arXiv subjects

Zhi Li

Publications and source records attributed to Zhi Li.

At least 55 records · Page 3Linked to original sources

arXiv2026

APCReg: Anatomical-Prior-Guided Coarse-to-Fine CBCT--IOS Registration via Multi-View Projection and Reliability-Controlled Residual Correction

Registration between cone-beam computed tomography (CBCT) and intraoral scans (IOS) is essential for patient-specific surgical planning. However, disparate imaging modalities, limited overlap, and large pose offsets make automated registration unreliable. Consequently, clinical registration remains dependent on conventional geometry pipelines and manual clinician adjustment. To address these challenges, we propose APCReg, an anatomical-prior-guided coarse-to-fine framework for global registration and reliability-controlled residual correction. Specifically, multi-view anatomical coarse registration (MACR) performs ordered orthogonal projection alignment (buccal, proximal, and occlusal) to decompose the six-degree-of-freedom search before three-dimensional refinement. Overlap-aware residual registration (OARR) combines shared KPConv features, a folded arch-length cue, overlap-gated cross-attention, and Sinkhorn matching. Finally, dental-arch-structured hypothesis selection evaluates diverse poses on held-out reliable correspondences, while a ground-truth-free coarse-retention guard conditionally retains a geometrically reliable coarse pose. On 60 held-out jaw pairs, APCReg achieves a submillimeter mean Chamfer distance of 0.87 mm and a Hausdorff distance of 2.92 mm under this evaluation protocol, and ranks first across the six reported metrics among the evaluated open-source baselines.

eess.IV↗

arXiv2026

Global vs. Product Observables in Bipartite Quantum Systems: The Sharp Bound

To probe a bipartite quantum system, one may use arbitrary global operators or restrict to product operators acting separately on the two subsystems. We determine the sharp universal comparison between the resulting norms. For every $z\in M_n\otimes M_m$, we prove $\|z\|_1\leq\sqrt{2}\min\{n,m\}\|z\|_\varepsilon$, where $\|\cdot\|_1$ is the trace norm and $\|\cdot\|_\varepsilon$ is the injective tensor norm associated with the trace norms on $M_n$ and $M_m$. To prove the upper bound, we establish an $L_1$ noncommutative Khintchine inequality whose random coefficients are the entries of a Haar unitary. We also show that the coefficient $\sqrt{2}$ is sharp. As applications, we show that the same sharp constant governs the gap between bipartite correlation measured in trace norm and that measured by a correlation function, and obtain an improved universal upper bound for quantum data hiding. The upper bound has also been formalized and machine-checked in Lean.

quant-ph↗

arXiv2026

On Optimal Quantum Data Hiding and Maximal Separable Ball

Quantum data hiding asks how much distinguishing power can be lost when global measurements are restricted to local measurements and classical communication. In this work, we establish sharp results and improved bounds for several natural classes of restricted measurements. For bipartite systems on $\mathbb C^n\otimes\mathbb C^m$, we prove that the optimal data-hiding ratios against separable and LOCC measurements are both $\min\{n,m\}$. This result follows from a stronger result that, for every $2\le p\le\infty$, the largest centered Schatten $p$-ball whose associated binary measurements are implementable by finite-round LOCC has radius $\min\{n,m\}^{2/p-1}$. This strengthens the classic separable-ball theorems, while also providing an explicit finite-round LOCC implementation. For Alice-first one-way LOCC with Alice's local dimension equal to $n$, we prove that the optimal ratio is $(1+o(1))n$, with the upper bound obtained from a Gaussian rank-one POVM. For local operations without communication, we improve the universal upper bound to $(π\sqrt3/4+o(1))\min\{n,m\}$.

quant-ph↗

arXiv2026

BulkPR-Bench: Benchmarking Queue-Level Governance of Interacting Pull Requests

Coding-agent benchmarks increasingly cover long-horizon, end-to-end, and interactive development, but typically retain one requested outcome or a fixed change sequence. Sequential policies can process a pull-request (PR) queue one candidate at a time, but when queued PRs interact, maximizing safe delivery can require jointly deciding which changes to merge and in what order. We introduce BulkPR-Bench, an executable benchmark in which an agent must recover consequential PR relations and return a large safe subset in executable order under a rolling-release protocol. The suite contains 581 newly authored candidate PRs on frozen snapshots of 18 real repositories. Registered state-by-state repository execution, including hidden safety checks, validates the gold relation graph; an exact oracle then computes the largest safe subset. Our primary metric, Relational Delivery Score (RDS), scores safe delivery and correct rejection over relation groups from the realized merge trace; Global Safety-Gated Yield (Global-SGY) separately measures strict delivery of the realized whole-queue plan. Under the buffered primary protocol with batch size $K=32$, the three highest RDS estimates among the six models are 66.6%, 62.0%, and 57.9%, compared with 53.1% for the strongest sequential baseline. Only 8 of 324 model runs complete a queue exactly. Critical-relation recall ranges from 35.2% to 57.7%, and diagnostic runs supplied with the gold relations show substantial remaining headroom. Gains on relation groups therefore do not yet translate into dependable whole-queue governance.

cs.SE↗

arXiv2026

Day-Ahead Forecasting of Largest Single Infeed/Outfeed on the Irish Power Grid: A Generative Artificial Intelligence Approach

This paper presents a generative artificial intelligence (Gen AI) approach for forecasting, at a day-ahead stage, the largest single infeed (LSI) and largest single outfeed (LSO) on the Irish power system to assist in reserve dimensioning. Developed collaboratively between EirGrid, the electric transmission system operator (TSO) for Ireland, and GridZero.ai using the GridZero.ai platform, the system delivers accurate forecasts up to 38 hours ahead of real-time using limited data available before the day-ahead and intra-day energy market gate closure timings. Initial performance demonstrates an accuracy with a mean absolute percentage error (MAPE) that is only 1.1\% higher than the results possible using full market data (8-hours ahead). Thus, if this approach is integrated into operational systems and such high levels of accuracy are maintained, reserve procurement costs could be significantly reduced. The results also demonstrate the practicality and extensibility of AI-powered resource planning for TSOs.

eess.SY↗

arXiv2026

Inunda: A GPU-Native, Agent-enabled, Differentiable Solver for High-Resolution Flood Inundation Modeling

Predicting where floodwater goes and how deep it gets, at high resolution and across large domains, remains computationally expensive with conventional hydraulic solvers, while purely data-driven surrogates are fast but lack physical guarantees and generalize poorly beyond their training events. We present Inunda, a GPU-native flood inundation model that solves the two-dimensional shallow water equations. Inunda uses a mass-conservative local-inertial scheme and runs multi-day events over millions of cells in minutes on a single GPU. Because every operator is autograd-compatible, the solver is differentiable by construction: model parameters can be estimated by gradient descent against gage observations through reverse-mode automatic differentiation of the full simulation. We demonstrate Inunda on three case studies. For a hindcast of Hurricane Harvey (2017) in Harris County, Texas, Inunda matches surveyed high-water marks to a mean absolute error of 0.67 m, competitive with or better than a suite of established flood models, and reaches a median gage water-level Nash Sutcliffe efficiency of +0.72, more than double the +0.31 of the operational National Water Model v3.0. For the July 2025 Central Texas flash flood, Inunda is driven by an 18-member 1-km convection-allowing precipitation ensemble to produce probabilistic flood forecasts whose skill improves systematically as lead time to the crest shortens. For a post-fire flash-flood application in the Rio Ruidoso burn scar, differentiable calibration recovers the saturated hydraulic conductivity as a spatially explicit field at the model's own resolution and traces its multi-year post-fire recovery. Inunda provides an open, end-to-end pipeline for real-event flood modeling that couples the accuracy of physics-based hydraulics with the calibration and coupling advantages of modern differentiable programming.

physics.geo-ph↗

arXiv2026

Low-luminosity Wolf-Rayet stars: a model-data comparison

A growing number of Galactic Wolf-Rayet (WR) stars, in particular WC and transitional WN/C (WNC) objects, have been reported at comparatively low luminosities. If confirmed, these low-luminosity WR stars provide stringent tests of stellar-evolution models, because their HR-diagram locations and surface compositions are highly sensitive to internal mixing and to the adopted WR-phase mass-loss history.We examine whether the HR-diagram positions and wind properties of low-luminosity WC/WNC stars can be reproduced by single-star evolutionary tracks at approximately solar metallicity, and we identify cases where additional channels (e.g. binary stripping) or dominant systematic uncertainties are likely required. Low-luminosity WNC/WC stars offer sensitive leverage on WR mixing and mass-loss prescriptions. A staged model-data comparison shows that revised WR winds can alleviate the luminosity-side tension for faint WCL stars, but the simultaneous requirements of temperature, surface composition, and WR-like wind density remain important. The WNC stars provide the strongest evidence that additional mixing, stripping, or binary-related channels may be required.

astro-ph.SR↗

arXiv2026

Tracing the kinematic perturbations of the Milky Way spiral arms with APOGEE DR17 and Gaia DR3

Aims. We constrain the dynamical perturbations of the spiral arms in the Milky Way disk, based on the non-axisymmetric streaming motions of RGB stars revealed by APOGEE and \textit{Gaia}. Methods. We develop a revised steady-state radial-velocity response model that incorporates both the $V_{R,\sin}$ and the dynamically important $V_{R,\cos}$ components for a two-armed logarithmic spiral potential. The model is validated using orbit integrations with \texttt{AGAMA} and Bayesian parameter recovery with \texttt{dynesty}, and is applied to the smoothed two-dimensional radial-velocity field of RGB stars while accounting for Lindblad and corotation resonances. Results. The revised model reproduces the phase and amplitude of the mock radial-velocity field to the $\sim2\%$ level, substantially improving upon earlier $V_{R,\sin}$-only formulations. Applied to the observational data, it yields a robust pitch angle of $p \simeq 10^\circ$ and a local surface density contrast of $ξ\simeq 5$--$18\%$ at the solar radius. The radial scale length is less well-constrained ($h_{R,1} \simeq 40$--$50\,\mathrm{kpc}$) due to intrinsic parameter covariance. Resonance effects strongly shape the velocity field, thus affecting the fitting: the radial velocity becomes extremely large near the Lindblad resonances, whereas it vanishes close to the corotation resonance. Conclusions. Our results demonstrate that including both the $V_{R,\sin}$ and $V_{R,\cos}$ terms is essential for a physically consistent interpretation of stellar streaming motions induced by a spiral potential. The observed kinematics constrain the spiral pattern speed to $Ω_{p} \approx 10$--$20\,\mathrm{km\,s}^{-1}\mathrm{kpc}^{-1}$.

astro-ph.GA↗

arXiv2026

Many-body chirality of topological stabilizer states

A defining feature of chirality is the distinction between a system and its mirror image. Despite extensive experimental observations of chiral phases and theoretical advances, a quantum-information theoretic characterization of chirality based solely on the entanglement structure of many-body quantum states remains elusive. Here, we introduce the notion of many-body chirality by formulating it as an obstruction to transforming a quantum state into its complex conjugate through finite-depth local operations. We rigorously establish many-body chirality for stabilizer realizations of $\mathbb{Z}_d^{(k)}$ anyon theories, proving that complex conjugation can be implemented by local quantum channels if and only if the underlying anyon data are mirror invariant. This reveals forms of chirality that evade conventional diagnostics, including examples with vanishing modular commutator, vanishing chiral central charge, and commuting-projector realizations. We further show that this obstruction is intrinsically four-partite, while invisible to tripartite entanglement structure. Finally, we prove that $\mathbb{Z}_d^{(k)}$ states with $d>2$ possess intrinsic many-body imaginarity: their complex phase structure cannot be removed by finite-depth local unitaries. Remarkably, this includes states that are not many-body chiral.

quant-ph↗

arXiv2026

PolySpeech-100: A Large-Scale Benchmark for Speech Understanding Across 100+ Languages and Dialects

While End-to-End (E2E) Speech-Large Language Models (Speech-LLMs) are rapidly evolving, their evaluation methodologies remain limited to the era of simple transcription. Existing benchmarks suffer from three critical limitations: a pronounced bias towards high-resource languages, a focus on low-level recognition (ASR) rather than semantic reasoning, and a neglect of regional dialects. To bridge this gap, we introduce PolySpeech-100, a massive-scale benchmark designed to assess `native-level' speech comprehension across 110 linguistic variants. We employ a novel hybrid construction pipeline that augments gold-standard human recordings with instruction-driven synthetic speech, allowing us to cover 19 distinct Chinese dialects and over 80 low-resource languages. Extensive evaluation of 22 state-of-the-art models (including Gemini-3, GPT-Audio, and Qwen2.5-Omni) yields pivotal insights. First, we demonstrate that open-source E2E models outperform Cascade (ASR+LLM) systems on heavy dialects, proving that direct audio processing preserves critical paralinguistic cues and prosodic features (e.g., intonation, stress) that are often lost in standard transcription. Second, we reveal a significant performance gap: while commercial models maintain robustness, open-source models suffer catastrophic degradation on low-resource languages. Finally, counter-intuitively, we observe that under standard zero-shot settings, Chain-of-Thought prompting frequently degrades speech understanding performance for most evaluated models, revealing a potential modality alignment gap in current architectures. PolySpeech-100 establishes a rigorous standard for the next generation of inclusive, omni-capable Speech-LLMs. The data, demo, and code are publicly available at https://github.com/YoungSeng/PolySpeech-100.

cs.CL↗

arXiv2026

A hitchhiker's guide to Poisson gradient estimation

Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two approaches address this: *Exponential Arrival Time* (EAT) simulation and *Gumbel-SoftMax* (GSM) relaxation. We provide the first systematic comparison of these methods, along with practical guidance for practitioners. Our main technical contribution is a modification to the EAT method that theoretically guarantees an unbiased first moment (exactly matching the firing rate), and reduces second-moment bias. We evaluate these methods on their distributional fidelity, gradient quality, and performance on two tasks: (1) variational autoencoders with Poisson latents, and (2) partially observable generalized linear models, where latent neural connectivity must be inferred from observed spike trains. Across all metrics, our modified EAT method exhibits better overall performance (often comparable to exact gradients), and substantially higher robustness to hyperparameter choices. These results extend to over-dispersed Negative Binomial latents, where modified EAT again performs best. However, only GSM generalizes to arbitrary non-Poisson distributions, including the under-dispersed regime. Together, our results clarify the trade-offs between these methods and offer concrete recommendations for practitioners working with Poisson latent variable models.

stat.ML↗

arXiv2026

Fisher-Preserving Guidance: Training-Free Manifold Constraints for Safe Diffusion Control

Diffusion models are effective for waypoint prediction in visual navigation, but standard sampling and test time guidance can produce unreliable or inefficient trajectories when updates drift off the training manifold. We propose Fisher Preserving Guidance with Outer Product Span Projection, a training-free inference method that avoids large Fisher drift associated with off-distribution actions while optimizing a task objective. Our method computes the Fisher-preserving update via a low-rank Jacobian factorization, requiring only a single backward pass per step and enabling real-time use. We further introduce Truncated Fisher Denoising Sensitivity as an uncertainty signal and use it for robust multi-sample action blending. Experiments on toy and realistic navigation benchmarks, including Maze2D with TSDF-based guidance, PushT with official Diffusion Policy weights, and visual navigation in simulation and on real robots, demonstrate consistent improvements in performance over strong diffusion-policy baselines without additional training.

cs.RO↗

arXiv2026

Periodic orbits and gravitational waveforms around a Schwarzschild black hole with a cloud of strings embedded in perfect fluid dark matter

In this study, we explore the dynamics of particle orbits and their corresponding gravitational wave signatures in the vicinity of a Schwarzschild black hole (BH) surrounded by a cloud of strings and embedded in a perfect fluid dark matter medium. The model is characterized by two parameters: $a$, associated with the string cloud, and $α$, representing the dark matter distribution. We systematically analyze how the marginally bound orbit (MBO) and the innermost stable circular orbit (ISCO) depend on these parameters. Our findings reveal that while both the orbital radius and angular momentum increase with increasing $a$, they decrease as $α$ increases; notably, the energy exhibits the opposite trend, decreasing with $a$ and increasing with $α$. Furthermore, we examine periodic orbits indexed by rational numbers $q$ and the gravitational waveforms they generate. The results demonstrate that an increase in the string cloud parameter $a$ induces a significant phase delay in the waveform. Specifically, waveforms with lower values of $a$ oscillate over shorter time intervals, whereas those with higher values extend to longer time scales. These distinct features, including noticeable differences in amplitude, allow the waveforms to be clearly distinguished from those in a pure Schwarzschild spacetime.

gr-qc↗

arXiv2026

Nonlinear Photonic Tripartite Phase

Anderson localization is usually understood as a transition between extended and localized phases, with criticality confined to a single mobility edge. Recent advances predict that quasiperiodic systems can instead host a finite critical window bounded by mobility edges, in which localized, critical and extended states coexist. Yet both the experimental realization of this regime and whether interactions can provide controlled access to it remain unknown. Here, we realize such a tripartite phase in a nonlinear quasiperiodic photonic lattice and show that Kerr nonlinearity, acting as an effective interaction, enables state-selective access to the critical window. By tracking wavepacket dynamics, we distinguish localized, critical and extended transport regimes and uncover a state-selective response: rather than simply reinforcing localization through self-trapping, weak nonlinearity drives low-energy localized states into the critical window, whereas stronger nonlinearity restores localization. By contrast, critical, extended and high-energy localized states evolve monotonically towards self-trapped behaviour. Our results reveal a state-selective mechanism by which interactions provide controlled access to a pre-existing critical window in quasiperiodic systems.

cond-mat.mes-hall↗

arXiv2026

Tripartite Haar random state has no bipartite entanglement

We show that no EPR-like bipartite entanglement can be distilled from a tripartite Haar random state $|Ψ\rangle_{ABC}$ by local unitaries or local operations when each subsystem $A$, $B$, or $C$ has fewer than half of the total qubits. Specifically, we derive an upper bound on the probability of sampling a state with EPR-like entanglement at a given EPR fidelity tolerance, showing a doubly-exponential suppression in the number of qubits. Our proof relies on a simple volume argument supplemented by an $ε$-net argument and concentration of measure. Viewing $|Ψ\rangle_{ABC}$ as a bipartite quantum error-correcting code $C\to AB$, this implies that neither output subsystem $A$ nor $B$ supports any non-trivial logical operator. We also establish general constraints on the structure of tripartite entanglement in Haar random states, showing that W- or GHZ-like entanglement cannot be distilled and that nontrivial global symmetries are absent. Finally, we discuss a physical interpretation in the AdS/CFT correspondence, indicating that a connected entanglement wedge does not necessarily imply bipartite entanglement, contrary to a previous belief.

quant-ph↗

arXiv2026

HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL

Calibrating distributed hydrologic models is a critical bottleneck across operational water resources management - streamflow prediction, reservoir operation, drought monitoring, infrastructure design, and flood forecasting all depend on it. Each basin demands an expert to translate hydrograph signatures into adjustments of a high-dimensional parameter vector, and the resulting workflow does not transfer between watersheds. We ask: can frontier large language model (LLM) agents replace the human hydrologic modeler, and if not, what would it take? We benchmark nine frontier LLM agents - Claude Opus 4.6/4.7, Sonnet 4.6, GPT-5/5.4/5.4-pro, and Gemini 2.5-pro/3.1-pro/3-flash - on the operational CREST distributed hydrologic model used by the U.S. National Weather Service for flash-flood forecasting. Best-of-twenty-rounds Nash-Sutcliffe Efficiency (NSE) across four held-out gauges spanning 329-40,792 km2 ranges from -0.16 (GPT-5.4) to 0.75 (Sonnet 4.6); the ceiling reproduces across all three vendors and capability tiers, with the strongest models concentrating in the 0.65-0.75 band, and no model reaches the human-expert reference except Opus-4.7 on one gauge. We argue this gap is not a parameter-count problem but a domain-grounding problem. We then propose HYDROAGENT, fine-tuning open-weight Qwen3-4B with supervised fine-tuning on 2,576 expert calibration trajectories and Group-Relative Policy Optimization using NSE as a verifiable reward from online CREST simulations - reinforcement learning with simulation feedback (RLSF). For Earth system science, a small domain-tuned policy with simulator-in-the-loop RL is a more compute-efficient and physically faithful path than scaling generic frontier models, and the multi-modal richness of Earth data - remote sensing, in-situ time series, and forecaster narrative - makes domain agents a leveraged direction for AI in physical science.

cs.LG↗

arXiv2026

Terahertz detection within charge density wave state

Terahertz (THz) technology enables multi-Tbps satellite communications, but conventional semiconductor detectors suffer from fundamental performance degradation above 1 THz due to the Drude limit of free electrons. Here, we theoretically demonstrate that charge density wave (CDW) materials offer a paradigm-shifting solution via their collective electronic response. We show that a static bias electric field can continuously tune the THz resonant absorption frequency of CDW states from 0 to cutoff frequency, and enhance the nonlinear rectification current by more than one order of magnitude. This unprecedented electric-field tunability makes CDW materials ideal candidates for next-generation ultrafast THz detectors working at room-temperature.

cond-mat.mes-hall↗

arXiv2026

Dynamically Characterizing the Structures of Dirac Points via Wave Packets

Topological non-trivial band structures are the core problem in the field of topological materials. In this paper, we investigate the topological band structure in a system with controllable Dirac points from the perspective of wave packet dynamics. By adding a third-nearest-neighboring coupling to the graphene model, additional pairs of Dirac points emerge. The emergence and annihilation of Dirac points result in hybrid and parabolic points, and we show that these band structures can be revealed by the dynamical behaviors of wave packets. Particularly, for the gapped hybrid point, the motion of the wave packet shows a one-dimensional \emph{Zitterbewegung} motion. Furthermore, we also show that the winding number associated with the Dirac point and parabolic point can be determined via the center-of-mass and spin texture of wave packets, respectively. The results of this work could motivate new experimental methods to characterize the system's topological signatures through wave packet dynamics, which may also find application in systems of other exotic topological materials.

cond-mat.mes-hall↗