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Ying Wang

Publications and source records attributed to Ying Wang.

At least 55 records · Page 3Linked to original sources
arXiv2026

Testing a continuous-variable noncontextuality inequality with a hybrid-encoded system

Continuous-variable quantum systems are promising candidates for quantum computing and quantum information processing. It is widely known that quadrature measurements on Gaussian continuous-variable systems admit a noncontextual hidden-variable description. Here, we demonstrate that this description fails once the same Gaussian correlations are instead probed using Hadamard tests. We realize the test with a hybrid discrete--continuous-variable system---the polarization and spatial modes of a single photon deterministically generated from an InAs/GaAs quantum emitter, with the controlled operations being the phase-space displacements selected through the Gottesman--Kitaev--Preskill correspondence. By directly measuring the correlations of the operators, we observe a violation of the noncontextual hidden-variable inequality by 380 standard deviations and independently bound the residual noncommutativity of the implemented operations. Our results open up new possibilities for studying fundamental quantum physics using photonic-encoded continuous-variable systems.

quant-ph↗
arXiv2026

MicroEvo: Knowledge-Guided LLM Sampling for Efficient Microarchitecture Design Space Exploration

Microarchitecture design space exploration suffers from expansive search spaces and expensive PPA evaluation, leaving only a small simulation budget for design decision-making. Existing methods perform blind search without considering microarchitectural dependencies and fail to learn from the iterative search effectively, leading to wasted evaluations and weak Pareto convergence. In this paper, we propose MicroEvo, a knowledge-guided framework that couples off-the-shelf LLMs with Monte Carlo Tree Search (MCTS) for multi-objective microarchitecture optimization. MicroEvo combines LLM-driven evolutionary operators, a Pareto-aware tree policy that balances Pareto contribution and diversity, an active knowledge accumulation mechanism that extracts and reuses optimization insights, and state-aware directives that adapt the search behavior online. Experiments show that MicroEvo improves Pareto-front quality by up to 36.2% over NSGA-II and achieves 10.6x higher search efficiency, and also demonstrates strong scalability to a complex industrial-scale core. The code repository is available at: https://github.com/GEAR-SEU/MicroEvo-ICCAD-26.

cs.AI↗
arXiv2026

Contrastive Branch Policy Optimization

Reinforcement learning with verifiable rewards (RLVR) enables language models to learn multi-turn interaction with external tools, yet its sparse outcome rewards provide no signal for identifying which intermediate decisions are responsible for success. Branch sampling induces local comparisons among alternative continuations, but existing methods tend to conflate two distinct problems: allocating a fixed rollout budget and translating branch outcomes into token-level credit. We introduce Contrastive Branch Policy Optimization (CBPO), which disentangles these two problems and assigns a dedicated mechanism to each. Generation entropy screens candidate branch positions across the entire response, while path-level and node-level decay distribute a fixed budget across trajectories and positions to prevent exploration from collapsing onto a few paths or adjacent tokens. A parent trajectory together with the branches that share an identical token prefix forms an exact-prefix group, and the reward variation within this controlled group defines the Contrastive Branch Value (CBV), an outcome-based estimate of local decision sensitivity that rescales continuation advantages without altering their sign. When multiple nodes are selected along the same trajectory, CBPO partitions it into non-overlapping credit segments, thereby avoiding duplicated gradients on shared tokens. Requiring only outcome rewards and no process-level annotation, CBPO provides a practical solution for fine-grained credit assignment in tool-integrated agent training. Extensive experiments on ten benchmarks, including five for mathematical reasoning and five for knowledge-intensive search, show that CBPO consistently outperforms state-of-the-art policy-optimization and branch-based methods, attaining the highest macro-average accuracy in both domains and across two model scales.

cs.LG↗
arXiv2026

Polymer Genome in the Age of Artificial Intelligence

Artificial intelligence (AI) is redefining the landscape of polymer science. Although numerous AI applications have been introduced in this field, the roles of polymer encoding strategies and different applications of AI models in polymer design remain insufficiently understood. Here, we build upon the foundation of polymer databases to critically examine the performance and applicability of current encoding strategies across different use cases. We then focus on two major AI application domains, property prediction and inverse design, to evaluate the strengths, weaknesses and suitable scenarios for various model architectures. Finally, we emphasize the significance of the online platforms for promoting data accessibility and accelerating the migration from experience-based discovery toward AI-driven innovation in polymer science and engineering. Through these discussions, we aim to provide practical guidance for future research and development in AI-assisted polymer design.

cond-mat.soft↗
arXiv2026

Beyond Legal Spacing: A Residual-Aware Characterization of Entangling-Zone Spacing in Neutral-Atom Compilation

Neutral-atom processors rely on spatially arranged qubit arrays and parallel Rydberg entangling gates for scalable execution. Their compilers enforce geometric spacing rules for simultaneous gates, yet legal separation does not make residual van der Waals coupling disappear. This paper studies that gap between geometric legality and residual noise by treating entangling-zone spacing as a cross-layer reliability-parallelism variable anchored to experimental neutral-atom geometry. We combine fixed-schedule residual replay, surface-code simulation with matched correlated decoding, and fresh recompilation to connect spacing to physical residual exposure, logical reliability, and makespan cost. The results show that near-floor spacing can produce structured correlated exposure that is visible both at the physical layer and, in the tightest case, after quantum error correction (QEC). Modest geometric slack strongly suppresses this residual contribution, but the timing cost of looser spacing is mediated by placement and scheduling rather than by a simple monotonic slowdown. These findings distinguish hardware legality from residual-noise safety and motivate spacing-aware compiler evaluations that report physical geometry, QEC absorption, and scheduling cost together.

quant-ph↗
arXiv2026

Where Atom Loss Lands Matters: Decoder-Aware Risk Deposition in Neutral-Atom QEC

Neutral-atom arrays are emerging as a leading platform for scalable quantum error correction (QEC). Qubits are routed and reused across the array, while detected loss is reported to the decoder as erasure information. Existing neutral-atom compilers optimize this movement, including routing, shuttling, and reuse, and often model loss through scalar exposure costs. Yet total exposure is an incomplete statistic for erasure-corrected QEC. It captures how much loss occurs, but not where it lands on the code, which we call its deposition. Under the same expected atom-loss budget, different deposition patterns over a code patch induce substantially different logical error rates (LER). We formalize this as decoder-aware risk deposition and present CAST, a compiler-side optimization pass that overlays a code-topology sensitivity map on a role-indexed exposure ledger and minimizes a decoder-weighted harm objective under a comparable-exposure constraint, using only local route, role, and seam-cooling actions. Across surface-code memory, physical-scale architecture models, lattice surgery, and decoder-mismatch checks, CAST lowers LER relative to topology-blind exposure minimization, improving on it in 35 of 48 physical-scale settings and by as much as 5.3x where exposure is heterogeneous and routing has slack. The largest gains occur when high exposure and high decoder sensitivity are initially misaligned, giving CAST room to redirect risk toward lower-impact code roles. CAST shows that decoder-aware atom-loss risk deposition can be optimized as a compiler-side pass in neutral-atom QEC.

quant-ph↗
arXiv2026

Decoder-Prior Poisoning in Quantum Error Correction: Attacks and PriorGuard Defense

Quantum error correction (QEC) protects quantum computations by repeatedly measuring stabilizer syndromes and using a classical decoder to infer corrections. Modern surface-code decoders are increasingly calibration-aware: they use recent device behavior to set priors such as matching-graph edge probabilities, and these priors directly shape the selected correction. We identify the prior-update path as an overlooked integrity-critical attack surface: a poisoned prior can change decoding decisions even when the syndrome stream, logical labels, and decoder implementation are unchanged. This makes prior poisoning different from ordinary syndrome anomalies and difficult for raw syndrome-anomaly tests to detect. We introduce PriorGuard, a lightweight semantic guard that checks proposed prior changes on high-influence detector-graph entries against private syndrome-derived evidence, accepts updates only when their log-odds movement is evidence-supported, and otherwise falls back to the last evidence-supported prior. We evaluate on Stim-generated rotated surface-code memory circuits decoded with PyMatching. Prior influence is highly concentrated, with the top 10 percent of entries carrying over 60 percent of positive logical-error influence; poisoning only 6.4 percent of decoder-prior entries can raise logical error rate (LER) by 3.35x. PriorGuard recovers most attack-induced loss with a low false-positive rate and modest update-boundary overhead of about 1.7--1.8 ms and less than 0.31 MB auxiliary memory per update.

quant-ph↗
arXiv2026

Scattering Theory For 3D Cubic Damped Magnetic Schrödinger Equation

We consider the three-dimensional defocusing cubic nonlinear Schrödinger equation with variable coefficients, a magnetic potential, and a non-negative localized damping term, \[ i\partial_tu+(\nabla-iA)\cdot G(\nabla-iA)u+ia(x)u=|u|^2u, \qquad t>0,\quad x\in\mathbb R^3. \] No non-trapping condition is imposed on the metric $G$. Instead, the variable-coefficient region is assumed to be contained in the effective damping region. Under a one-centre condition on the tangential magnetic field, we prove global well-posedness for initial data in $H^{1+\varepsilon}$, uniform mass and energy bounds, and show the local energy decay. To obtain scattering, we impose a support condition on the full magnetic field inside the damping region. Under these stronger assumptions, the solution scatters to a free Schrödinger evolution in $H^s$ for every $0\le s<1$. The appendix discusses a separate constant-damping framework for abstract Hamiltonians.

math.AP↗
arXiv2026

Temporal Straightening for Latent Planning

Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental -- to planning. Inspired by the perceptual straightening hypothesis in human visual processing, we introduce temporal straightening to improve representation learning for latent planning. Using a curvature regularizer that encourages locally straightened latent trajectories, we jointly learn an encoder and a predictor of a Joint-Embedding Predictive Architecture (JEPA) world model. We show that reducing curvature this way makes the Euclidean distance in latent space a better proxy for the geodesic distance and improves the conditioning of the planning objective. We demonstrate empirically that temporal straightening makes gradient-based planning more stable and yields significantly higher success rates across a suite of goal-reaching tasks. Our code is available at https://agenticlearning.ai/temporal-straightening.

cs.LG↗
arXiv2026

Hankel Transform and $(α,β)$ Somos-4 Sequences

An $(α,β)$ Somos-$4$ sequence $S_n$ is defined by the recurrence $S_nS_{n-4}=αS_{n-1}S_{n-3}+βS_{n-2}^2$ ($n\geq 4$), with suitable initial values, where $α$ and $β$ are constant parameters. A widely studied question is the following: When does the Hankel transform of a generating function become an $(α,β)$ Somos-4 sequence? In particular, how can $α$ and $β$ be derived for such a function? A sufficient condition for this problem has been established by Wang and Zhang. In this paper, we obtain the following three main results. (i): We extend the Wang--Zhang sufficient condition by working over the rational function field. Then we combine this result with the Sulanke--Xin quadratic transformation to resolve all of Barry's currently unsolved $(α,β)$ Somos-4 conjectures, which arise in diverse contexts, including generalized Catalan recurrences, Riordan arrays, generalized Bernstein arrays, and elliptic curves. (ii): We show that the odd and even subsequences of an $(α,β)$ Somos-4 sequence are again $(α,β)$ Somos-4 sequences with transformed parameters. This is employed to establish Barry's Hurwitz transform conjecture. (iii): Using the theory of orthogonal polynomials, we prove a Hankel determinant formula and thereby prove a conjecture related to the $(α,β)$ Somos-4 sequence. In addition, we prove some conjectures on formulas for periodic Hankel determinants.

math.CO↗
arXiv2026

Rethinking Unified Memory for NPU-PIM Systems: Dual-View Memory for Dynamic Inference of LLM

Heterogeneous architectures that combine neural processing unit (NPU) and processing-in-memory (PIM) are increasingly adopted to accelerate LLM inference. Prior work focuses on building a unified memory that allows NPUs and PIM to share data without duplication. However, these designs implicitly assume that each tensor is bound to a fixed execution device, and therefore rely on static, device-biased data mappings. We observe that this assumption does not hold in modern LLM workloads. Due to phase changes (e.g., prefill vs. decode) and dynamic behaviors such as MoE routing, the optimal execution device for the same tensor can change at runtime. Under such dynamic execution, device-biased mappings become mismatched to access patterns, leading to substantial bandwidth underutilization and performance loss. This paper presents PFM (PIM-as-Flexible-Memory), a dual-view memory system that decouples physical data layout from accessor-visible logical views. PFM stores data in a jointly optimized physical layout and exposes different logical interpretations to NPUs and PIM, enabling efficient access across devices without data duplication or relayout. We further design accessor-aware address translation and runtime scheduling mechanisms to support dynamic execution when LLM workloads fluctuate and the optimal execution device dynamically changes. Our evaluation across LLMs shows that PFM improves end-to-end throughput by up to 2.32$\times$, demonstrating its effectiveness and broad applicability as a unified memory management solution for NPU-PIM systems.

cs.AR↗
arXiv2026

Pattern Zooming: Near-Field Wideband Beam Training with Wavenumber-Domain Codebook

Near-field beam training is essential for harvesting the high-gain potential of extremely large-scale multiple input multiple output systems. To reduce training overhead, existing works have mostly leveraged the beam squint effect by utilizing time-delay (TD) beamforming with polar-domain codebook, which enables the simultaneous sweeping of multiple angles at a specific distance. However, such methods still suffer from high overhead due to the exhaustive distance searching. To address this challenge, we propose a pattern zooming based near-field wideband beam training with wavenumber-domain codebook. Specifically, we first establish a refined wideband Fourier planewave channel representation, based on which we reveal a pattern zooming effect, where the wavenumber-domain patterns across different subcarriers exhibit frequency-dependent scaling relative to the center frequency. Exploiting this property, we develop a TD-assisted beam sweeping strategy that simultaneously probes multiple wavenumber directions to rapidly acquire the complete wavenumber-domain pattern. Based on the acquired pattern, we further derive exact, approximation-free closed-form expressions that establish a rigorous mapping between the receiver coordinates and the wavenumber-domain pattern, enabling accurate user localization with only a few pilots. Finally, numerical results validate the superiority of our proposed scheme in terms of both beamforming gain and training overhead.

eess.SP↗
arXiv2026

AP Association for RHS-Enabled Cell-Free Uplink MIMO in Industrial Indoor UAV Networks

Indoor industrial UAV uplink networks face serious blockage and shadowing from shelves, metal equipment, and production facilities. UAVs are also often clustered and fly along similar straight inspection routes at fixed heights. These features make traditional small-cell deployment less suitable, especially when high reliability, continuous coverage, and good service for weak UAVs are required. Cell-free networks can improve robustness through distributed access points (APs) and UAV?centric communications. Reconfigurable holographic surface (RHS)-enabled APs provide programmable analog receive beams and generate scalar post-RHS observations, which are jointly processed at the CPU for distributed uplink MIMO detection at relatively low hardware cost. Conventional AP association relies on distance, large-scale fading, or post-combining SINR obtained with user-specific digital combiners. Here, however, all UAVs served by a single-feed RHS AP share one amplitude?constrained receive pattern and one scalar AP output. We therefore derive an SINR-like score from this physical output and, under weak inter-AP disturbance correlation, approximate the CPU-side log-det objective by an additive per-AP surrogate, yielding a low-complexity ranking rule. The results show that the nearest AP is not always the best choice, the AP-UAV height difference may have an optimal value, and larger serving clusters bring diminishing returns. Simulations show that the proposed method improves the minimum UAV data rate, average spectral efficiency, fairness, and energy efficiency compared with benchmark schemes.

cs.NI↗
arXiv2026

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL

Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate. Existing work therefore focuses on small-scale circuits with clean labels, limiting scalability to realistic designs. Meanwhile, Large Language Models (LLMs) can generate Register-Transfer-Level (RTL) at scale, but their functional incorrectness has hindered their use in circuit analysis. In this work, we make a key observation: even when LLM-Generated RTL is functionally imperfect, the synthesized netlists still preserve structural patterns that are strongly indicative of the intended functionality. Building on this insight, we propose a cost-effective data augmentation and training framework that systematically exploits imperfect LLM-Generated RTL as training data for netlist representation learning, forming an end-to-end pipeline from automated code generation to downstream tasks. We conduct evaluations on circuit functional understanding tasks, including sub-circuit boundary identification and component classification, across benchmarks of increasing scales, extending the task scope from operator-level to IP-level. The evaluations demonstrate that models trained on our noisy synthetic corpus generalize well to real-world netlists, matching or even surpassing methods trained on scarce high-quality data and effectively breaking the data bottleneck in circuit representation learning.

cs.LG↗
arXiv2026

RTLCurator: Label-Efficient Data Curation for RTL Generation

Training large language models (LLMs) to write register-transfer level (RTL) requires large corpora of paired specifications and code, and such data is scarce enough that most public corpora are now synthesized. Synthesis provides scale but not correctness, and in two widely used RTL datasets only 24.4% and 53.5% of pairs pass generated functional tests. This raises the question of how much of such a corpus to keep and which part of it. Correctness alone is a poor answer. A pair that misbehaves in one corner case still shows valid syntax and interface conventions, and complex sequential designs are both harder to generate and harder to validate, so filtering by correctness leaves a corpus of short and simple modules. Correctness is also hard to obtain, since behavior leaves little trace on the surface in RTL, and validating an entire corpus only sorts pairs into passed and failed. We present RTLCurator, which learns a behavior-aware compatibility prior by contrasting each specification with implementations that fail simulation, and calibrates it to a new corpus using a small number of validated pairs. It then constructs the retained subset by balancing alignment, representation coverage, and RTL structural richness. On CodeV and RTLCoder, keeping 80% of the corpus this way improves on training with the full corpus across all reported metrics while validating only 10% of the pool, whereas ranking by the score alone falls below random selection and filtering the whole pool by simulation does no better.

cs.AR↗
arXiv2026

GLST: Defending Confidence-Driven V2X Collaborative Perception Against Stealthy Multi-Attacker Feature Injection

Collaborative perception (CP) improves autonomous-driving perception by enabling connected vehicles to exchange intermediate features via V2X. Confidence-driven sparse communication reduces bandwidth by transmitting only perception-critical spatial regions, but creates a security risk: once a collaborator is compromised, malicious features in high-confidence or ego-uncertain regions may be preferentially selected and amplified during fusion. Using Where2comm as a representative framework, we show that the proposed Pretend Benign attack exploits its spatial-confidence mechanism by injecting stealthy perturbations into uncertain yet perception-critical regions, substantially degrading 3D object detection while preserving benign-like feature characteristics. Beyond this attack-framework pair, we identify a broader weakness of existing trust-based defenses: their reliance primarily on a single consistency signal leaves them vulnerable when multiple attackers form a pseudo-consensus that biases trust estimation. We therefore propose Global-Local Structural Trust (GLST), a lightweight defense that assesses collaborator reliability through three complementary perspectives: global feature consistency, multi-scale local residual consistency, and structural consistency with ego-side semantic topology. The resulting trust scores guide feature fusion to suppress unreliable collaborators. Experiments on OPV2V show that GLST achieves competitive performance against single-attacker Pretend Benign attacks and substantially stronger robustness in multi-attacker settings. Under a four-attacker Pretend Benign attack, GLST maintains 0.69 AP@0.3, whereas existing single-signal defenses degrade severely. GLST also remains effective against gradient-based attacks such as PGD, indicating that multi-level trust modeling is essential for securing confidence-driven CP.

cs.CR↗
arXiv2026

Traceable LLM Reasoning for Fake-Order Fraud Detection

Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provide limited interpretability. To address these limitations, we propose DeepScrub, a reinforcement learning framework built upon large language models (LLMs) for fake-order fraud detection with traceable reasoning. DeepScrub introduces three innovations. First, a semantic unification module converts heterogeneous risk signals into textual descriptions that LLMs can understand. Second, continued pre-training on risk-control corpora injects domain knowledge, and task rewards jointly evaluate prediction correctness and reasoning quality. Third, the SUggest-REflect (SURE) mechanism incorporates expert feedback and model self-checking to iteratively refine reasoning paths. On a real-world fake-order fraud detection dataset, DeepScrub achieves a macro-F1 score of 85.3%, outperforming the best baseline by 2.7 percentage points. Our task-optimized 8B model further surpasses a 32B model, showing that domain adaptation can matter more than model scale in this setting. In a four-week live pilot, DeepScrub achieved 91.8% precision and 88.5% recall, improving over first-stage human reviewers by 16.6 and 38.8 percentage points. It reduced first-stage manual review workload by 94% and saved nearly one million RMB annually. These results show that DeepScrub improves fraud review accuracy, reduces first-stage review workload, and provides traceable evidence for production risk-review workflows.

cs.CR↗
arXiv2026

Proof of Barry's Four Hankel Determinant Conjectures

Barry introduced a central transform of integer sequences and proposed four conjectures concerning the Hankel transforms of central transform of four rational families. We prove these four conjectures. The proofs are unified within a common algebraic framework: we interpret the Hankel determinants as Gram determinants and use a basis of shifted monic Chebyshev polynomials to reveal the finite-band structure of the associated Gram matrices.

math.CO↗