arXiv subjects

Zifeng Yuan

Publications and source records attributed to Zifeng Yuan.

11 recordsLinked to original sources

arXiv2026

Metasurface-integrated VCSEL designed for polarization control in optical Ising machines

The orthogonal polarization states of vertical-cavity surface-emitting lasers (VCSELs) can be used to describe candidate solutions to the Ising Hamiltonian, which is useful for solving quadratic unconstrained binary optimization problems. However, the natural anisotropy of VCSELs tends to overly favor one polarization state, which impedes the system from working as desired. In this work, we have designed and fabricated a metasurface, which may lead to a VCSEL with reduced undesired anisotropy. By changing the geometric size of nano-structures in the metasurface, the polarization state of the output light can be altered. Based on the injection-locking theory and spin-flip model, we numerically show that VCSELs with lowered anisotropy are more easily affected by the injection locking needed in Ising systems. Additionally, we numerically study the evolution of a 3-bit VCSEL-based Ising system and verify that the computational accuracy of the photonic Ising machine can be improved to more than twice that of its counterpart with higher anisotropy.

physics.optics↗

arXiv2026

Mesa Orientation Engineering for Polarization Locking in VCSELs

While polarization stability is common in many optical systems, intentional polarization bi-stability is also important for polarization-encoded computing. We propose a mesa orientation strategy to engineer the polarization switching of vertical-cavity surface-emitting lasers (VCSELs). Further experiments demonstrate improved polarization locking performance.

physics.optics↗

arXiv2026

Engineering Polarization Switching in VCSELs with Custom Aperture Shapes

We experimentally fabricate and measure vertical-cavity surface-emitting lasers (VCSELs) with custom aperture shapes, demonstrating predictable polarization control and switching, supporting their use in photonic computation schemes like Ising machines with polarization-based information encoding.

physics.optics↗

arXiv2026

Enhanced Polarization Locking in VCSELs

While optical injection locking (OIL) of vertical-cavity surface-emitting lasers (VCSELs) has been widely studied in the past, the polarization dynamics of OIL have received far less attention. Recent studies suggest that polarization locking via OIL could enable novel computational applications such as polarization-encoded Ising computers. However, the inherent polarization preference and limited polarization switchability of VCSELs hinder their use for such purposes. To address these challenges, we fabricate VCSELs with tailored oxide aperture designs and combine these with bias current tuning to study the overall impact on polarization locking. Experimental results demonstrate that this approach reduces the required injection power (to as low as 3.6 μW) and expands the locking range. To investigate the impact of the approach, the spin-flip model (SFM) is used to analyze the effects of amplitude anisotropy and bias current on polarization locking, demonstrating strong coherence with experimental results.

physics.optics↗

arXiv2026

Auto-Discovery-Bench: Diagnosing Structured State Tracking in Oracle-Guided Discovery

Interactive discovery requires agents to maintain and update structured beliefs over many rounds of feedback. Before evaluating agents in noisy, open-ended scientific environments, it is useful to isolate this prerequisite capability under controlled conditions. We introduce Auto-Discovery-Bench, a deterministic oracle-guided diagnostic benchmark in which agents recover hidden structures through repeated hypothesis--intervention--feedback cycles. The benchmark instantiates three controlled discovery abstractions: directed graph discovery, undirected relational discovery, and symbolic equation discovery. Across models, performance degrades as the number of variables, trajectory length, and distractors increase. A separate trajectory-tracking diagnostic shows that many failures persist even when intervention selection and hypothesis generation are removed, suggesting that limitations in maintaining and integrating long-range structured information are an important bottleneck for oracle-guided discovery. Auto-Discovery-Bench is not intended to replace realistic discovery environments; rather, it provides a reproducible, low-confound diagnostic testbed for isolating a prerequisite capability for interactive scientific agents.

cs.LG↗

arXiv2026

HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Bounds

Many scientific problems are underdetermined: multiple distinct hypotheses are equally consistent with the same observations. In such settings, effective inference requires not only producing valid explanations, but also systematically exploring and covering the admissible hypothesis set. We introduce HypoSpace, a benchmark that treats large language models (LLMs) as samplers over finite hypothesis spaces and evaluates them on three metrics: Validity, Uniqueness, and Recovery. HypoSpace spans three structured domains (causal graph inference, gravity-constrained 3D voxel reconstruction, and Boolean genetic interaction modeling) with deterministic validators and exactly enumerable solution spaces, plus real-world anchored case studies. Empirically, HypoSpace reveals a capability- and scale-dependent coverage failure: models can maintain high Validity while exhibiting reduced Uniqueness and Recovery as admissible hypothesis spaces become larger or more combinatorial. We further show that the analysis on stratified decoding partially mitigates this collapse, demonstrating HypoSpace's utility as a diagnostic benchmark for set-valued inference. Code is available at: https://github.com/CTT-Pavilion/_HypoSpace.

cs.CL↗

arXiv2026

WeatherReasonSeg: A Benchmark for Weather-Aware Reasoning Segmentation in Visual Language Models

Existing vision-language models (VLMs) have demonstrated impressive performance in reasoning-based segmentation. However, current benchmarks are primarily constructed from high-quality images captured under idealized conditions. This raises a critical question: when visual cues are severely degraded by adverse weather conditions such as rain, snow, or fog, can VLMs sustain reliable reasoning segmentation capabilities? In response to this challenge, we introduce WeatherReasonSeg, a benchmark designed to evaluate VLM performance in reasoning-based segmentation under adverse weather conditions. It consists of two complementary components. First, we construct a controllable reasoning dataset by applying synthetic weather with varying severity levels to existing segmentation datasets, enabling fine-grained robustness analysis. Second, to capture real-world complexity, we curate a real-world adverse-weather reasoning segmentation dataset with semantically consistent queries generated via mask-guided LLM prompting. We further broaden the evaluation scope across five reasoning dimensions, including functionality, application scenarios, structural attributes, interactions, and requirement matching. Extensive experiments across diverse VLMs reveal two key findings: (1) VLM performance degrades monotonically with increasing weather severity, and (2) different weather types induce distinct vulnerability patterns. We hope WeatherReasonSeg will serve as a foundation for advancing robust, weather-aware reasoning.

cs.CV↗

arXiv2026

Kinetics of Stacking Order Evolution During Heterogeneous Ice Formation

The selection of stacking order in a broad range of close-packed polymorphic materials remains a challenging enigma. Using in situ cryogenic transmission electron microscopy, we uncover the atomistic mechanisms governing the vapour deposition growth of ice. We find that the heterogeneous ice nucleation and growth undergoes recrystallization accompanied by bifurcation, reflecting a coherent epitaxial transition from a cubic-ice embryonic core to hexagonal-ice prismatic dendrites, with intermediate stacking-disordered layers serving as a dynamic fluctuating bridge. Supported by molecular dynamics simulations, these phenomena are attributed to a surface-constrained, symmetry-breaking crystallization preference aligned with the principle of minimizing free energy. Our results highlight the critical role of the combined effects of surface and symmetry in shaping ice crystallization, providing fresh insights into crystal growth mechanisms and guiding principles for the design of advanced materials.

cond-mat.mtrl-sci↗

arXiv2025

RaindropGS: A Benchmark for 3D Gaussian Splatting under Raindrop Conditions

3D Gaussian Splatting (3DGS) under raindrop conditions suffers from severe occlusions and optical distortions caused by raindrop contamination on the camera lens, substantially degrading reconstruction quality. Existing benchmarks typically evaluate 3DGS using synthetic raindrop images with known camera poses (constrained images), assuming ideal conditions. However, in real-world scenarios, raindrops often interfere with accurate camera pose estimation and point cloud initialization. Moreover, a significant domain gap between synthetic and real raindrops further impairs generalization. To tackle these issues, we introduce RaindropGS, a comprehensive benchmark designed to evaluate the full 3DGS pipeline-from unconstrained, raindrop-corrupted images to clear 3DGS reconstructions. Specifically, the whole benchmark pipeline consists of three parts: data preparation, data processing, and raindrop-aware 3DGS evaluation, including types of raindrop interference, camera pose estimation and point cloud initialization, single image rain removal comparison, and 3D Gaussian training comparison. First, we collect a real-world raindrop reconstruction dataset, in which each scene contains three aligned image sets: raindrop-focused, background-focused, and rain-free ground truth, enabling a comprehensive evaluation of reconstruction quality under different focus conditions. Through comprehensive experiments and analyses, we reveal critical insights into the performance limitations of existing 3DGS methods on unconstrained raindrop images and the varying impact of different pipeline components: the impact of camera focus position on 3DGS reconstruction performance, and the interference caused by inaccurate pose and point cloud initialization on reconstruction. These insights establish clear directions for developing more robust 3DGS methods under raindrop conditions.

cs.CV↗

arXiv2025

RGB-to-Polarization Estimation: A New Task and Benchmark Study

Polarization images provide rich physical information that is fundamentally absent from standard RGB images, benefiting a wide range of computer vision applications such as reflection separation and material classification. However, the acquisition of polarization images typically requires additional optical components, which increases both the cost and the complexity of the applications. To bridge this gap, we introduce a new task: RGB-to-polarization image estimation, which aims to infer polarization information directly from RGB images. In this work, we establish the first comprehensive benchmark for this task by leveraging existing polarization datasets and evaluating a diverse set of state-of-the-art deep learning models, including both restoration-oriented and generative architectures. Through extensive quantitative and qualitative analysis, our benchmark not only establishes the current performance ceiling of RGB-to-polarization estimation, but also systematically reveals the respective strengths and limitations of different model families -- such as direct reconstruction versus generative synthesis, and task-specific training versus large-scale pre-training. In addition, we provide some potential directions for future research on polarization estimation. This benchmark is intended to serve as a foundational resource to facilitate the design and evaluation of future methods for polarization estimation from standard RGB inputs.

cs.CV↗

arXiv2021

Towards Plug-and-Play Visual Graph Query Interfaces: Data-driven Canned Pattern Selection for Large Networks

Canned patterns (i.e. small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIs for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel generic and extensible framework called TATTOO that takes a data-driven approach to automatically selecting canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity, and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.

cs.DB↗