arXiv subjects

Di Zhu

Publications and source records attributed to Di Zhu.

At least 19 recordsLinked to original sources

arXiv2026

Endorsement Without New Evidence: How Sequential Voting Inflates Mandates in Online Community Governance

Online communities often treat large support margins in public elections as strong mandates. We argue that such margins can overstate the independent scrutiny behind a decision. Using 198,275 free-text rationales from Wikipedia admin elections, we introduce vote-text divergence, a measure that flags a decisive vote paired with a thin, deferential rationale. Divergence rises as voters arrive later, even after controlling for voter and election fixed effects. The pattern is consistent with information saturation: once prior text is accounted for, arrival order no longer predicts divergence, while accumulated prior evidence does. The effect is strongest among peripheral voters in the co-voting network. Yet divergence does not predict worse post-promotion outcomes, such as administrative activity or survival. Public tallies can therefore weaken the scrutiny signal even while selecting capable administrators: a margin may appear to reflect more consensus and support than it actually contains.

cs.SI↗

arXiv2026

Parity-selective spin splitting in coplanar antiferromagnets via bichromatic driving

Parity is a central characteristic of momentum-dependent spin splitting in antiferromagnets (AFMs). Yet, intrinsic crystal symmetries typically restrict the splitting to either even or odd parity, preventing flexible spin control. Using a coplanar AFM, we demonstrate that bichromatic ($\omega$--$n\omega$) Floquet driving offers a natural way to bypass this constraint. This mechanism generates asymmetric spin textures unattainable in static AFMs or under monochromatic driving. For the specific AFM considered here, we reveal an elegant relation between spin-splitting parity and harmonic hierarchy: $\omega$--$2\omega$ fields generate highly tunable odd- and mixed-parity spin splittings, whereas higher-order harmonics ($n \ge 3$) exclusively produce even-parity states. The macroscopic magnetization can also be toggled via the specific driving protocol and harmonic $n$. These distinct spin splitting states manifest in qualitatively different macroscopic spin currents generated after an optical quench---a definitive transport signature complementing direct visualization via spin- and angle-resolved photoemission spectroscopy.

cond-mat.mtrl-sci↗

arXiv2026

When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses

Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions. We ask when this substitution is valid and when it fails, and package the answer as an evaluation framework for intelligent synthetic-user systems. A single protocol, run across four models spanning two families and an 8B-to-frontier capability range, is applied to two independent domains of real human-response data: U.S. general social attitudes (General Social Survey) and cross-cultural values (World Values Survey). Every model is benchmarked against a suite of non-LLM baselines fit on held-out human data. Under demographic prompting and the survey-simulation protocols we test, two failures replicate across both domains, all four models, and both families. First, at the individual level no LLM beats even the strongest baseline; on cross-cultural values every model falls well below it, and the gap survives distance-aware and proper scoring. Second, models systematically over-determine demographics, treating identity as far more predictive of attitudes than it is among real people, a distortion present for nearly every question-group combination and robust to a coding-invariant measure. Neither failure is remedied by a larger, more capable model. A decision-impact analysis shows why this matters in practice: on a segment-targeting task the models inflate between-segment gaps two to fourfold, would direct a team to the wrong segment in half of U.S. and most cross-cultural cases, and manufacture segment splits that do not exist in real people. We make the cross-domain benchmark and the evaluation framework available on request, so that teams can determine in advance when synthetic-user evidence is safe for decision support and when it is not.

cs.CL↗

arXiv2026

Nonrelativistic Spin-Orbit-Coupling Effects in Odd-Parity Coplanar Magnets

Spin-orbit coupling (SOC) is a relativistic effect that underpins a broad spectrum of phenomena in condensed matter physics, from topological phases of matter to spintronic functionality. Its relativistic origin, however, restricts strong SOC to heavy-element materials and locks spin-momentum texture into a fixed, material-specific pattern. Here we show that odd-parity coplanar magnets offer a nonrelativistic pathway to highly tunable SOC effects. We construct a bilayer coplanar magnet via symmetry-guided stacking of two monolayer odd-parity altermagnets and demonstrate that Rashba, Weyl, and Dresselhaus spin textures can all be realized, and that the spin texture can be switched between these forms simply by tuning the layer Neel vector. Through the spin Edelstein effect and the realization of fully gapped chiral topological superconducting phases, we demonstrate that this nonrelativistic SOC achieves physical equivalence to its relativistic counterpart. Our findings identify a new class of odd-parity coplanar magnets as a versatile platform for engineering SOC effects.

cond-mat.mtrl-sci↗

arXiv2026

Segment-chirped periodically poled lithium niobate waveguides for broadband supercontinuum generation

Supercontinuum generation is a key technology in nonlinear optics, supporting a wide range of applications in frequency metrology and spectroscopy. Integrated photonics offers a promising route toward compact and efficient supercontinuum sources, yet extending the bandwidth while maintaining high spectral flatness remains a central challenge. Here we demonstrate an integrated broadband supercontinuum source based on segment-chirped periodically poled lithium niobate (SC-PPLN) nanophotonic waveguides. By discretizing the chirped poling profile into independently optimized segments, this approach enables high-fidelity ferroelectric domain inversion with near-ideal duty cycles and establishes broadband quasi-phase matching, overcoming the domain inhomogeneity and efficiency limitations commonly encountered in conventional chirped poling. The engineered phase-matching landscape supports efficient wavelength conversion and simultaneous activation of multiple second- and third-order nonlinear processes. Experimentally, we achieve a spectrally flat supercontinuum spanning three optical octaves, from 320 nm in the ultraviolet to 2600 nm in the mid-infrared. These results establish segment-chirped poling as a practical strategy for broadband wavelength conversion and supercontinuum generation in integrated photonics.

physics.optics↗

arXiv2026

VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data

Wearable devices enable continuous monitoring of physiological signals such as ECG and PPG, but existing mHealth systems are largely limited to task-specific prediction pipelines or reactive question answering over static summaries. They lack the ability to support temporal reasoning, persistent physiological context, and proactive monitoring over long-term signal streams. We propose VitalAgent, a tool-augmented agentic framework for ECG/PPG-based mHealth that supports both reactive question answering and proactive monitoring. VitalAgent is built on a longitudinal physiological memory and a tool-augmented reasoning interface that enables dynamic computation over raw signals. We further introduce VitalBench, a longitudinal physiological monitoring benchmark dataset comprising 1,862 QA pairs for reactive question answering and 90.2 hours of continuous ECG/PPG recordings for proactive monitoring, covering cardiac, physical activity, and stress-related tasks. Experiments demonstrate that VitalAgent achieves over 25% improvement over prompt-based and ReAct baselines in reactive evaluation and supports proactive alert monitoring over long-term physiological signals, highlighting the importance of dynamic tool use and long-term physiological monitoring.

cs.AI↗

arXiv2026

FundaPod: A Multi-Persona Agent Pod Platform with Knowledge Graph Memory for AI-Assisted Fundamental Investment Research

Large language models (LLMs) are increasingly applied in finance, yet most existing work emphasizes trading signals or financial NLP tasks centered on prediction. Institutional fundamental research, by contrast, requires human analysts or AI agents to gather evidence, identify business drivers, compare competing viewpoints, and generate investment memos. Its broader goal is not merely to predict outcomes, but to produce investment plans that are transparent, reusable, and verifiable, while contributing to the cumulative development of investment knowledge. We present FundaPod, a multi-persona agent platform for AI-assisted fundamental investment research. We argue that fundamental research is a human-centric decision-support task that is qualitatively distinct from trading-signal generation, and is therefore better served by an independence-preserving architecture. In FundaPod, AI agents with different personas, such as value investors or macro strategists, conduct research independently under a shared provenance contract. Their disagreements are then surfaced post hoc for adjudication by the human portfolio manager (PM) through a knowledge-graph memory system. This paper contributes five design principles for human-AI hybrid systems supporting fundamental research, grounded in design-science practice and theories of cognitive isolation and human-machine coordination. It also describes four architectural mechanisms: a persona distillation pipeline that turns public investor materials into deployable agents; a declarative skill registry that lets the planner derive typed task graphs; a grounded evidence model that links memo claims to verifiable sources; and a knowledge-graph "second brain" that connects tickers, memos, analysts, and themes. We demonstrate the architecture through a complete case study and a persona-based memo comparison.

cs.AI↗

arXiv2026

Integrated photon-pair sources on periodically poled thin-film lithium tantalate

Chip-integrated photon-pair sources based on spontaneous parametric down-conversion (SPDC) have emerged as a promising solution for scalable quantum light generation. Thin-film lithium tantalate (TFLT) is a compelling $\chi^{(2)}$ platform, combining strong nonlinearity with a high optical-damage threshold, weak photorefractive response, and ferroelectricity that enables quasi-phase matching. However, SPDC-based photon-pair generation on TFLT has not yet been demonstrated. Here, we combine high-quality periodic poling with low-loss nanophotonic waveguides to realize photon-pair sources on TFLT in both traveling-wave and resonant configurations. In periodically poled straight waveguides, we achieve broadband photon-pair generation with high efficiency ($2.1~\mathrm{GHz}~\mathrm{mW}^{-1}$) and coincidence-to-accidental ratio (up to $3.8\times10^{5}$). We further confirm high-purity single-photon operation via heralded second-order correlation ($g^{(2)}_\mathrm{H}(0) = 0.0018 \pm 0.0002$) and high-fidelity time-energy entanglement through Franson interference (visibility of $98.9 \pm 0.5\%$). In periodically poled racetrack resonators, we map out a broad quantum frequency comb spanning the telecom C- and L-bands. By isolating individual frequency-correlated pairs, we measure a high spectral brightness of $11~\mathrm{GHz}~\mathrm{mW}^{-1}~\mathrm{GHz}^{-1}$. These results are competitive with the state of the art across $\chi^{(2)}$ integrated platforms, positioning TFLT as a strong contender for integrated quantum light sources, with applications in wavelength-multiplexed quantum communications and photonic quantum information processing.

physics.optics↗

arXiv2026

Standalone LLM and a Pre-specified Agentic Pipeline for Explaining ICU Mortality Predictions: a Feasibility Study on the eICU Demo Dataset

Machine-learning models can predict ICU mortality accurately, but feature-attribution methods alone rarely provide the clinical narrative needed for bedside use. Large language models (LLMs) may bridge this gap, and multi-step agentic pipelines are a plausible extension because they separate data interpretation, guideline checking, and final explanation. This revised feasibility study preserves the original standalone-versus-agentic comparison while making the main clinical findings more explicit. Using the retained local eICU Demo artifact set (2,353 ICU stays; 8.1\% mortality), XGBoost achieved an AUROC of 0.855 (95\% CI 0.796--0.906) and an AUPRC of 0.332 (95\% CI 0.217--0.494). On a stratified 38-case explanation subset, the standalone LLM produced 1 explanation with explicit outcome leakage, whereas the four-step agentic pipeline produced none. Among the 14 cases that overlapped with the SHAP review subset, the standalone LLM showed higher SHAP alignment (mean Jaccard 0.171 versus 0.077) and higher direction consistency (92.9\% versus 78.6\%), while the agentic pipeline showed higher guideline grounding (0.762 versus 0.143), higher value specificity (0.236 versus 0.143), and slightly higher plausibility (0.700 versus 0.671). Clinically, the results suggest that agentic decomposition may improve safety-relevant grounding and patient-specific detail, but it should be paired with attribution-based checks before use in high-stakes risk explanation.

cs.AI↗

arXiv2026

Light-Induced Even-Wave Spin Splittings in Nonmagnetic Centrosymmetric Systems with Spin-Orbit Coupling

Spin splitting underpins a vast range of spin-dependent phenomena. Traditionally, two primary mechanisms generate such splitting: relativistic spin-orbit coupling (SOC) and nonrelativistic magnetic exchange coupling (MEC). Governed by distinct symmetry constraints, they produce splittings of opposite parity -- odd for SOC and even for MEC -- a dichotomy that underpins the distinct spin physics of nonmagnetic and magnetic systems. In this work, we break this dichotomy by demonstrating the dynamic generation of even-parity spin splitting in centrosymmetric, nonmagnetic systems driven by circularly polarized light. We show that the symmetry of the induced splitting is controlled by the angular character of the underlying orbitals, enabling the realization of s-wave, d-wave, and g-wave spin-split band structures identical to those of ferromagnets and altermagnets. Furthermore, we find that these spin-split bands can naturally host a Chern insulator phase. We also discuss the associated spin and orbital magnetization. Our results establish a direct and previously unrecognized conceptual link between the two fundamental mechanisms of spin splitting.

cond-mat.mtrl-sci↗

arXiv2026

MuQ-Eval: An Open-Source Per-Sample Quality Metric for AI Music Generation Evaluation

Distributional metrics such as Fr\'echet Audio Distance cannot score individual music clips and correlate poorly with human judgments, while the only per-sample learned metric achieving high human correlation is closed-source. We introduce MUQ-EVAL, an open-source per-sample quality metric for AIgenerated music built by training lightweight prediction heads on frozen MuQ-310M features using MusicEval, a dataset of generated clips from 31 text-to-music systems with expert quality ratings. Our simplest model, frozen features with attention pooling and a two-layer MLP, achieves system-level SRCC = 0.957 and utterance-level SRCC = 0.838 with human mean opinion scores. A systematic ablation over training objectives and adaptation strategies shows that no addition meaningfully improves the frozen baseline, indicating that frozen MuQ representations already capture quality-relevant information. Encoder choice is the dominant design factor, outweighing all architectural and training decisions. LoRA-adapted models trained on as few as 150 clips already achieve usable correlation, enabling personalized quality evaluators from individual listener annotations. A controlled degradation analysis reveals selective sensitivity to signal-level artifacts but insensitivity to musical-structural distortions. Our metric, MUQ-EVAL, is fully open-source, outperforms existing open per-sample metrics, and runs in real time on a single consumer GPU. Code, model weights, and evaluation scripts are available at https://github.com/dgtql/MuQ-Eval.

cs.AI↗

arXiv2026

Discovering Governing Spatial Interaction Mechanisms in Dynamic Urban Systems

Governing equations are fundamental for describing and predicting dynamic urban geographic systems. Unlike physical systems guided by first principles, urban spatiotemporal phenomena emerge from coupled geographic processes that lack deterministic theoretical foundations, making the discovery of governing equations elusive and largely heuristic. Spatiotemporal dynamics in urban systems are often observed as sequential snapshot data of spatial distribution, while the cause of such dynamics is often implied or unknown. In this study, we propose a unified differential equation formalism that decomposes urban dynamics into a time-invariant spatial interaction process and a self-dynamic component. Building on this formalism, we introduce the Urban Discovery Framework (U-Discovery), which integrates hypothesis generation, neural fitting, and governing equation identification for the discovery of governing spatial interaction laws. U-Discovery leverages Large Language Models and literature-based reasoning to propose differential equation candidates. Each candidate was calibrated from the observed spatiotemporal dynamics using a neural fitting method. The candidates are evaluated and ranked based on the fitting error and mathematical complexity. Our synthetic experiments prove that U-Discovery can find the sole governing equation from the simulated dynamics. Empirical experiments in Hennepin County, Minnesota, further demonstrate the potential of U-Discovery in identifying optimal governing laws from real-world human activity dynamics.

physics.soc-ph↗

arXiv2026

BMW: Bayesian Model-Assisted Adaptive Phase II Clinical Trial Design for Win Ratio Statistic

The win ratio (WR) statistic is increasingly used to evaluate treatment effects based on prioritized composite endpoints, yet existing Bayesian adaptive designs are not directly applicable because the WR is a summary statistic derived from pairwise comparisons and does not correspond to a unique data-generating mechanism. We propose a Bayesian model-assisted adaptive design for randomized phase II clinical trials based on the WR statistic, referred to as the BMW design. The proposed design uses the joint asymptotic distribution of WR test statistics across interim and final analyses to compute posterior probabilities without specifying the underlying outcome distribution. The BMW design allows flexible interim monitoring with early stopping for futility or superiority and is extended to jointly evaluate efficacy and toxicity using a graphical testing procedure that controls the family-wise error rate (FWER). Simulation studies demonstrate that the BMW design maintains valid type I error and FWER control, achieves power comparable to conventional methods, and substantially reduces expected sample size. An R Shiny application is provided to facilitate practical implementation.

stat.ME↗

arXiv2026

On-Chip Generation of Co-Polarized and Spectrally Separable Photon Pairs

On-chip generation of high-purity single photons is essential for scalable photonic quantum technologies. Spontaneous parametric down-conversion (SPDC) is widely used to generate photon pairs for heralded single-photon sources, but intrinsic spectral correlations of the pairs often limit the purity and interference visibility of the heralded photons. Existing approaches to suppress these correlations rely on narrowband spectral filtering, which introduces loss, or exploiting different polarizations, which complicates on-chip integration. Here, we demonstrate a new strategy for generating spectrally separable photon pairs in thin-film lithium niobate nanophotonic circuits by harnessing higher-order spatial modes, with all interacting fields residing in the same polarization. Spectral separability is achieved by engineering group-velocity matching using higher-order transverse-electric modes, combined with a Gaussian-apodized poling profile to further suppress residual correlations inherent to standard periodic poling. Subsequent on-chip mode conversion with efficiency exceeding 95\% maps the higher-order mode to the fundamental mode and routes the photons into distinct output channels. The resulting heralded photons exhibit spectral purities exceeding 94\% inferred from joint-spectral intensity and 89\% from unheralded $g^{(2)}$ measurement. This approach enables flexible spectral and temporal engineering of on-chip quantum light sources for quantum computing and quantum networking.

quant-ph↗

arXiv2026

Light-Induced Even-Parity Unidirectional Spin Splitting in Coplanar Antiferromagnets

When a coplanar antiferromagnet (AFM) with $xy$-plane magnetic moments exhibits a spin-split band structure and unidirectional spin polarization along $z$, the spin polarization is forced to be an odd function of momentum by the fundamental symmetry $[\bar{C}_{2z}\|\mathcal{T}]$. Coplanar AFMs displaying such odd-parity unidirectional spin splittings are known as odd-parity magnets. In this work, we propose the realization of their missing even-parity counterparts. We begin by deriving the symmetry conditions required for an even-parity, out-of-plane spin splitting. We then show that irradiating a spin-degenerate coplanar AFM with circularly polarized light lifts the $[\bar{C}_{2z}|\mathcal{T}]$ constraint, dynamically generating this even-parity state. Specifically, the light-induced unidirectional spin splitting exhibits a $d$-wave texture in momentum space, akin to that of a $d$-wave altermagnet. We prove this texture's robustness against spin canting and show it yields a unique clover-like angular dependence in the Drude spin conductivity. Our work demonstrates that optical driving can generate novel spin-split phases in coplanar AFMs, thereby diversifying the landscape of materials exhibiting distinct spin splittings.

cond-mat.mtrl-sci↗

arXiv2025

Evolutionary Physics-Informed Temporal Fusion for Lane-Change Intention Prediction

Early lane-change intention prediction is essential for autonomous driving and ADAS, but it remains challenging because lane-changing behavior depends on evolving traffic risk, surrounding-vehicle interactions, and target-lane feasibility rather than only instantaneous vehicle states. This study proposes an evolutionary physics-informed temporal fusion framework for three-class lane-change intention prediction, including left lane change, right lane change, and no lane change. Instead of using static physics-informed variables alone, the proposed method derives temporal descriptors from conventional traffic signals, including risk evolution, gap persistence, counterfactual lane utility, interaction pressure gradient, maneuver feasibility, and intent consistency. These descriptors are fused with temporal embeddings learned from raw trajectory sequences through a sequence encoder, and the fused representation is used for final classification. Experiments are conducted on the highD and exiD datasets under 1\,s, 2\,s, and 3\,s prediction horizons. The proposed model achieves Macro F1-scores of 0.9514, 0.9256, and 0.8872 on highD, and 0.9386, 0.9070, and 0.8531 on exiD, respectively. The improvement is especially pronounced in exiD ramp-adjacent scenarios, indicating that temporal physical evolution is particularly useful in interaction-rich environments. These results demonstrate that combining evolutionary physics-informed descriptors with learned temporal representations provides a more dynamic and interpretable solution for early lane-change intention prediction.

cs.LG↗

arXiv2025

Towards Practical and Usable In-network Classification

In-network machine learning enables real-time classification directly on network hardware, offering consistently low inference latency. However, current solutions are limited by strict hardware constraints, scarce on-device resources, and poor usability, making them impractical for ML developers and cloud operators. To this end, we propose ACORN, an end-to-end system that automates the distributed deployment of practical machine learning models across the network. ACORN provides a fully automated pipeline that loads and deploys Python ML models on network devices using an optimized deployment plan from an ILP planner. To support larger models under hardware constraints and allow runtime programmability, ACORN adopts a novel data plane representation for Decision Tree, Random Forest, and Support Vector Machine models. We implement ACORN prototype in P4 and run it on real programmable hardware. Our evaluation shows ACORN can deploy classification ML models with 2-4x more features than state-of-the-art solutions, while imposing negligible overhead on network performance and traffic. We will make our data plane program, model translator, optimizer, and all related scripts publicly available.

cs.NI↗

arXiv2025

Integrated polarization-entangled photon source for wavelength-multiplexed quantum networks

Entangled photons are fundamental resources for quantum communication, computing, and networking. Among them, polarization-entangled photon pairs play an important role due to their straightforward state manipulation and direct use in quantum key distribution, teleportation, and network protocols. However, realizing compact, efficient, and scalable polarization-entangled sources that meet the requirements of practical deployment remains a major challenge. Here, we present a simple yet high-performance on-chip polarization-entangled photon-pair source on thin-film lithium niobate (TFLN). Our device employs dual quasi-phase matching (D-QPM) that sequentially supports type-0 and type-I spontaneous parametric down-conversion in a single nanophotonic waveguide, eliminating the need for interferometers, polarization rotators, or other complex circuits. The source directly produces high-fidelity Bell states with broad bandwidth, high brightness, and low noise. Using this integrated platform, we realize wavelength-multiplexed entanglement distribution in a four-user quantum network deployed over metropolitan fiber links up to 50 km. These results establish a robust and scalable pathway toward practical quantum communication systems and multi-user quantum mesh networks based on integrated photonics.

physics.optics↗