arXiv subjects

Yuan Chen

Publications and source records attributed to Yuan Chen.

At least 19 recordsLinked to original sources

arXiv2026

Reference-Beam-Assisted Perturbation-Resistant Imaging Through A Single Multimode Fiber

Multimode fiber (MMF) imaging is highly sensitive to mechanical perturbations, which alter modal coupling and disrupt the speckle-to-target mapping. Here, we propose a reference-beam-assisted method for image reconstruction across multiple fiber configurations. An independent reference beam propagates through the same MMF as the imaging signal without target modulation, providing target-independent transmission-state information through its speckle patterns without requiring a separate configuration-sensing structure. A neural network jointly processes the signal and reference speckles to reconstruct target images. Experiments with MNIST and Fashion-MNIST across 10 fiber configurations demonstrate improved reconstruction quality, faster training convergence, and more accurate configuration identification. Further evaluation across 40 configurations confirms the reconstruction benefit of reference-beam assistance over a larger set of perturbed fiber states. These results provide a foundation for developing perturbation-adaptive MMF imaging systems for flexible endoscopy and minimally invasive optical diagnostics.

physics.optics↗

arXiv2026

Scattering-force-driven light-sheet fluorescence microscopy for scan-free three-dimensional imaging

Optical manipulation offers a powerful non-contact approach to precise positioning, directional transport and three-dimensional (3D) imaging of microscale objects. However, positional fluctuations during optical confinement introduce spatial sampling errors that compromise imaging quality and acquisition speed. Here, we demonstrate a scattering-force-driven optical manipulation imaging method that replaces the stepwise scanning of conventional optical trapping with continuous directional transport of microparticles. An axial scattering force generated by a low-numerical-aperture focused beam propels microparticles through a light sheet, while their positions and fluorescence signals are simultaneously recorded to establish direct spatial correspondence between particle motion and tomographic images. Experiments demonstrate that this approach reduces the effects of positional fluctuations on 3D imaging, achieving an axial sampling interval of 0.1 μm and an imaging rate of 250 Hz. The method also enables effective imaging of samples with diverse structural features while reducing optical power requirements by 70%. By combining robust spatial sampling, high-speed acquisition and reduced photodamage, this approach offers a promising route to rapid 3D imaging, dynamic observation and microscale spatial analysis of microparticles and biological specimens.

physics.optics↗

arXiv2026

JOYS: Launch and destruction of dust in protostellar jets. The case of BHR71-IRS1 with JWST MIRI

Protostellar winds can theoretically lift solids from the planet-forming disks, but direct evidence for launched dust has been scarce so far. Numerous atomic lines that are unique to mid-infrared (IR) wavelengths reveal refractories eroded from dust grains and provide information on wind properties in the earliest stages of the star formation process. We present JWST MIRI-MRS spectral imaging of the inner 2000 au of the BHR71-IRS1 blueshifted side of the outflow. Atomic line intensities are compared to shock models to constrain the physical conditions and elemental abundances of the outflowing gas. Dust continuum maps were constructed from PSF-subtracted cubes, and the spectral energy distribution of the dust was analyzed. The ionized central jet of BHR71-IRS1 is spatially resolved and imaged for the first time, revealing a unique inventory of refractory, volatile, and noble-gas fine-structure lines (Fe, Ni, Co, Cl, S, Ne, Ar). The emission is concentrated along four bright knots that wiggle along the jet axis. PSF-subtracted continuum maps reveal extended mid-IR continuum emission co-spatial with the jet bullets and within the H$_2$-traced outflow cone. Spectral energy distributions along the jet are fit together with the extinction, revealing a warm (200-400 K) and a cold (70-90 K) dust component. The shock modeling constrained by the mid-IR lines indicates a decline in the shock velocity from 70 to 35 km s$^{-1}$ and in the pre-shock density from $>$10$^5$ to $ 4\times 10^4$ cm$^{-3}$ with distance from the protostar. Gas-phase Fe and Ni are measurably depleted relative to Solar abundances, consistent with a substantial fraction of refractories remaining locked in grains in spite of the shocks. These JWST observations provide direct evidence that dust is launched in a Class 0 jet and at least partly survives shock processing.

astro-ph.SR↗

arXiv2026

Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery

Modern music streaming platforms face a persistent tradeoff: exploiting familiar content versus driving the exploration of novel items. While users frequently desire discovery, they hesitate to select unknown artists over proven favorites. Providing transparent, natural language rationales that explain why an unexplored item is recommended lowers this barrier. However, while Large Language Models (LLMs) excel at this nuanced explainability, their real-time deployment is severely bottlenecked by prohibitive inference costs and computational overhead. In this paper, we present an industry case study of a decoupled recommendation architecture that successfully scales exploration without compromising latency. Our system isolates LLM inference asynchronously offline, pre-computing personalized candidate pools of undiscovered artists alongside tailored rationales. Large-scale online A/B experiments validate our design. We demonstrate that combining LLM-backed recommendations with these explanatory rationales significantly reduces the trust barrier for new content, yielding statistically significant improvements in both user exploration and overall engagement on the discovery surfaces.

cs.AI↗

arXiv2026

Filter-Free Indistinguishable Photon Generation from Continuous-Wave-Driven Integrated Microresonators

Quantum networks require scalable photon sources combining narrow linewidth, high efficiency, and high indistinguishability. Microresonator photon-pair sources are promising candidates, yet a source-only description based on the joint spectral amplitude (JSA) predicts near-zero Hong-Ou-Mandel (HOM) interference visibility between photons generated by independent, CW-driven microresonators. In this work, we show that the observed HOM interference is not determined by the JSA alone. By combining finite-time detection with cavity-enhanced spontaneous four-wave mixing, we characterize a detector-conditioned heralded state governed by the idler-photon detection window. We further demonstrate that independently optimizing the idler and signal detection windows allows both heralded-photon indistinguishability and intrinsic heralding efficiency to approach unity, without spectral filtering or complex source engineering. Utilizing integrated high-$Q$ silicon nitride microresonators, we achieve HOM visibilities of 0.992(8) and 0.942(12), without background subtraction, at fourfold count rates of 4.5(3) and 12.2(6) Hz, respectively. Our work establishes CW-driven high-$Q$ microresonators as a robust and scalable platform for quantum-network primitives.

quant-ph↗

arXiv2026

Data-driven Effective Modeling of Stochastic Chemical Reaction Networks

The Stochastic Simulation Algorithm (SSA), widely considered an exact algorithm for stochastic chemical reaction networks, suffers from high computational cost. In this work, we propose a data-driven effective model that operates on a user-defined coarse time step independent of the underlying microscopic reaction-event scale. This is accomplished by directly approximating the finite-time transition kernel of the continuous-time Markov chain induced by SSA, using a generative machine learning model trained on short bursts of SSA simulation data. The trained model constructs a stochastic propagator that recursively generates statistically consistent trajectories at the constant coarse time step, with significantly reduced computational cost. In this paper, we employ conditional normalizing flow as the stochastic propagator. A comprehensive set of numerical examples is presented to demonstrate the accuracy and efficiency of the proposed method.

math.NA↗

arXiv2026

Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation

Recent advances in large language models (LLMs) have enabled their use as conversational recommender systems (CRS), demonstrating strong recommendation accuracy and natural dialogue. However, guiding multi-turn interactions to elicit user preferences effectively remains challenging. Existing approaches either use separate reinforcement learning agents with templated interactions or optimize for interactivity judged by another LLM, without measuring how much useful information is actually gained. We propose a new approach that quantifies the effectiveness of each interaction by the reduction in the assistant's uncertainty, measured via entropy over recommendations. We apply this entropy reduction as a reward---without relying on ground-truth recommendations, which are often unavailable in real-world scenarios---to fine-tune the LLM, enabling strategic interaction generation. Empirical results with supervised fine-tuning (SFT) and direct preference optimization (DPO) on the INSPIRED and ReDial datasets show that our method improves both recommendation quality and conversational efficiency.

cs.IR↗

arXiv2026

Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?

Computer-use agents (CUAs) increasingly act through desktop GUIs to complete long-horizon tasks. Current benchmarks primarily measure end-task success or single-frame grounding. Neither isolates whether a model can reconstruct the causal, task-relevant transition produced by an action- crucial for rejecting stale observations, verifying progress, and recovering from failure. This is difficult because inference, remote input, app rendering, and screenshot capture are asynchronous: the next observation may be delayed, occluded, transient, or unrelated, then misread as progress and carried into subsequent planning. We introduce Desktop-Delta Bench (DDB), an offline step-level benchmark with 2,013 human-verified instances from novel, multi-app Linux trajectories across ~15 applications and 50 task domains. DDB trajectories targets 3 failure dimensions- state verification, source tracking, and context-aware control- through 2 complementary tasks: 463 3-frame temporal-ordering instances, including 105 with a cross-trajectory decoy, and 1,550 before-after pairs labeled from 5 actions + its payload. We evaluate 8 closed and open-source model families across 32 ordering and 16 single-action settings, observing consistent gaps. Ordering remains unsaturated: best non-decoy and decoy exact-match rates are 65.1% and 65.7%. Task context improves decoy identification by 6.9 percentage points but reduces non-decoy exact match by 2.2 points; error analysis reveals systematic copying of the presented A-B-C order. Single-action results show that inferring the action family is harder than locating it: click F1 is 0.96 vs, 0.76 for drag, while recognized drags are generally localized well. DDB, thus, complements end-to-end benchmarks by filling the missing diagnostic layer between GUI grounding and final task success, enabling targeted improvements to desktop CUA verification, reliability, and recovery.

cs.AI↗

arXiv2026

When HTTP 402 Meets the Blockchain: Risks on Emerging x402 Payments

x402 is an emerging payment protocol for Web APIs and autonomous AI agents. x402 extends HTTP 402 with a payment negotiation flow and delegates payment proof verification and on-chain settlement to third-party facilitators. As a result, facilitators serve as a shared payment infrastructure for many independent merchants. This centralizes trust and validation in one component, so a single flaw can affect many services. Despite rapid adoption by major vendors and economically meaningful mainnet activity, the security posture of real-world x402 deployments remains poorly characterized. We present the first systematic study of authorization correctness and execution safety in current facilitator-mediated x402 deployments in the wild, identifying eight security rules for facilitators as critical payment infrastructure. Based on our analysis of rule violations, we derive four new attack vectors, including Free Shopping, Asset Theft, Service Denial, and Gas Abuse. These attacks exploit weaknesses in the real-world facilitator and server implementations and cause severe harm, including direct financial loss to merchants, theft of facilitator-held assets, unbounded sponsor-paid gas/fees, and disruption of payment services. To assess the security of x402 deployments at scale, we propose a semi-automated black-box tool and apply it to 15 major x402 facilitators collectively used by over 60K sellers and 360K buyers. Alarmingly, we find violations in all evaluated facilitators. We responsibly disclosed our findings to the affected parties, who acknowledged the issues and adopted mitigations, including changes by Coinbase. Finally, we complement our controlled testing with an empirical measurement of over 119 million recent Base and Solana transactions, quantifying x402 adoption, facilitator centralization, and ecosystem-level risk indicators.

cs.CR↗

arXiv2026

An ultralow-loss integrated photonic platform for discrete-variable quantum information processing

Photonic integrated circuits offer a scalable and robust route toward quantum information technologies by consolidating photon sources and linear optical networks onto compact, wafer-manufacturable chips. Although silicon photonics has enabled diverse discrete-variable quantum breakthroughs -- spanning multiphoton entanglement, quantum networking, and photonic qubit fusion for quantum computing -- scaling these platforms beyond proof-of-principle demonstrations remains severely constrained by a critical system-level bottleneck. Optical loss compounds rapidly across photon generation, routing, and state analysis, causing multiphoton generation probabilities to plummet exponentially as circuit depth and complexity grow. Here we overcome this rate-loss barrier by demonstrating a monolithic, ultralow-loss silicon nitride (Si$_3$N$_4$) integrated photonic platform engineered for high-performance discrete-variable quantum information processing. Our architecture seamlessly integrates narrowband photon-pair sources with low-loss qubit-fusion circuits and reconfigurable state-analysis interferometers. The on-chip sources prepare Einstein-Podolsky-Rosen (EPR) states with a fidelity of 0.9875(3) and exhibit near-unity photon indistinguishability, yielding a heralded Hong-Ou-Mandel interference visibility of 0.990(6). By executing on-chip fusion of two EPR states, we synthesize and characterize four-photon Greenberger-Horne-Zeilinger states with a record fidelity of 0.943(8) and a fourfold count rate of 27 Hz -- more than two orders of magnitude higher than previous silicon-photonic implementations. Combined with standard CMOS-compatible fabrication on 150-mm-diameter wafers, these results establish ultralow-loss Si$_3$N$_4$ integrated photonics as a definitive, manufacturable platform for deployable, large-scale quantum information processors.

quant-ph↗

arXiv2026

Criticality and reduced dynamical resilience in PM2.5 pollution systems

Concentration-based metrics underpin air-quality assessment, while dynamical persistence and recovery describe how rapidly high-PM2.5 episodes dissipate and how strongly they retain memory. Here we introduce a finite-memory multiplicative reversion (FMMR) process that links the lognormal concentration backbone of PM2.5 variability with event recurrence, temporal memory, variance amplification and local dynamical resilience. Across station observations and reanalysis data, elevated PM2.5 regimes show a coherent set of critical signatures: stronger memory, rising autocorrelation, broader upper tails, amplified variance, reduced resilience and more clustered exceedance events. Together, these co-occurring signals reveal dynamical criticality in PM2.5 pollution systems, with critical slowing down expressed as a loss of restoring capacity under high-pollution conditions. A gridded comparison across populated and emission-influenced regions further shows that areas with similar PM2.5 burden can differ in recovery capacity, while eastern China has shifted toward higher resilience during recent air-quality improvements and India and West Africa occupy lower-resilience states. By identifying where pollution burden and recovery capacity diverge, these findings establish dynamical persistence and resilience as complementary dimensions of PM2.5 risk and provide a quantitative basis for resilience-oriented air-quality assessment.

physics.soc-ph↗

arXiv2026

R^3: Advertisement Compliance Rectification via Group-Relative Experience Extractor and Curriculum Reinforcement

Rigorous content moderation is crucial for online advertising but leads to millions of daily rejections. This scale renders manual rectification infeasible, particularly for video advertisements. However, existing safety-driven methods often suffer from aggressive over-editing, which compromises the advertiser's original semantic intent merely to satisfy compliance. In this work, we target the rectification of textual violations in video ads, covering both speech transcripts and on-screen text. We propose R^3, a novel framework designed to harmonize compliance with original semantic intent preservation. Our approach integrates three key innovations: (1) an experience-driven data synthesis framework that bootstraps high-quality supervision via a group-Relative compliance experience extractor; (2) a curriculum Reinforcement learning strategy with hierarchical rewards designed to enforce compliance while maximizing semantic consistency; and (3) a comprehensive video Rectification framework seamlessly integrating text recognition, rewriting, and re-rendering for industrial deployment. Extensive experiments on industrial datasets and online A/B testing demonstrate that R^3 significantly outperforms state-of-the-art baselines, achieving an optimal trade-off between violation rectification and intent preservation.

cs.CL↗

arXiv2026

DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation

Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjects according to user instructions. However, evaluating these models remains a significant challenge. Existing benchmarks exhibit critical limitations: 1) insufficient diversity and comprehensiveness in subject images, 2) inadequate granularity in assessing model performance across different subject difficulty levels and prompt scenarios, and 3) a profound lack of actionable insights and diagnostic guidance for subsequent model refinement. To address these limitations, we propose DSH-Bench, a comprehensive benchmark that enables systematic multi-perspective analysis of subject-driven T2I models through four principal innovations: 1) a hierarchical taxonomy sampling mechanism ensuring comprehensive subject representation across 58 fine-grained categories, 2) an innovative classification scheme categorizing both subject difficulty level and prompt scenario for granular capability assessment, 3) a novel Subject Identity Consistency Score (SICS) metric demonstrating a 9.4\% higher correlation with human evaluation compared to existing measures in quantifying subject preservation, and 4) a comprehensive set of diagnostic insights derived from the benchmark, offering critical guidance for optimizing future model training paradigms and data construction strategies. Through an extensive empirical evaluation of 19 leading models, DSH-Bench uncovers previously obscured limitations in current approaches, establishing concrete directions for future research and development.

cs.CV↗

arXiv2026

Comb-enabled spectral-domain image transport through perturbation-prone multimode fibers

Multimode fibers (MMFs) offer a compact platform for imaging, sensing, and information transport, but their practical deployment is hindered by sensitivity to fiber perturbations, which alter modal coupling and invalidate conventional speckle-based calibrations. Here, we demonstrate perturbation-resilient image transport through MMFs by combining image-to-spectrum encoding with dual-comb spectroscopy. Two-dimensional images are converted into comb-line-resolved spectral signatures before fiber transmission, allowing spatial information to be carried in the spectral domain rather than in the output speckle field. After propagation, dual-comb heterodyne detection maps the encoded spectrum into the radio-frequency domain, enabling massively parallel spectral readout with a single photodetector. Neural-network-assisted compressive reconstruction further enables high-fidelity imaging from sparse, noisy, and spectrally aliased measurements. Our approach achieves Pearson correlation coefficients exceeding 0.9 under strong fiber perturbations and supports frame rates up to 2.5 MHz, allowing the observation of transient switching dynamics in a digital micromirror device. These results establish a powerful tool for robust, real-time image transport through flexible MMFs, with potential applications in remote sensing and fiber-based optical instrumentation.

physics.optics↗

arXiv2026

Modeling Earth-Scale Human-Like Societies with One Billion Agents

Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations. Traditional agent-based models (ABMs) have been employed to study these dynamics, but are constrained by simplified agent behaviors. Recent advances in large language models (LLMs) enable agents to exhibit sophisticated social behaviors, yet face significant scaling challenges. We present Light Society, an agent-based simulation framework that advances both fronts. Light Society formalizes social processes as structured transitions of agent and environment states, governed by a set of LLM-powered simulation operations. Joint algorithmic and system optimizations, particularly a mixture-of-models engine that combines full LLMs with distilled surrogates, enable Light Society to efficiently simulate societies with over one billion agents. Grounded in real-world demographic profiles from the World Values Survey, simulations of Trust Games and opinion diffusion at up to one billion agents demonstrate Light Society's high fidelity and efficiency in modeling diverse social phenomena, providing researchers with a practical foundation for hypothesis testing and the study of emergent collective behaviors at planetary scale.

cs.MA↗

arXiv2026

Probing the ubiquity of complex ices in protostars with JWST: the first systematic quantification of weak ice bands between 6.8 and 7.9 micron

Complex organic molecules (COMs) are the key to understanding the chemical evolution from simple interstellar molecules to potential prebiotic material. Although COMs have been extensively studied in the gas phase toward protostars, their counterparts in ices, where they are thought to form at earlier stages, remain far less constrained. A number of diagnostic features of complex ices lie between 6.8 and 8.8 um, a region known as the "COM ice fingerprint range," but previous infrared facilities lacked the sensitivity and spectral resolution required to quantify the weak bands therein. With the unprecedented sensitivity and resolving power of JWST, these limitations can now be overcome. Here, we present the first large-sample quantitative study of the absorption features at 7.02, 7.24, 7.40, and 7.67 um, using MIRI-MRS spectra of 21 protostars. The CH4 band at 7.67 um is the strongest band and shows remarkably uniform peak positions (7.67-7.68 um) and FWHMs (0.06-0.08 um), suggesting CH4 ice as its dominant carrier. The 7.24 and 7.40 um bands exhibit larger source-to-source variations in peak positions and FWHMs, but their occurrence and intensities are strongly correlated with each other. Comparisons with existing and new laboratory spectra suggest HCOO- as the most likely carrier of these two bands, yet HCOO- cannot fully reproduce their intensity ratios, implying additional contributions from other species such as C2H5OH, CH3CHO, and CH3COCH3. Our results reveal, for the first time, the potential ubiquity of weak features of complex ices in protostars, which have remained largely undetected due to observational limitations.

astro-ph.GA↗

arXiv2026

ASSCG: Just-Right Gating over Chattering for Fast-Slow LLM Planning in Autonomous Driving

Large language models (LLMs) can improve autonomous driving planning but are costly to query online, and existing fast-slow planners often rely on hand-designed triggering rules that either over-call the slow system or call it at the wrong times. We formulate slow-system invocation as a resource-aware sequential decision problem and propose the Adaptive Slow-System Control Gate (ASSCG), which makes frame-level Query/Cache/Drop decisions to refresh, reuse, or suppress slow guidance. ASSCG uses an RWKV backbone for efficient long-horizon gating and is trained with supervised fine-tuning followed by GRPO-style compute-aware reinforcement fine-tuning. We apply ASSCG to two different fast-slow architectures: (i) AsyncDriver on nuPlan Hard20 closed-loop evaluation, where ASSCG improves score to 67.28 (+2.28) while reducing average end-to-end inference latency by 60%; and (ii) a RecogDrive-based dual system that we build by replacing its original VLM-2B module with a lightweight ViT-based fast planner and adding an LLM slow planner, evaluated on NAVSIM, where ASSCG achieves 91.4 PDMS (+0.6) and increases average speed by 25%. The project page, including video visualizations and additional results, is available at https://williamxuanyu.github.io/asscg/.

cs.RO↗

arXiv2026

Reconstructive comb spectroscopy: A single-pixel detection paradigm beyond dual-comb limitations

Frequency comb spectroscopy has revolutionized broadband molecular fingerprinting with mode-defined resolution. While dual-comb spectroscopy stands as a dominant paradigm for high-resolution measurements, it relies on mutually coherent dual combs, and its applicability to non-cooperative sensing is limited by the requirement for phase-sensitive detection and controlled optical returns. Here, we introduce reconstructive comb spectroscopy, a fundamentally different paradigm that eliminates these constraints. By integrating a mode-programmable optical comb with a computational sensing scheme based on single-pixel detection, our method achieves picometer-level spectral resolution over a 10-nm (1.27-THz) instantaneous bandwidth, with single-photon sensitivity down to 10^-4 photons per pulse, and compressed spectral acquisition at 2.5% sampling while maintaining reconstruction errors below 10%. We demonstrate robust performance through scattering media and from non-cooperative targets. These capabilities establish reconstructive comb spectroscopy as a new platform for gas sensing, with broad applicability in remote atmospheric monitoring, industrial leak detection, and standoff chemical-threat identification.

physics.optics↗