arXiv subjects

Bin Wu

Publications and source records attributed to Bin Wu.

At least 55 records · Page 3Linked to original sources

arXiv2026

Quantum Chip Paradigm Framework

Quantum Electronic Design Automation (Q-EDA) is emerging as quantum chips move from laboratory prototypes to scalable engineering systems. This paper argues that superconducting quantum chip design is approaching a "SPICE moment" similar to early classical EDA, where growing qubit scale, control complexity, frequency planning, packaging, process variation, and cryogenic measurement feedback require a shift from experience-based design to model-driven engineering. We propose a Quantum Chip Paradigm Framework that treats Q-EDA not only as software, but as part of the quantum chip development paradigm. Unlike classical HDL-first design, quantum chip design must begin with physical structures such as Josephson junctions, resonators, couplers, readout elements, control lines, and packaging environments. The framework emphasizes PCell-based modeling, SPICE-Q simulation, Quantum PDKs, and design-technology-measurement co-optimization. We further outline a hierarchical Q-EDA system spanning physical structures, qubit PCells, logical qubits, quantum arithmetic, functional quantum IP, and Quantum SoC systems. The key goal is to turn physical models, layout rules, simulation results, fabrication data, and measurement feedback into reusable and auditable engineering objects for large-scale quantum processors and fault-tolerant quantum computing.

quant-ph↗

arXiv2026

SPICE-Q and Large-Scale Quantum Chip Production

We propose SPICE-Q, a SPICE-inspired design-technology co-optimization framework for superconducting quantum processors. Rather than replacing tools such as HFSS, Qiskit Metal, pyEPR, SQcircuit, SQuADDS, scqubits, or QuTiP, SPICE-Q aims to connect them through a unified, traceable data chain spanning process rules, layout, electromagnetic simulation, energy-participation-ratio and circuit quantization, Hamiltonian extraction, noise analysis, cryogenic test, and manufacturing feedback. The central mapping is from process and PDK constraints to layout geometry, electromagnetic modes, equivalent circuit parameters, effective Hamiltonians, and finally metrics such as frequency, coupling, anharmonicity, decoherence, readout performance, and yield. This flow must capture Josephson-junction variability, transmon frequency allocation, resonator and Purcell constraints, coupler crosstalk, microwave routing, 3D interconnects, material/interface loss, package modes, and wafer-scale process statistics. By introducing standardized model interfaces, statistical parameter models, model cards, version governance, and closed-loop calibration from cryogenic and fabrication data, SPICE-Q frames superconducting quantum-chip design as an engineering workflow rather than a collection of isolated simulations. We argue that scalable and fault-tolerant quantum processors will require such a continuous model chain from device physics and electromagnetic fields to quantum dynamics, noise, manufacturability, and system-level yield.

quant-ph↗

arXiv2026

Fabless Quantum Chip Design and Commercial Production

This paper proposes a fabless quantum-chip design and production architecture for superconducting quantum computing, centered on the SPICE-Q multiphysics simulation framework. The proposed ecosystem connects process-certified quantum PDKs, parameterized device cells, traceable model cards, SPICE-Q physical modeling languages, unified Q-EDA flows, foundry sign-off rules, cryogenic test feedback, and reusable quantum IP. In this model, design firms do not merely outsource fabrication; they prepare verified tape-outs under standardized process constraints and calibrated physical models. Its economic value lies in reducing repetitive device debugging, process exploration, and low-level layout effort, while its feasibility depends on PDK maturity, foundry yield, cryogenic test throughput, model-prediction accuracy, data-feedback mechanisms, and IP licensing boundaries. We argue that superconducting quantum chips can move from the current largely vertically integrated development model toward a fabless-foundry ecosystem only when hardware design is supported by standardized, verifiable, and reusable software and process interfaces. The required pillars are certified PDKs, PCell-based parameterized design, SPICE-Q cross-physics simulation, end-to-end Q-EDA automation, and a tradable quantum-IP market. By adapting lessons from the classical semiconductor industry to quantum hardware, this framework defines a path toward scalable, manufacturable, and commercially reusable superconducting quantum-chip design.

quant-ph↗

arXiv2026

Atom Optics for Multidimensional Raman Interferometry

The coherent control of atomic wave packets in multiple momentum dimensions is a central challenge in Raman atom optics and a key requirement for multidimensional atom interferometry. In this paper, a momentum-basis theoretical framework is developed for two-dimensional Raman interactions. We formulate the atom-laser interaction for two-dimensional Raman beams and obtain the effective two-level ground-state Hamiltonian after adiabatic elimination of the excited states. The resulted Hamiltonian is constructed on the reachable momentum-state lattice and includes AC Stark shifts, intra-dimensional Raman couplings, and cross-dimensional Raman coupling terms, thereby describing multidimensional Raman dynamics beyond a simple superposition of independent single-dimensional models. The framework is used to analyze the principles and operating conditions of two-dimensional atom interferometry. Cross-dimensional Raman coupling and sequential reverse Raman transitions are identified as the main mechanisms that redistribute atoms into non-target momentum states and reduce the contrast of atomic interferometer fringe. Experimentally, Ramsey fringes are observed in a cold-atom fountain interferometer, with a contrast of $14.8\%$ at an interrogation time of $T=10~{\rm ms}$, in good agreement with the theoretical calculation. The conditions required for velocity-sensitive multidimensional atom interferometry are further clarified, including detuning control, velocity-class selection, and suppression of undesired inter-dimensional transitions. This work provides a theoretical and experimental basis for multidimensional Raman atom interferometry.

physics.atom-ph↗

arXiv2026

Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards

Recent advances in large language models (LLMs) trained with reinforcement learning (RL) have improved Text-to-SQL performance. However, RL-based approaches still struggle with complex queries due to two key limitations: insufficient stepwise execution-aware reasoning grounded in database feedback, and the lack of process-level rewards for guiding reasoning optimization. To address these issues, we propose CoCTE, a divide-and-conquer and execution-aware reasoning framework that progressively composes SQL queries through intermediate view validation and structured Common Table Expressions (CTEs), improving both accuracy and interpretability. To realize a CoCTE reasoning process, we develop Reward-SQL, a unified approach with three stages: (1) model initialization, which equips LLMs with structured CoCTE reasoning capabilities; (2) process reward design, which delivers fine-grained, execution-aware supervision; and (3) process-supervised RL and inference, which integrates process rewards into training and guides the inference stage by process rewards. This paper addresses the core challenges in Reward-SQL and makes the following contributions. We introduce a process reward model (PRM) that combines execution-aware trajectory scoring with entropy-based step weighting, providing dense and interpretable supervision across reasoning steps. We integrate PRM into both RL training and inference stages, stabilizing optimization and improving trajectory exploration with process-level signals. Experiments show that Reward-SQL significantly outperforms baselines with comparable model sizes, and exhibits strong cross-domain generalization.

cs.CL↗

arXiv2026

On Compositeness of $D_{s0}^*(2317)$ and its decay to $D_sπ^0$

We demonstrate that the $D_{s0}^{*}(2317)$ can be described as a bound-state pole arising from the coupling between a discrete $c\bar{s}(1^3P_0)$ state and the $DK$ continuum state in the Lee-Friedrichs model. The elementariness and compositeness of the $D_{s0}^{*}(2317)$ are determined to be about $Z:X\approx 51.1 \%:48.9\%$, indicating a nearly equal admixture of compact quark-model state and hadronic molecular components. The decay width of $D_{s0}^{*}(2317)\rightarrow D_s^{+}π^0$ is evaluated within the quark rearrangement framework. The transition $c\bar{s}(1^3P_0)\rightarrow D_sπ^0$ proceeds via the OZI-allowed $c\bar{s}(1^3P_0)-D_sη$ coupling followed by $η-π^0$ mixing, which serves as the primary source of isospin violation in this channel. The coupling between $DK$ and $D_sπ^0$ is computed in a quark rearrangement model, where the isospin violation effect originates from the mass difference between the $D^0K^+$ and $D^+K^0$ thresholds. The parameters of the scheme shares the same ones with those of the underlying potential model and only the vacuum production strength is adjusted to reproduce the physical $D_{s0}^{*}(2317)$ mass. This calculation may shed more insight to the nature of exotic $D_{s0}^{*}(2317)$ state and its isospin-breaking decay properties.

hep-ph↗

arXiv2026

InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

The rapid evolution of Large Language Models has catalyzed a surge in scientific idea production, yet this leap has not been accompanied by a matching advance in idea evaluation. The fundamental nature of scientific evaluation needs knowledgeable grounding, collective deliberation, and multi-criteria decision-making. However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened evaluation dimensions, and the inherent bias in LLM-as-a-Judge. To address these, we regard idea evaluation as a knowledge-grounded, multi-perspective reasoning problem and introduce InnoEval, a deep innovation evaluation framework designed to emulate human-level idea assessment. We apply a heterogeneous deep knowledge search engine that retrieves and grounds dynamic evidence from diverse online sources. We further achieve review consensus with an innovation review board containing reviewers with distinct academic backgrounds, enabling a multi-dimensional decoupled evaluation across multiple metrics. We construct comprehensive datasets derived from authoritative peer-reviewed submissions to benchmark InnoEval. Experiments demonstrate that InnoEval can consistently outperform baselines in point-wise, pair-wise, and group-wise evaluation tasks, exhibiting judgment patterns and consensus highly aligned with human experts.

cs.CL↗

arXiv2026

Automated Conjecture Resolution with Formal Verification

Recent advances in large language models have significantly improved their ability to perform mathematical reasoning, extending from elementary problem solving to increasingly capable performance on research-level problems. However, reliably solving and verifying such problems remains challenging due to the inherent ambiguity of natural language reasoning. In this paper, we propose an automated framework that integrates natural language reasoning with formal verification to tackle research-level mathematical problems. Our framework consists of two components: an informal reasoning agent, Rethlas, and a formal verification agent, Archon. Rethlas combines reasoning primitives with our theorem search engine, Matlas, to explore solution strategies and construct candidate proofs. Archon, equipped with LeanSearch, translates informal arguments into formalized Lean 4 projects through task decomposition, iterative refinement, and automated proof synthesis, ensuring machine-checkable correctness. Using this framework, we resolve an open problem in commutative algebra and formally verify the resulting proof in Lean 4 with essentially no human involvement. Additional case studies illustrate the capabilities of Rethlas in informal mathematical reasoning and discovery, as well as the ability of Archon to formalize research-level proofs in Lean 4. Our experiments demonstrate that strong theorem retrieval tools enable the discovery and application of cross-domain mathematical techniques, while the formal agent can autonomously fill nontrivial gaps in informal arguments. More broadly, our work illustrates a promising paradigm for mathematical research in which informal and formal reasoning systems, equipped with theorem retrieval tools, operate in tandem to produce verifiable results, reduce human effort, and support human-AI collaborative mathematical research.

cs.LG↗

arXiv2026

Changes in behaviour when adherers to an intervention experience a different epidemic than non-adherers

Non-pharmaceutical interventions (NPIs), including mask-wearing, physical distancing, and hygiene measures, provide the primary means of reducing transmission in the early stages of an epidemic. Individuals adopt one of two strategies-adherence (A) or non-adherence (N) to NPIs. These strategies influence the transmission rate and thus the number of infections, but they also come with inherent costs and benefits. We propose a model coupling behavior and disease dynamics in adherers and non-adherers based on the SIR framework. This gives rise to six behavioral-epidemiological compartments. Using numerical simulations and analytical considerations, we first examine the case where strategies are fixed. Stronger NPIs and more initial adherers lead to fewer infections, and adherers consistently experience lower infection risk than non-adherers. We then introduce behavioral switching based on the benefits and costs of the two strategies. When NPIs are effective, higher transmission rates promote adherence, resulting in fewer infections. Strikingly, in high-severity outbreaks, even modestly effective NPIs can significantly reduce infections. These findings highlight the critical role of the coupling between behavior and disease dynamics, and underscore how individual choices can compromise or compensate public health interventions.

q-bio.PE↗

arXiv2026

Conditional stability for an inverse problem of a fully-discrete stochastic hyperbolic equation

In this paper, we investigate a discrete inverse problem of determining three unknowns, i.e. initial displacement, initial velocity and random source term, in a fully discrete approximation of one-dimensional stochastic hyperbolic equation. We firstly prove a new Carleman estimate for the fully-discrete stochastic hyperbolic equation. Based on this Carleman estimate, we then establish a Lipschitz stability for this discrete inverse problem by the discrete spatial derivative data at the left endpoint and the measurements of the solution and its time derivative at the final time. Owing to the discrete setting, an extra term with respect to mesh size arises in the right-hand side of the stability estimate.

math.AP↗

arXiv2026

Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation

Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching distillation (DMD) as the de facto choice. Existing methods, however, train the student to match the teacher's output indiscriminately, treating every rollout, frame, and pixel as equally reliable supervision. We argue that this caps distilled quality, since it overlooks two complementary axes of variance in DMD supervision: Inter-Reliability across student rollouts whose supervision varies in reliability, and Intra-Perplexity across spatial regions and temporal frames that contribute unequally to where quality can still be improved. The objective thus conflates two questions under a uniform weight: whether to learn from each rollout, and where to concentrate optimization within it. To address this, we propose Stream-R1, a Reliability-Perplexity Aware Reward Distillation framework that adaptively reweights the distillation objective at both rollout and spatiotemporal-element levels through a single shared reward-guided mechanism. At the Inter-Reliability level, Stream-R1 rescales each rollout's loss by an exponential of a pretrained video reward score, so that rollouts with reliable supervision dominate optimization. At the Intra-Perplexity level, it back-propagates the same reward model to extract per-pixel gradient saliency, which is factored into spatial and temporal weights that concentrate optimization pressure on regions and frames where refinement yields the largest expected gain. An adaptive balancing mechanism prevents any single quality axis from dominating across visual quality, motion quality, and text alignment. Stream-R1 attains consistent improvements on all three dimensions over distillation baselines on standard streaming video generation benchmarks, without architectural modification or additional inference cost.

cs.CV↗

arXiv2026

Mean first passage time and the Kramers escape rate of phase transitions for the Bardeen-AdS-class black hole

In this study, by utilizing the constructed generalized free energy alongside the Mean First-Passage Time and the Kramers escape rate from stochastic dynamics, we have obtained a comprehensive landscape of the phase transitions for the Bardeen-AdS-class black hole. This black hole model admits two distinct categories of solutions. Type I black holes feature a regular black hole solution, and Type II black holes possess a vacuum state solution. In the phase transition between the small black hole and the large black hole for Type I, the process may pass through a stable, metastable, or unstable regular black hole as an intermediate state. In contrast, for Type II black holes, the phase transition occurs exclusively between the vacuum state and the small black hole, and the transition process does not involve any regular black hole intermediate states.

gr-qc↗

arXiv2026

AgentSearchBench: A Benchmark for AI Agent Search in the Wild

The rapid growth of AI agent ecosystems is transforming how complex tasks are delegated and executed, creating a new challenge of identifying suitable agents for a given task. Unlike traditional tools, agent capabilities are often compositional and execution-dependent, making them difficult to assess from textual descriptions alone. However, existing research and benchmarks typically assume well-specified functionalities, controlled candidate pools, or only executable task queries, leaving realistic agent search scenarios insufficiently studied. We introduce AgentSearchBench, a large-scale benchmark for agent search in the wild, built from nearly 10,000 real-world agents across multiple providers. The benchmark formalizes agent search as retrieval and reranking problems under both executable task queries and high-level task descriptions, and evaluates relevance using execution-grounded performance signals. Experiments reveal a consistent gap between semantic similarity and actual agent performance, exposing the limitations of description-based retrieval and reranking methods. We further show that lightweight behavioral signals, including execution-aware probing, can substantially improve ranking quality, highlighting the importance of incorporating execution signals into agent discovery. Our code is available at https://github.com/Bingo-W/AgentSearchBench.

cs.AI↗

arXiv2026

Comparing relativistic and non-relativistic quark pair creation models

We investigate the strong decay properties of light unflavored and strange mesons within a relativistic quark-pair-creation (QPC) framework, and compare the results with those obtained in the conventional non-relativistic QPC model. Our analysis shows that, within the present theoretical and experimental uncertainties, the relativistic QPC model yields predictions for strong decay widths of comparable overall quality to those of the non-relativistic QPC model. This indicates that the non-relativistic QPC approach remains adequate for estimating decay widths in most practical applications. Nevertheless, owing to the inclusion of Lorentz boosts and Wigner rotations, the relativistic QPC model exhibits a stronger suppression of decay amplitudes in the high-energy region. This feature may be useful in studies based on unquenched quark models, where the relativistic QPC coupling could lead to more controlled meson-loop effects and mass shifts.

hep-ph↗

arXiv2026

Expected Shortfall Panel Regression

Expected Shortfall (ES) is a coherent measure of tail risk that captures the average loss beyond a quantile threshold. Despite the growing literature on ES regression conditional on covariates, no existing work considers ES modeling in panel data settings where both cross-sectional and temporal dependencies are present. This paper introduces the panel ES regression model with a latent factor structure to capture cross-sectional dependence. We develop a two-stage estimation procedure robust to heavy-tailed errors, recovering the conditional quantile in the first stage and iteratively estimating the ES factor model in the second stage. Theoretically, we establish the consistency and asymptotic normality of the proposed two-step ES estimators and derive non-asymptotic error bounds for both the panel quantile and ES estimators. We also provide a non-asymptotic normal approximation for the standardized ES regression estimator, bridging asymptotic theory and finite-sample practice. Simulation evidence shows that the proposed method delivers substantial gains in both parameter estimation and factor recovery, particularly in the presence of latent tail dependence. An empirical application further indicates that the extracted ES factors carry distinct pricing information that is not captured by conventional mean or quantile-based approaches.

stat.ME↗

arXiv2026

Jiggled interferometer: Ground-based gravitational wave detector using rapidly-repeated free-falling test masses

We propose the Jiggled Interferometer (JIGI), a novel ground-based gravitational wave detector employing low-frequency noise mitigation similar to that of space-based detectors. Using rapidly-repeated free-fall test masses, JIGI eliminates seismic and suspension thermal noise during free fall. Compared to the Juggled Interferometer, it offers improved angular stability and avoids tracking lasers. We analyze detrending -- a required step to remove actuation-induced noise -- and show sensitivity gains of about four orders of magnitude in the 0.1-0.3 Hz band, relative to seismic and suspension noise extrapolated from the Cosmic Explorer.

gr-qc↗

arXiv2026

Probabilistic Evolution of Black Hole Thermodynamic States via Fokker-Planck Equation

Employing the generalized free energy landscape and solving the associated Fokker-Planck equation, we obtain the time-dependent probability evolution of the order parameter for the RN-AdS black hole phase transitions. Our analysis reveals two distinct kinetic regimes, namely relaxation dynamics initialized at the unstable maximum and phase transition from the metastable state. Furthermore, we characterize the non-equilibrium irreversibility and macroscopic uncertainty using the entropy production rate and the Shannon entropy. The results demonstrate that the phase transition synchronizes exactly with a prominent peak in the entropy production rate, identifying the barrier crossing event as a process fundamentally driven by maximum thermodynamic dissipation.

gr-qc↗

arXiv2026

Staleness Factors and Volatility Estimation at High Frequencies

In this paper, we propose a price staleness factor model that accounts for pervasive market friction across assets and incorporates relevant covariates. Using large-panel high-frequency data, we derive the maximum likelihood estimators of the regression coefficients, the nonstationary factors, and their loading parameters. These estimators recover the time-varying price staleness probabilities. We develop asymptotic theory in which both the dimension $d$ and the sampling frequency $n$ tend to infinity. Using a local principal component analysis (LPCA) approach, we find that the efficient price co-volatilities (systematic and idiosyncratic) are biased downward due to the presence of staleness. We provide bias-corrected estimators for both the spot and integrated systematic and idiosyncratic co-volatilities, and prove that these estimators are robust to data staleness. Interestingly, besides their dependence on the dimensionality $d$, the integrated plug-in estimates converge at a rate of $n^{-1/2}$ without requiring correcting term, whereas the local PCA estimates converge at a slower rate of $n^{-1/4}$. This validates the aggregation efficiency achieved through nonlinear, nonstationary factor analysis via maximum likelihood estimation. Numerical experiments justify our theoretical findings. Empirically, we demonstrate that the staleness factor provides unique explanatory power for cross-sectional risk premia, and that the staleness correction reduces out-of-sample portfolio risk.

math.ST↗