosting Qwen3-32B by 7.8 absolute points and shrinking the gap with strong proprietary models. We further illustrate, through case studies, how D3-Gym environments can serve as a testbed for studying agentic optimization loops such as Autoresearch on real scientific workflows. We open-source D3-Gym, its creation workflow, sampled trajectories, and training scripts at https://github.com/OSU-NLP-Group/D3-Gym.

cs.AI↗

arXiv2026

Hermes - Towards an Optimal High-Performance Algorithm for Cosmic Statistics of Large Data Sets

We present Hermes, an in situ multiresolution framework for efficient and flexible measurements of cosmic large-scale-structure statistics from discrete catalogues. Hermes reconstructs a catalogue as a continuous density field in a compact scaling-function basis and replaces explicit counting of particle tuples with algebraic operations among window-filtered fields. Standard binning schemes for counts-in-cells, two-point and higher-order correlation functions are thereby expressed through choices of window functions, while new statistics can be constructed by modifying the kernels without redesigning the estimator. We introduce PyHermes, an open-source Python implementation combining multiresolution reconstruction, FFT-based convolution, MPI/thread parallelism, and GPU acceleration. It supports isotropic and anisotropic two-point statistics, marked correlations, standard and multipole three-point functions, filtered statistics, and differential operators for derived physical fields. Tests with cosmological N-body halo catalogues demonstrate a range of clustering measurements and quantify the computational efficiency and scalability of the approach. By separating field representation from statistical windows, a single reconstructed field can be reused for many standard and customised measurements, making Hermes well suited to large data sets from current and future galaxy surveys.

astro-ph.CO↗

arXiv2026

MUltiplexed Survey Telescope (MUST) Science White Paper I: Overview of Large-Scale Structure Cosmology in the Era of Stage-V Spectroscopic Surveys

The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg$^2$ field of view. MUST aims to conduct the first Stage-V spectroscopic survey in the 2030s, mapping the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to a redshift of z ~ 5.5, corresponding to approximately 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, ranging from local bright galaxies and luminous red galaxies to emission-line galaxies, and high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, tests of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe.

astro-ph.CO↗

arXiv2026

Reconfigurable Momentum-space vectorial lasing enabled by Quasi-BIC

Bound states in the continuum (BICs) have enabled lasers with rich momentum-space textures. However, the output patterns of quasi-BIC lasers remain largely static and confined to a few geometries. Here, a reconfigurable momentum-space vectorial laser was proposed based on two-dimensional photonic crystal. By selectively exciting quasi-BIC modes, we identify the geometric asymmetry factors favoring single BIC, dual-BIC, and radiative mode with BIC operation. This approach yields vectorial lasing with characteristic patterns lasing in momentum space of bidirectional double lobes (BDL), radially polarized ring with BDL, azimuthally polarized ring with BDL, and linearly polarized spot with BDL. Importantly, reversible switching between a single donut and a donut with BDL was achieved in the same device by varying the pump energy density. Our work establishes a compact, versatile platform for reconfigurable vectorial lasers, with potential applications in tunable optical tweezers, super-resolution imaging, and on-chip optical interconnects.

physics.optics↗

arXiv2026

Reconfigurable and Recyclable Low-Threshold Quasi-BIC Lasers via a Tunable polymer Coating

Reconfigurable and sustainable microcavity lasers are highly desirable for next-generation integrated photonics. Here, we report a recyclable, low-threshold quasi-bound state in the continuum (q-BIC) laser fabricated via low-cost, high-throughput interference lithography. By introducing a polyvinyl alcohol (PVA) coating on a dye-doped photonic crystal, we suppress out-of-plane symmetry breaking, which reinforces optical confinement and reduces the lasing threshold. The q-BIC modes are further tuned through tailoring the refractive-index of the PVA layer by using Kramers-Kronig relation via Rhodamine 6G doping, demonstrating a wavelength shift of 7.14 nm and a sensitivity of 215 nm RIU as a sensing prob. More importantly, lasing modes are reversibly tuning via precisely controlling the coating thickness. Exploiting the dissolving and re-coating process, the laser is repeatedly reconfigured while maintaining performance. This work provides a sustainable and adaptive platform for sensing and reconfigurable photonic systems.

physics.optics↗

arXiv2026

Privacy Control in Conversational LLM Platforms: A Walkthrough Study

Large language models (LLMs) are increasingly integrated into daily life through conversational interfaces, processing user data via natural language inputs and exhibiting advanced reasoning capabilities, which raises new concerns about user control over privacy. While much research has focused on potential privacy risks, less attention has been paid to the data control mechanisms these platforms provide. This study examines six conversational LLM platforms, analyzing how they define and implement features for users to access, edit, delete, and share data. Our analysis reveals an emerging paradigm of data control in conversational LLM platforms, where user data is generated and derived through interaction itself, natural language enables flexible yet often ambiguous control, and multi-user interactions with shared data raise questions of co-ownership and governance. Based on these findings, we offer practical insights for platform developers, policymakers, and researchers to design more effective and usable privacy controls in LLM-powered conversational interactions.

cs.HC↗

arXiv2026

Spatiotemporal Topological Phase Transition in non-Hermitian Photonic System

While energy band topology in spatial photonic crystals (PCs) and momentum-band topology in temporal crystals have each served as powerful probes of topological phases in their respective domains, their unification in a static platform remains unexplored. In this Letter, we bridge this gap by introducing a waveguide assisted non-Hermitian SSH model, in which controlled tuning of loss and coupling drives PT-symmetry breaking and enables a continuous transition between energy- and momentum-gap regimes. This allows us to construct a complete spatiotemporal topological phase diagram in a unified parameter space. By mapping this phase diagram onto a spatially graded PC, we experimentally observe multiple Bloch momentum-band gaps and a continuous spatiotemporal topological transition via translating across the static sample, enabling real-time control over the evolution pathway of the band topology. Our work creates a versatile, bias-free platform for exploring synthetic spacetime physics and opens new avenues for controlling light via non-Hermitian band engineering.

physics.optics↗

arXiv2026

Synesthesia of Vehicles: Tactile Data Synthesis from Visual Inputs

Autonomous vehicles (AVs) rely on multi-modal fusion for safety, but current visual and optical sensors fail to detect road-induced excitations which are critical for vehicles' dynamic control. Inspired by human synesthesia, we propose the Synesthesia of Vehicles (SoV), a novel framework to predict tactile excitations from visual inputs for autonomous vehicles. We develop a cross-modal spatiotemporal alignment method to address temporal and spatial disparities. Furthermore, a visual-tactile synesthetic (VTSyn) generative model using latent diffusion is proposed for unsupervised high-quality tactile data synthesis. A real-vehicle perception system collected a multi-modal dataset across diverse road and lighting conditions. Extensive experiments show that VTSyn outperforms existing models in temporal, frequency, and classification performance, enhancing AV safety through proactive tactile perception.

cs.AI↗

arXiv2026

Dual Flat-Bands of Bound State in the Continuum and Radiative Mode via TE-TM Coupling

A general symmetry-controlled mechanism is proposed for realizing dual flat-bands of bound state in the continuum (BIC) and its radiative counterpart in photonic crystal slabs. By breaking the vertical mirror symmetry of slab, inter-polarization coupling between TE-like and TM-like modes is activated, while intra-polarization coupling among modes within the same polarization class is simultaneously preserved. The cooperative action of these two coupling channels gives rise to the concurrent flattening of both the BIC-hosting band and the radiative band, resulting in a dual flat-band system with strongly contrasting quality (Q) factors. An effective two-step coupling model is constructed to capture the essential physics and show that the emergence of the flat bands is governed by geometric tuning rather than accidental degeneracies. The mechanism is shown to be generic with respect to polarization and material platform, enabling dual flat-band states in both low- and high-index systems, with substantially enhanced angular bandwidths in the latter. These finding establish a unified route for flat-band photonic engineering and provide a robust platform for angle-tolerant resonant photonic functionalities.

physics.optics↗

arXiv2025

Design Exploration of AI-assisted Personal Affective Physicalization

Personal Affective Physicalization is the process by which individuals express emotions through tangible forms to record, reflect on, and communicate. Yet such physical data representations can be challenging to design due to the abstract nature of emotions. Given the shown potential of AI in detecting emotion and assisting design, we explore opportunities in AI-assisted design of personal affective physicalization using a Research-through-Design method. We developed PhEmotion, a tool for embedding LLM-extracted emotion values from human-AI conversations into parametric design of physical artifacts. A lab study was conducted with 14 participants creating these artifacts based on their personal emotions, with and without AI support. We observed nuances and variations in participants' creative strategies, meaning-making processes and their perceptions of AI support in this context. We found key tensions in AI-human co-creation that provide a nuanced agenda for future research in AI-assisted personal affective physicalization.

cs.HC↗

arXiv2025

A Multi-modal Fusion Network for Terrain Perception Based on Illumination Aware

Road terrains play a crucial role in ensuring the driving safety of autonomous vehicles (AVs). However, existing sensors of AVs, including cameras and Lidars, are susceptible to variations in lighting and weather conditions, making it challenging to achieve real-time perception of road conditions. In this paper, we propose an illumination-aware multi-modal fusion network (IMF), which leverages both exteroceptive and proprioceptive perception and optimizes the fusion process based on illumination features. We introduce an illumination-perception sub-network to accurately estimate illumination features. Moreover, we design a multi-modal fusion network which is able to dynamically adjust weights of different modalities according to illumination features. We enhance the optimization process by pre-training of the illumination-perception sub-network and incorporating illumination loss as one of the training constraints. Extensive experiments demonstrate that the IMF shows a superior performance compared to state-of-the-art methods. The comparison results with single modality perception methods highlight the comprehensive advantages of multi-modal fusion in accurately perceiving road terrains under varying lighting conditions. Our dataset is available at: https://github.com/lindawang2016/IMF.

cs.AI↗

arXiv2025

MoDE: Mixture of Diffusion Experts for Any Occluded Face Recognition

With the continuous impact of epidemics, people have become accustomed to wearing masks. However, most current occluded face recognition (OFR) algorithms lack prior knowledge of occlusions, resulting in poor performance when dealing with occluded faces of varying types and severity in reality. Recognizing occluded faces is still a significant challenge, which greatly affects the convenience of people's daily lives. In this paper, we propose an identity-gated mixture of diffusion experts (MoDE) for OFR. Each diffusion-based generative expert estimates one possible complete image for occluded faces. Considering the random sampling process of the diffusion model, which introduces inevitable differences and variations between the inpainted faces and the real ones. To ensemble effective information from multi-reconstructed faces, we introduce an identity-gating network to evaluate the contribution of each reconstructed face to the identity and adaptively integrate the predictions in the decision space. Moreover, our MoDE is a plug-and-play module for most existing face recognition models. Extensive experiments on three public face datasets and two datasets in the wild validate our advanced performance for various occlusions in comparison with the competing methods.

cs.CV↗

arXiv2025

Customizing Emotional Support: How Do Individuals Construct and Interact With LLM-Powered Chatbots

Personalized support is essential to fulfill individuals' emotional needs and sustain their mental well-being. Large language models (LLMs), with great customization flexibility, hold promises to enable individuals to create their own emotional support agents. In this work, we developed ChatLab, where users could construct LLM-powered chatbots with additional interaction features including voices and avatars. Using a Research through Design approach, we conducted a week-long field study followed by interviews and design activities (N = 22), which uncovered how participants created diverse chatbot personas for emotional reliance, confronting stressors, connecting to intellectual discourse, reflecting mirrored selves, etc. We found that participants actively enriched the personas they constructed, shaping the dynamics between themselves and the chatbot to foster open and honest conversations. They also suggested other customizable features, such as integrating online activities and adjustable memory settings. Based on these findings, we discuss opportunities for enhancing personalized emotional support through emerging AI technologies.

cs.HC↗

arXiv2025

Key drivers of the preference for dynamic dark energy

Joint analysis of the baryon acoustic oscillations (BAO) measurement by the Dark Energy Spectroscopic Instrument (DESI) first data release, Type Ia supernovae (SNe) of the Dark Energy Survey Year 5 (DES5YR) release and cosmic microwave background (CMB) data favors a quintom-like dynamic dark energy model over the standard Lambda cold dark matter ($Λ$CDM) model at $3.9σ$ level (Adame et al. 2024). We confirm the previous finding in the literature that the preference for dynamic dark energy does not rely on the detailed modeling of CMB physics and remains at a similar significance level ($3.2σ$) when the full CMB likelihood is replaced by a CMB acoustic-oscillation angle ($θ_\star$) prior and a baryon abundance ($Ω_bh^2$) prior. The computationally efficient $θ_\star$ and $Ω_bh^2$ priors allow us to take a frequentist approach by comparing DES5YR SNe and DESI BAO with a large number ($\gtrsim 10^4$) of Planck-constrained $Λ$CDM simulations. We find that $\geq 3.2σ$ preference for dynamic dark energy is very rare (occurrence rate = $0.28\%$) in simulations. When we combine DESI BAO with SN simulations or combine DES5YR SNe with BAO simulations, the occurrence rate of $\geq 3.2σ$ preference for dynamic dark energy increases to $1.2\%$ and $4.8\%$, respectively. These results indicate an internal inconsistency, i.e., a significant tension between DESI BAO + DES5YR SNe and Planck-constrained $Λ$CDM models in both Bayesian and frequentist points of view. Although both DESI BAO and DES5YR SNe contribute to the preference for dynamic dark energy, the contribution from DES5YR SNe is more significant. In the frequentist point of view, even DES5YR SNe alone is in tension with Planck-constrained $Λ$CDM models, though in Bayesian point of view this tension is prior dependent and inconclusive.

astro-ph.CO↗

arXiv2025

Rambler in the Wild: A Diary Study of LLM-Assisted Writing With Speech

Speech-to-text technologies have been shown to improve text input efficiency and potentially lower the barriers to writing. Recent LLM-assisted dictation tools aim to support writing with speech by bridging the gaps between speaking and traditional writing. This case study reports on the real-world writing experiences of twelve academic or creative writers using one such tool, Rambler, to write various pieces such as blog posts, diaries, screenplays, notes, or fictional stories, etc. Through a ten-day diary study, we identified the participants' in-context writing strategies using Rambler, such as how they expanded from an outline or organized their loose thoughts for different writing goals. The interviews uncovered the psychological and productivity affordances of writing with speech, pointing to future directions of designing for this writing modality and the utilization of AI support.

cs.HC↗

arXiv2024

Pair Counting without Binning -- A New Approach to Correlation Functions in Clustering Statistics

This paper presents a novel perspective on correlation functions in the clustering analysis of the large-scale structure of the universe. We first recognise that pair counting in bins of radial separation is equivalent to evaluating counts-in-cells (CIC), which can be modelled using a filtered density field with a binning-window function. This insight leads to an in situ expression for the two-point correlation function (2PCF). Essentially, the core idea underlying our method is to introduce a window function to define the binning scheme, enabling pair-counting without binning. This approach develops a concept of generalised 2PCF, which extends beyond conventional discrete pair counting by accommodating non-sharp-edged window functions. To extend this framework to N-point correlation functions (NPCF) using current optimal edge-corrected estimators, we developed a binning scheme independent of the specific parameterisation of polyhedral configurations. In particular, we demonstrate a fast algorithm for the three-point correlation function (3PCF), where triplet counting is accomplished by assigning either a spherical tophat or a Gaussian filter to each vertex of triangles. Additionally, we derive analytical expressions for the 3PCF using a multipole expansion in Legendre polynomials, accounting for filtered field (binning) corrections. Numerical tests using several suites of N-body simulation samples show that our approach aligns remarkably well with the theoretical predictions. Our method provides an exact solution for quantifying binning effects in practical measurements and offers a high-speed algorithm, enabling high-order clustering analysis in extremely large datasets from ongoing and upcoming surveys such as Euclid, LSST, and DESI.

astro-ph.CO↗

arXiv2024

Curvature perturbations from kinetic preheating after $α$-attractor inflation

Preheating at the end of inflation is a violent nonlinear process that efficiently transfers the energy of the inflaton to a second field, the preheat field. When the preheat field is light during inflation and its background value modulates the preheating process, the superhorizon isocurvature perturbations of the preheat field may be converted to curvature perturbations that leave an imprint on the cosmic microwave background and the large-scale structure of the universe. We use high-precision lattice simulations to study kinetic preheating after $α$-attractor inflation, a case where the effective mass of the preheat field is naturally suppressed during inflation. By comparing the expansion e-folds between different Hubble patches, we find that the conversion from isocurvature perturbations to curvature perturbations is very inefficient and can hardly be detected by cosmological observations.

astro-ph.CO↗

arXiv2024

UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models

OwnThink stands as the most extensive Chinese open-domain knowledge graph introduced in recent times. Despite prior attempts in question answering over OwnThink (OQA), existing studies have faced limitations in model representation capabilities, posing challenges in further enhancing overall accuracy in question answering. In this paper, we introduce UniOQA, a unified framework that integrates two complementary parallel workflows. Unlike conventional approaches, UniOQA harnesses large language models (LLMs) for precise question answering and incorporates a direct-answer-prediction process as a cost-effective complement. Initially, to bolster representation capacity, we fine-tune an LLM to translate questions into the Cypher query language (CQL), tackling issues associated with restricted semantic understanding and hallucinations. Subsequently, we introduce the Entity and Relation Replacement algorithm to ensure the executability of the generated CQL. Concurrently, to augment overall accuracy in question answering, we further adapt the Retrieval-Augmented Generation (RAG) process to the knowledge graph. Ultimately, we optimize answer accuracy through a dynamic decision algorithm. Experimental findings illustrate that UniOQA notably advances SpCQL Logical Accuracy to 21.2% and Execution Accuracy to 54.9%, achieving the new state-of-the-art results on this benchmark. Through ablation experiments, we delve into the superior representation capacity of UniOQA and quantify its performance breakthrough.

cs.CL↗