arXiv subjects

Hongjian Zhou

Publications and source records attributed to Hongjian Zhou.

At least 19 recordsLinked to original sources

arXiv2026

OptiClear: Differentiable Curvilinear Design Rule Legalization for Inverse-Designed Photonic Devices

Photonic inverse design enables ultra-compact, high-performance devices with highly curvilinear and non-intuitive geometries, but the resulting layouts often violate fabrication design rules and hinder foundry manufacturing. Legalization methods designed for rectilinear Manhattan electrical layouts are not directly applicable to curvilinear inverse-designed photonic devices. Meanwhile, existing fabrication-aware inverse-design methods apply soft penalties on small features and sharp curvatures, but still cannot guarantee design-rule-compliant final layouts. In this work, we present OptiClear, a curvilinear design rule legalization framework for inverse-designed photonic devices. OptiClear provides two complementary legalization engines: OptiClear-R, a rule-based morphological legalizer that efficiently resolves violation regions through iterative morphology-guided mask processing, and OptiClear-D, a differentiable legalizer that formulates legalization as a minimum-distortion mask optimization problem under morphological stationary-point constraints, explicitly seeking a rule-compliant layout with minimal geometric deviation from the original design. We further develop customized differentiable morphological GPU operators that significantly improve the scalability of high-resolution mask legalization. Comprehensive evaluation across diverse inverse-designed photonic devices and a wide range of design-rule settings shows that OptiClear reduces design-rule violations from thousands to zero. The rule-based legalizer offers high runtime efficiency, while the differentiable legalizer more faithfully preserves the original optical functionality. This work establishes curvilinear design rule legalization as a practical post-design electronic-photonic design automation (EPDA) stage for translating high-performance inverse-designed photonic layouts into manufacturable tape-out-ready devices.

physics.optics↗

arXiv2026

BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents

Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness and tool registry differ, and integrating a new model into a comparable evaluation surface costs weeks of model-specific engineering. These are symptoms of a broader reproducibility problem in deep research agent research. Here, we introduce BioMedArena, an open-source toolkit that addresses this reproducibility gap and provides an arena for comparing deep research agents under a shared evaluation environment. BioMedArena decouples six layers of biomedical agent evaluation -- benchmark loading, tool exposure, tool selection, harness mode, context management, and scoring -- and exposes 166 biomedical benchmarks and 75 biomedical tools across 9 functional families. Adding a new model, benchmark, or tool can be accomplished with a few-line provider adapter. Beyond evaluation infrastructure, BioMedArena ships a library of high-quality reference components: 6 agent harnesses (including our proposed Mutual-Evolve) and 6 context-management strategies, any of which can be equipped on any backbone. Equipping these components substantially improves all 12 backbones; on each of 8 representative biomedical benchmarks, the best equipped backbone surpasses prior state-of-the-art (SOTA), by 15.01 percentage points on average. The toolkit, configurations, and per-task traces are available at https://github.com/AI-in-Health/BioMedArena.

cs.AI↗

arXiv2026

Measuring Epistemic Resilience of LLMs Under Misleading Medical Context

Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increasingly use them for health advice. We show this assumption is fragile: when misleading context is injected into questions that LLMs originally answer correctly, they abandon the correct answer. We call the ability to maintain correct judgment under adversarial context epistemic resilience, and introduce MedMisBench to measure it. MedMisBench contains 10,932 medical question items and 48,889 misleading context-option pairs spanning medical reasoning, agentic capability, and patient-journey evaluation. Across 11 model configurations, mean accuracy falls from 71.1% on original questions to 38.0% under focused misleading context, with 51.5% attack success. The most damaging injections are formal, rule-like fabrications: authority-framed falsehoods reach 69.5% attack success and exception-poisoning claims reach 64.1%. A 14-member clinical panel from 7 countries identified serious potential harm in 38.2% of reviewed cases. MedMisBench exposes a structural blind spot in LLM evaluation in medical settings: existing benchmarks measure what models know, but not whether they preserve correct medical judgment under misleading context.

cs.CL↗

arXiv2026

PRISM: Photonics-Informed Inverse Lithography for Manufacturable Inverse-Designed Photonic Integrated Circuits

Recent advances in photonic inverse design have demonstrated the ability to automatically synthesize compact, high-performance photonic components that surpass conventional, hand-designed structures, offering a promising path toward scalable and functionality-rich photonic hardware. However, the practical deployment of inverse-designed PICs is bottlenecked by manufacturability: their irregular, subwavelength geometries are highly sensitive to fabrication variations, leading to large performance degradation, low yield, and a persistent gap between simulated optimality and fabricated performance. Unlike electronics, photonics lacks a systematic, flexible mask optimization flow. Fabrication deviations in photonic components cause large optical response drift and compounding error in cascaded circuits, while calibrating fabrication models remains costly and expertise-heavy, often requiring repeated fabrication cycles that are inaccessible to most designers. To bridge this gap, we introduce PRISM, a photonics-informed inverse lithography workflow that makes photonic mask optimization data-efficient, reliable, and optics-informed. PRISM (i) synthesizes compact, informative calibration patterns to minimize required fabrication data, (ii) trains a physics-grounded differentiable fabrication model, enabling gradient-based optimization, and (iii) performs photonics-informed inverse mask optimization that prioritizes performance-critical features beyond geometry matching. Across multiple inverse-designed components with both electron-beam lithography and deep ultra-violet photolithography processes, PRISM significantly boosts post-fabrication performance and yield while reducing calibration area and turnaround time, enabling and democratizing manufacturable and high-yield inverse-designed photonic hardware at scale.

physics.optics↗

arXiv2026

End-to-End Physical Design Automation Flow for Yield-Optimized Inverse-Designed Large-Scale Electronic-Photonic Integrated Circuits

As AI systems scale to multi-chiplet and wafer-level architectures, the demand for ultra-high bandwidth and system scalability has outpaced the capabilities of electrical interconnects and computing units. Large-scale heterogeneous electronic-photonic integrated chiplets (EPICs) provide a promising solution, but their practical adoption is limited by the lack of a unified, fabrication-aware physical design automation stack. At the same time, inverse-designed ultra-compact photonic devices offer orders-of-magnitude improvements in spatial and spectral density, yet remain constrained by insufficient design-for-manufacturing support and yield optimization. In this work, we present OptoSynthesizer, an end-to-end physical design automation flow for yield-optimized, inverse-designed EPICs. It integrates three key components across the physical design pipeline: (1) OptoSynthesizer-InvDes, a physical-AI-augmented, digital-twin-assisted photonic inverse design and photonics-aware inverse lithography framework; (2) OptoSynthesizer-Place, a GPU-accelerated routing-informed EPIC placer for large-scale routability-optimized layout; and (3) OptoSynthesizer-Route, a hierarchical curvy-aware waveguide router with global-planning-assisted electrical-optical co-routing. Together, these toolkits form a seamless flow from EPIC netlists to fabrication-ready, yield-robust GDS layouts. We demonstrate how this framework enables compact large-scale photonic tensor cores and high-bandwidth interconnect fabrics for heterogeneous EPIC platforms, providing a practical foundation for manufacturable large-scale EPICs in next-generation AI systems.

physics.optics↗

arXiv2026

Harnessing Photonics for Machine Intelligence

The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to reshape data movement and computation. This review reframes photonic computing from a circuits-and-systems perspective, moving beyond building-block progress toward cross-layer system analysis and full-stack design automation. We synthesize recent advances through a bottleneck-driven taxonomy that delineates the operating regimes and scaling trends where photonics can deliver end-to-end sustained benefits. A central theme is cross-layer co-design and workload-adaptive programmability to sustain high efficiency and versatility across evolving application domains at scale. We further argue that Electronic-Photonic Design Automation (EPDA) will be pivotal, enabling closed-loop co-optimization across simulation, inverse design, system modeling, and physical implementation. By charting a roadmap from laboratory prototypes to scalable, reproducible electronic-photonic ecosystems, this review aims to guide the CAS community toward an automated, system-centric era of photonic machine intelligence.

physics.optics↗

arXiv2026

RiskAgent: Synergizing Language Models with Validated Tools for Evidence-Based Risk Prediction

Large Language Models (LLMs) achieve competitive results compared to human experts in medical examinations. However, it remains a challenge to apply LLMs to complex clinical decision-making, which requires a deep understanding of medical knowledge and differs from the standardized, exam-style scenarios commonly used in current efforts. A common approach is to fine-tune LLMs for target tasks, which, however, not only requires substantial data and computational resources but also remains prone to generating `hallucinations'. In this work, we present RiskAgent, which synergizes language models with hundreds of validated clinical decision tools supported by evidence-based medicine, to provide generalizable and faithful recommendations. Our experiments show that RiskAgent not only achieves superior performance on a broad range of clinical risk predictions across diverse scenarios and diseases, but also demonstrates robust generalization in tool learning on the external MedCalc-Bench dataset, as well as in medical reasoning and question answering on three representative benchmarks, MedQA, MedMCQA, and MMLU.

cs.LG↗

arXiv2025

Toward Large-Scale Photonics-Empowered AI Systems: From Physical Design Automation to System-Algorithm Co-Exploration

In this work, we identify three considerations that are essential for realizing practical photonic AI systems at scale: (1) dynamic tensor operation support for modern models rather than only weight-static kernels, especially for attention/Transformer-style workloads; (2) systematic management of conversion, control, and data-movement overheads, where multiplexing and dataflow must amortize electronic costs instead of letting ADC/DAC and I/O dominate; and (3) robustness under hardware non-idealities that become more severe as integration density grows. To study these coupled tradeoffs quantitatively, and to ensure they remain meaningful under real implementation constraints, we build a cross-layer toolchain that supports photonic AI design from early exploration to physical realization. SimPhony provides implementation-aware modeling and rapid cross-layer evaluation, translating physical costs into system-level metrics so architectural decisions are grounded in realistic assumptions. ADEPT and ADEPT-Z enable end-to-end circuit and topology exploration, connecting system objectives to feasible photonic fabrics under practical device and circuit constraints. Finally, Apollo and LiDAR provide scalable photonic physical design automation, turning candidate circuits into manufacturable layouts while accounting for routing, thermal, and crosstalk constraints.

physics.optics↗

arXiv2025

Democratizing Electronic-Photonic AI Systems: An Open-Source AI-Infused Cross-Layer Co-Design and Design Automation Toolflow

Photonics is becoming a cornerstone technology for high-performance AI systems and scientific computing, offering unparalleled speed, parallelism, and energy efficiency. Despite this promise, the design and deployment of electronic-photonic AI systems remain highly challenging due to a steep learning curve across multiple layers, spanning device physics, circuit design, system architecture, and AI algorithms. The absence of a mature electronic-photonic design automation (EPDA) toolchain leads to long, inefficient design cycles and limits cross-disciplinary innovation and co-evolution. In this work, we present a cross-layer co-design and automation framework aimed at democratizing photonic AI system development. We begin by introducing our architecture designs for scalable photonic edge AI and Transformer inference, followed by SimPhony, an open-source modeling tool for rapid EPIC AI system evaluation and design-space exploration. We then highlight advances in AI-enabled photonic design automation, including physical AI-based Maxwell solvers, a fabrication-aware inverse design framework, and a scalable inverse training algorithm for meta-optical neural networks, enabling a scalable EPDA stack for next-generation electronic-photonic AI systems.

physics.optics↗

arXiv2025

Clinical-R1: Empowering Large Language Models for Faithful and Comprehensive Reasoning with Clinical Objective Relative Policy Optimization

Recent advances in large language models (LLMs) have shown strong reasoning capabilities through large-scale pretraining and post-training reinforcement learning, demonstrated by DeepSeek-R1. However, current post-training methods, such as Grouped Relative Policy Optimization (GRPO), mainly reward correctness, which is not aligned with the multi-dimensional objectives required in high-stakes fields such as medicine, where reasoning must also be faithful and comprehensive. We introduce Clinical-Objective Relative Policy Optimization (CRPO), a scalable, multi-objective, verifiable reinforcement learning method designed to align LLM post-training with clinical reasoning principles. CRPO integrates rule-based and verifiable reward signals that jointly optimize accuracy, faithfulness, and comprehensiveness without relying on human annotation. To demonstrate its effectiveness, we train Clinical-R1-3B, a 3B-parameter model for clinical reasoning. The experiments on three benchmarks demonstrate that our CRPO substantially improves reasoning on truthfulness and completeness over standard GRPO while maintaining comfortable accuracy enhancements. This framework provides a scalable pathway to align LLM reasoning with clinical objectives, enabling safer and more collaborative AI systems for healthcare while also highlighting the potential of multi-objective, verifiable RL methods in post-training scaling of LLMs for medical domains.

cs.AI↗

arXiv2025

Experimental road of the $J/ψϕ$ mass spectrum, current status, and implications

Inspired by the $X(4140)$ structure reported in the $J/ψϕ$ system by the CDF experiment in 2009, a series of searches have been carried out in the $J/ψϕ$ and $J/ψK$ channels, leading to the claim of ten structures in the $B \rightarrow J/ψϕK$ system. This article provides a comprehensive review of experimental developments, from the initial evidence of $X(4140)$ at CDF to the amplitude analyses and diffractive process investigations by the LHCb experiment, as well as theoretical interpretations of these states. A triplet of $J^{PC} = 1^{++}$ states with relatively large mass splittings [about 200~MeV (natural units are adopted)] has been identified in the $J/ψϕ$ system by LHCb. Their mass-squared values align approximately linearly with a possible radial quantum number, suggesting that the triplet may represent a radially excited family. For $X(4140)$, the first state in the triplet, its width reported by LHCb is inconsistent with that measured by other experiments, and possible reasons for this discrepancy are discussed. A potential connection between an excess at 4.35~GeV in the $J/ψϕ$ mass spectrum reported by the Belle experiment through a two-photon process and a potential spin-2 excess in the LHCb data is also investigated. In addition, potential parallels between the $J/ψϕ$ and $J/ψJ/ψ$ systems, both composed of two vector mesons, are discussed. The continued interest in, and complexity of, these systems underscore the necessity of further experimental exploration with increased statistical precision across a variety of experiments, particularly those with relatively flat efficiency across the Dalitz plot. The $J/ψω$, $ϕϕ$, $ρω$, and $ρϕ$ systems are mentioned, and the prospects for the $J/ψΥ$ and $ΥΥ$ systems, are also highlighted.

hep-ex↗

arXiv2025

Apollo: Automated Routing-Informed Placement for Large-Scale Photonic Integrated Circuits

As technology advances, photonic integrated circuits (PICs) are rapidly scaling in size and complexity, with modern designs integrating thousands of components. However, the analog custom layout nature of photonics, the curvy waveguide structures, and single-layer routing resources impose stringent physical constraints, such as minimum bend radii and waveguide crossing penalties, which make manual layout the de facto standard. This manual process takes weeks to complete and is error-prone, which is fundamentally unscalable for large-scale PIC systems. Existing automation solutions have adopted force-directed placement on small benchmarks with tens of components, with limited routability and scalability. To fill this fundamental gap in the electronic-photonic design automation (EPDA) toolchain, we present the first GPU-accelerated, routing-informed placement framework. It features an asymmetric bending-aware wirelength function with explicit modeling of waveguide routing congestion and crossings for routability maximization. Meanwhile, conditional projection is employed to gradually enforce a variety of user-defined layout constraints, including alignment, spacing, etc. This constrained optimization is accelerated and stabilized by a custom blockwise adaptive Nesterov-accelerated optimizer, ensuring stable and high-quality convergence. Compared to existing methods, our method can generate high-quality layouts for large-scale PICs with an average routing success rate of 94.79% across all benchmarks within minutes. By tightly coupling placement with physical-aware routing, our method establishes a new paradigm for automated PIC design, bringing intelligent, scalable layout synthesis to the forefront of next-generation EPDA. Our code is open-sourced at https://github.com/ScopeX-ASU/Apollo.

cs.ET↗

arXiv2025

LiDAR 2.0: Hierarchical Curvy Waveguide Detailed Routing for Large-Scale Photonic Integrated Circuits

Driven by innovations in photonic computing and interconnects, photonic integrated circuit (PIC) designs advance and grow in complexity. Traditional manual physical design processes have become increasingly cumbersome. Available PIC layout tools are mostly schematic-driven, which has not alleviated the burden of manual waveguide planning and layout drawing. Previous research in PIC automated routing is largely adapted from electronic design, focusing on high-level planning and overlooking photonic-specific constraints such as curvy waveguides, bending, and port alignment. As a result, they fail to scale and cannot generate DRV-free layouts, highlighting the need for dedicated electronic-photonic design automation tools to streamline PIC physical design. In this work, we present LiDAR, the first automated PIC detailed router for large-scale designs. It features a grid-based, curvy-aware A* engine with adaptive crossing insertion, congestion-aware net ordering, and insertion-loss optimization. To enable routing in more compact and complex designs, we further extend our router to hierarchical routing as LiDAR 2.0. It introduces redundant-bend elimination, crossing space preservation, and routing order refinement for improved conflict resilience. We also develop and open-source a YAML-based PIC intermediate representation and diverse benchmarks, including TeMPO, GWOR, and Bennes, which feature hierarchical structures and high crossing densities. Evaluations across various benchmarks show that LiDAR 2.0 consistently produces DRV-free layouts, achieving up to 16% lower insertion loss and 7.69x speedup over prior methods on spacious cases, and 9% lower insertion loss with 6.95x speedup over LiDAR 1.0 on compact cases. Our codes are open-sourced at https://github.com/ScopeX-ASU/LiDAR.

cs.ET↗

arXiv2025

Photonics-Aware Planning-Guided Automated Electrical Routing for Large-Scale Active Photonic Integrated Circuits

The rising demand for AI training and inference, as well as scientific computing, combined with stringent latency and energy budgets, is driving the adoption of integrated photonics for computing, sensing, and communications. As active photonic integrated circuits (PICs) scale in device count and functional heterogeneity, physical implementation by manual scripting and ad-hoc edits is no longer tenable. This creates an immediate need for an electronic-photonic design automation (EPDA) stack in which physical design automation is a core capability. However, there is currently no end-to-end fully automated routing flow that coordinates photonic waveguides and on-chip metal interconnect. Critically, available digital VLSI and analog/custom routers are not directly applicable to PIC metal routing due to a lack of customization to handle constraints induced by photonic devices and waveguides. We present, to our knowledge, the first end-to-end routing framework for large-scale active PICs that jointly addresses waveguides and metal wires within a unified flow. We introduce a physically-aware global planner that generates congestion- and crossing-aware routing guides while explicitly accounting for the placement of photonic components and waveguides. We further propose a sequence-consistent track assignment and a soft guidance-assisted detailed routing to speed up the routing process with significantly optimized routability and via usage. Evaluated on various large PIC designs, our router delivers fast, high-quality active PIC routing solutions with fewer vias, lower congestion, and competitive runtime relative to manual and existing VLSI router baselines; on average it reduce via count by ~99%, user-specified design rule violation by ~98%, and runtime by 17x, establishing a practical foundation for EPDA at system scale.

physics.optics↗

arXiv2025

Toward Lifelong-Sustainable Electronic-Photonic AI Systems via Extreme Efficiency, Reconfigurability, and Robustness

The relentless growth of large-scale artificial intelligence (AI) has created unprecedented demand for computational power, straining the energy, bandwidth, and scaling limits of conventional electronic platforms. Electronic-photonic integrated circuits (EPICs) have emerged as a compelling platform for next-generation AI systems, offering inherent advantages in ultra-high bandwidth, low latency, and energy efficiency for computing and interconnection. Beyond performance, EPICs also hold unique promises for sustainability. Fabricated in relaxed process nodes with fewer metal layers and lower defect densities, photonic devices naturally reduce embodied carbon footprint (CFP) compared to advanced digital electronic integrated circuits, while delivering orders-of-magnitude higher computing performance and interconnect bandwidth. To further advance the sustainability of photonic AI systems, we explore how electronic-photonic design automation (EPDA) and cross-layer co-design methodologies can amplify these inherent benefits. We present how advanced EPDA tools enable more compact layout generation, reducing both chip area and metal layer usage. We will also demonstrate how cross-layer device-circuit-architecture co-design unlocks new sustainability gains for photonic hardware: ultra-compact photonic circuit designs that minimize chip area cost, reconfigurable hardware topology that adapts to evolving AI workloads, and intelligent resilience mechanisms that prolong lifetime by tolerating variations and faults. By uniting intrinsic photonic efficiency with EPDA- and co-design-driven gains in area efficiency, reconfigurability, and robustness, we outline a vision for lifelong-sustainable electronic-photonic AI systems. This perspective highlights how EPIC AI systems can simultaneously meet the performance demands of modern AI and the urgent imperative for sustainable computing.

physics.optics↗

arXiv2025

Toward Intelligent Electronic-Photonic Design Automation for Large-Scale Photonic Integrated Circuits: from Device Inverse Design to Physical Layout Generation

Photonic Integrated Circuits (PICs) offer tremendous advantages in bandwidth, parallelism, and energy efficiency, making them essential for emerging applications in artificial intelligence (AI), high-performance computing (HPC), sensing, and communications. However, the design of modern PICs, which now integrate hundreds to thousands of components, remains largely manual, resulting in inefficiency, poor scalability, and susceptibility to errors. To address these challenges, we propose PoLaRIS, a comprehensive Intelligent Electronic-Photonic Design Automation (EPDA) framework that spans both device-level synthesis and system-level physical layout. PoLaRIS combines a robust, fabrication-aware inverse design engine with a routing-informed placement and curvy-aware detailed router, enabling the automated generation of design rule violation (DRV)-free and performance-optimized layouts. By unifying physics-driven optimization with machine learning and domain-specific algorithms, PoLaRIS significantly accelerates PIC development, lowers design barriers, and lays the groundwork for scalable photonic system design automation.

cs.ET↗

arXiv2025

Status and prospects of the $χ_{c1}(3872)$ at BESIII

The $χ_{c1}(3872)$ serves as a pivotal role for understanding hadronic structures, remaining one of the most extensively studied exotic particles despite the experimental discovery of numerous unconventional hadronic states. Sustained experimental and theoretical investigations into the particle over the past two decades have propelled its study into a high-precision regime, marked by refined measurements of its decay dynamics and line shape, thereby offering critical insights to resolve longstanding debates between molecular, tetraquark, hybrid, and charmonium interpretations of this particle. The BESIII experiment has made seminal contributions to the study of the $χ_{c1}(3872)$, leveraging its unique capabilities in high-statistics data acquisition and low-background condition. This article gives a concise review and prospects of the study of the $χ_{c1}(3872)$ from the BESIII experiment.

hep-ex↗

arXiv2024

Large Language Models in the Clinic: A Comprehensive Benchmark

The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention. Existing works mainly adopt the close-ended question-answering (QA) task with answer options for evaluation. However, many clinical decisions involve answering open-ended questions without pre-set options. To better understand LLMs in the clinic, we construct a benchmark ClinicBench. We first collect eleven existing datasets covering diverse clinical language generation, understanding, and reasoning tasks. Furthermore, we construct six novel datasets and clinical tasks that are complex but common in real-world practice, e.g., open-ended decision-making, long document processing, and emerging drug analysis. We conduct an extensive evaluation of twenty-two LLMs under both zero-shot and few-shot settings. Finally, we invite medical experts to evaluate the clinical usefulness of LLMs. The benchmark data is available at https://github.com/AI-in-Health/ClinicBench.

cs.CL↗