arXiv subjects

Di Wu

Publications and source records attributed to Di Wu.

At least 127 records · Page 7Linked to original sources

arXiv2026

Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions

Bayesian Optimal Experimental Design (BOED) provides a rigorous framework for decision-making tasks in which data acquisition is often the critical bottleneck, especially in resource-constrained settings. Traditionally, BOED typically selects designs by maximizing expected information gain (EIG), commonly defined through the Kullback-Leibler (KL) divergence. However, classical evaluation of EIG often involves challenging nested expectations, and even advanced variational methods leave the underlying log-density-ratio objective unchanged. As a result, support mismatch, tail underestimation, and rare-event sensitivity remain intrinsic concerns for KL-based BOED. To address these fundamental bottlenecks, we introduce an IPM-based BOED framework that replaces density-based divergences with integral probability metrics (IPMs), including the Wasserstein distance, Maximum Mean Discrepancy, and Energy Distance, resulting in a highly flexible plug-and-play BOED framework. We establish theoretical guarantees showing that IPM-based utilities provide stronger geometry-aware stability under surrogate-model error and prior misspecification than classical EIG-based utilities. We also validate the proposed framework empirically, demonstrating that IPM-based designs yield highly concentrated credible sets. Furthermore, by extending the same sample-based BOED template in a plug-and-play manner to geometry-aware discrepancies beyond the IPM class, illustrated by a neural optimal transport estimator, we achieve accurate optimal designs in high-dimensional settings where conventional nested Monte Carlo estimators and advanced variational methods fail.

stat.ML↗

arXiv2026

Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference

Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing instruments issued across multi-chain Web3 infrastructures, with growing significance for transparency, accessibility, and financial inclusion. While the market has expanded rapidly, empirical analyses of transaction-level behaviours remain limited. This paper conducts a quantitative, function-level dissection of U.S. Treasury-backed RWA tokens, including BUIDL, BENJI, and USDY across multi-chain: mostly Ethereum and Layer-2s. Decoded contract calls expose core financial primitives such as issuance, redemption, transfer, and bridging, revealing patterns that distinguish institutional participants from smaller or retail users for the extent and limits of inclusivity in current RWA adoption. To infer address-level economic roles, we introduce a curvature-aware representation learning model. Our method outperforms baseline models in role inference on our collected U.S. Treasury transaction dataset and generalizes to address classification across broader public blockchain transaction datasets. The decoded transaction-level patterns in tokenized U.S. Treasuries across chains surface the degree of retail participation, and the role inference model enables the distinction between institutional treasuries, arbitrage bots, and retail traders based on behavioral patterns, facilitating future more transparent, inclusive, and accountable Web3 finance.

q-fin.CP↗

arXiv2026

Building Low-Altitude Communication Networks: A Digital Twin-Based Optimization Framework

Low-altitude communication networks (LACNs) serve as the critical infrastructure of the emerging low-altitude economy (LAE), supporting services such as drone delivery and infrastructure inspection. However, LACNs operate in highly dynamic three-dimensional (3D) environments characterized by high mobility and predominantly line-of-sight (LoS) propagation, creating strong coupling among key performance objectives including coverage, interference mitigation, handover management, and sensing capability. Isolated tuning of individual objectives cannot capture these cross-objective interactions, rendering conventional approaches based on experience-driven tuning and repeated field trials inefficient and costly. To address these challenges, we propose DT-MOO, a Digital Twin-based Multi-Objective Optimization framework for LACNs. By constructing a high-fidelity virtual replica that integrates realistic environmental models, electromagnetic (EM) propagation, and traffic dynamics within a unified environment, DT-MOO enables joint evaluation and systematic optimization of interdependent network parameters, scoring candidate configurations by their combined effect on multiple objectives. As the foundational validation of the framework, we report real-world experiments in a 5G-enabled LACN focusing on coverage-interference co-optimization, where DT-MOO increases the high-quality coverage rate from 14.0% to 52.9% across all evaluated altitudes compared to an operator-provisioned, experience-based baseline, while achieving a net SINR gain under stringent criteria despite local spatial trade-offs, confirming its ability to handle coupled objectives in practical LACN deployment.

eess.SP↗

arXiv2026

Macroscopic Optical Nonreciprocity: A Black Hole as an Optical Diode

Optical reciprocity--the principle that light retraces the same path when source and detector are interchanged--is a foundational concept in geometric optics. In this Letter, we demonstrate that this ``symmetry-protected'' behavior can be qualitatively overturned in a rotating black hole when spontaneous Lorentz symmetry breaking introduces a nonminimally coupled background structure with a preferred direction. Through numerical ray-tracing simulations, we reveal a striking macroscopic signature: upon optical-path reversal achieved by exchanging the source and the observer, the shadow of the same black hole morphs from a quasi-symmetric rugby-ball shape into a distinct teardrop profile. This high-contrast nonreciprocity effectively turns the black hole into a cosmic-scale optical diode, offering a novel pathway to probe fundamental symmetries using current and next-generation horizon-scale imaging.

gr-qc↗

arXiv2026

Randomized Subsystem Descent for Fermion-to-Qubit Mapping

We propose a versatile and efficient algorithmic framework for optimizing fermion-to-qubit mappings by generalizing the idea of randomized block coordinate descent. Our greedy approach, termed Randomized Subsystem Descent, iteratively samples a tractable subsystem from the full Hamiltonian, performs optimization within the subsystem under a given metric, and then reintegrates the updated subsystem into the global operator. Restricting the optimization to a subsystem at each iteration ensures computational efficiency, bypassing the dimensional bottlenecks that usually hinder global search heuristics. We benchmark our algorithm on one- and two-dimensional lattice hopping models, the Hubbard model with up to $16 \times 16$ sites, alongside a collection of molecular electronic-structure Hamiltonians with up to 54 modes and more than 180,000 Pauli strings. Across all benchmarks, our method consistently provides appreciable reduction in (weighted) Pauli weight, suggesting that Randomized Subsystem Descent is a practical and scalable framework for lowering the resource overhead of finding hardware-efficient Hamiltonian encodings.

quant-ph↗

arXiv2026

You May Use the Same Channel Knowledge Map for Environment-Aware NLoS Sensing and Communication

As one of the key usage scenarios for the sixth generation (6G) wireless networks, integrated sensing and communication (ISAC) provides an efficient framework to achieve simultaneous wireless sensing and communication. However, traditional wireless sensing techniques mainly rely on the line-of-sight (LoS) assumptions, i.e., the sensing targets are directly visible to both the sensing transmitter and receiver. This hinders ISAC systems to be applied in complex environments such as the urban low-altitude airspace, which usually suffers from signal blockage and non-line-of-sight (NLoS) multi-path propagation. To address this challenge, in this paper, we propose a novel approach to enable environment-aware NLoS ISAC by leveraging the new technique called channel knowledge map (CKM), which was originally proposed for environment-aware wireless communications. One major novelty of our proposed method is that the same CKM built for wireless communication can be directly used to enable NLoS wireless sensing, thus enjoying the benefits of ``killing two birds with one stone''. To this end, the sensing targets are treated as virtual user equipment (UE), and the wireless communication channel priors are transformed into the sensing channel priors, allowing one single CKM to serve dual purposes. We illustrate our proposed framework by a specific CKM called \emph{channel angle-delay map} (CADM). Specifically, the proposed framework utilizes CADM to derive angle-delay priors of the sensing channel by exploiting the relationship between communication and sensing angle-delay distributions, enabling sensing target localization in the challenging NLoS environment. Extensive simulation results demonstrate significant performance improvements over classic geometry-based sensing methods, which is further validated by Cramér-Rao Lower Bound (CRLB) analysis.

cs.IT↗

arXiv2026

Channel Knowledge Map-Enabled NLoS ISAC Localization

Accurate localization in non-line-of-sight (NLoS) environments remains challenging even with both angle-of-arrival (AoA) and time-of-arrival (ToA) measurements. In complex urban scenarios, the absence of line-of-sight (LoS) paths and the lack of environment prior knowledge make geometric based localization methods inapplicable, while prior-based approach such as fingerprinting is sensitive to environmental perturbations. This paper proposes a novel environment-aware localization framework enabled by the emerging concept called channel knowledge map (CKM). In the offline stage, AoA-ToA path signatures are learned by the CKM, with each path mapped to one candidate scatterer, thereby forming geometric priors within the environment. In the online stage, observed paths are matched to the CKM to extract high-confidence scatterers. Nonlinear least squares (NLS) method is then applied to jointly estimate the user and dominant scatterer locations. Even with imperfect CSI matching, geometric feasibility consistent with CKM scatterer priors provides corrective information and suppresses ambiguity. Simulations demonstrate that the proposed scheme outperforms fingerprinting and offers a robust and scalable solution to address the challenging NLoS localization for integrated sensing and communication (ISAC) systems.

eess.SP↗

arXiv2026

Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge

Large language models (LLMs) are increasingly used as automated evaluators (LLM-as-a-Judge). This work challenges its reliability by showing that trust judgments by LLMs are biased by disclosed source labels. Using a counterfactual design, we find that both humans and LLM judges assign higher trust to information labeled as human-authored than to the same content labeled as AI-generated. Eye-tracking data reveal that humans rely heavily on source labels as heuristic cues for judgments. We analyze LLM internal states during judgment. Across label conditions, models allocate denser attention to the label region than the content region, and this label dominance is stronger under Human labels than AI labels, consistent with the human gaze patterns. Besides, decision uncertainty measured by logits is higher under AI labels than Human labels. These results indicate that the source label is a salient heuristic cue for both humans and LLMs. It raises validity concerns for label-sensitive LLM-as-a-Judge evaluation, and we cautiously raise that aligning models with human preferences may propagate human heuristic reliance into models, motivating debiased evaluation and alignment.

cs.AI↗

arXiv2026

Borderless Long Speech Synthesis

Most existing text-to-speech (TTS) systems either synthesize speech sentence by sentence and stitch the results together, or drive synthesis from plain-text dialogues alone. Both approaches leave models with little understanding of global context or paralinguistic cues, making it hard to capture real-world phenomena such as multi-speaker interactions (interruptions, overlapping speech), evolving emotional arcs, and varied acoustic environments. We introduce the Borderless Long Speech Synthesis framework for agent-centric, borderless long audio synthesis. Rather than targeting a single narrow task, the system is designed as a unified capability set spanning VoiceDesigner, multi-speaker synthesis, Instruct TTS, and long-form text synthesis. On the data side, we propose a "Labeling over filtering/cleaning" strategy and design a top-down, multi-level annotation schema we call Global-Sentence-Token. On the model side, we adopt a backbone with a continuous tokenizer and add Chain-of-Thought (CoT) reasoning together with Dimension Dropout, both of which markedly improve instruction following under complex conditions. We further show that the system is Native Agentic by design: the hierarchical annotation doubles as a Structured Semantic Interface between the LLM Agent and the synthesis engine, creating a layered control protocol stack that spans from scene semantics down to phonetic detail. Text thereby becomes an information-complete, wide-band control channel, enabling a front-end LLM to convert inputs of any modality into structured generation commands, extending the paradigm from Text2Speech to borderless long speech synthesis.

cs.SD↗

arXiv2026

CURLING -- III. Identifying Candidates of Wide-separation Gravitationally Lensed Quasars from the CatNorth Catalogue

Wide-separation lensed quasars (WSLQs) are a rare subclass of strongly lensed quasars produced by massive galaxy clusters. They provide valuable probes of dark-matter halos and quasar host galaxies. However, only about ten WSLQ systems are currently known, which limits further studies. To enlarge the sample from wide-area surveys, we developed a catalog-based pipeline and applied it to the CatNorth database, a catalog of quasar candidates constructed from Gaia DR3. CatNorth contains 1,545,514 quasar candidates with about 90% purity and a Gaia G-band limiting magnitude of roughly 21. The pipeline has three stages. First, we identify groups with separations between 10 and 72 arcsec using a HEALPix grid with 25.6 arcsec spacing and a friends-of-friends search. We then filter by intra-group color and spectral similarity, reducing the 1,545,514 sources to 14,244 groups while retaining all known, discoverable WSLQs. Finally, a visual check, guided by image geometry and the presence of likely foreground lenses, yields the candidate list with quality labels. We identify 333 new WSLQ candidates with separations from 10 to 56.8 arcsec. Using available SDSS DR16 and DESI DR1 spectroscopy, we uncover two new candidate systems; the remaining 331 candidates lack sufficient spectra and are labeled as 45 grade A, 98 grade B, and 188 grade C. We also compile 29 confirmed dual quasars as a by-product. When feasible, we plan follow-up spectroscopy and deeper imaging to confirm WSLQs among these candidates and enable the related science.

astro-ph.GA↗

arXiv2026

Energy Correlators from Star Integrals via Mellin Space

We explore the Mellin space representation for the collinear limit of $N$-point energy correlators in ${\cal N}=4$ super-Yang-Mills theory. We show that these correlators can be written as integro-differential operators acting on star integrals: one-loop $n$-gons in $n$ dimensions. For the three-point energy correlator, we obtain the Mellin representation, use it to relate the correlator to the massive box integral, and show how to solve this relation to match with the expected result. For the four-point energy correlator, we obtain the Mellin representation and use it to write the correlator to a sum of various box and hexagon integrals in special kinematics. Our results provide a systematic method to relate higher-point energy correlators in the collinear limit to star integrals, which are known exactly.

hep-th↗

arXiv2026

Open-Domain Safety Policy Construction

Moderation layers are increasingly a core component of many products built on user- or model-generated content. However, drafting and maintaining domain-specific safety policies remains costly. We present Deep Policy Research (DPR), a minimal agentic system that drafts a full content moderation policy based on only human-written seed domain information. DPR uses a single web search tool and lightweight scaffolding to iteratively propose search queries, distill diverse web sources into policy rules, and organize rules into an indexed document. We evaluate DPR on (1) the OpenAI undesired content benchmark across five domains with two compact reader LLMs and (2) an in-house multimodal advertisement moderation benchmark. DPR consistently outperforms definition-only and in-context learning baselines, and in our end-to-end setting it is competitive with expert-written policy sections in several domains. Moreover, under the same seed specification and evaluation protocol, DPR outperforms a general-purpose deep research system, suggesting that a task-specific, structured research loop can be more effective than generic web research for policy drafting. We release our experiment code at https://github.com/xiaowu0162/deep-policy-research.

cs.CL↗

arXiv2026

CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption

Privacy-preserving machine learning has become an important long-term pursuit in this era of artificial intelligence (AI). Fully Homomorphic Encryption (FHE) is a uniquely promising solution, offering provable privacy and security guarantees. Unfortunately, computational cost is impeding its mass adoption. Modern solutions are up to six orders of magnitude slower than plaintext execution. Understanding and reducing this overhead is essential to the advancement of FHE, particularly as the underlying algorithms evolve rapidly. This paper presents a detailed characterization of OpenFHE, a comprehensive open-source library for FHE, with a particular focus on the CKKS scheme due to its significant potential for AI and machine learning applications. We introduce CryptOracle, a modular evaluation framework comprising (1) a benchmark suite, (2) a hardware profiler, and (3) a predictive performance model. The benchmark suite encompasses OpenFHE kernels at three abstraction levels: workloads, microbenchmarks, and primitives. The profiler is compatible with standard and user-specified security parameters. CryptOracle monitors application performance, captures microarchitectural events, and logs power and energy usage for AMD and Intel systems. These metrics are consumed by a modeling engine to estimate runtime and energy efficiency across different configuration scenarios, with error geomean of $-7.02\%\sim8.40\%$ for runtime and $-9.74\%\sim15.67\%$ for energy. CryptOracle is open source, fully modular, and serves as a shared platform to facilitate the collaborative advancements of applications, algorithms, software, and hardware in FHE. The CryptOracle code can be accessed at https://github.com/UnaryLab/CryptOracle.

cs.CR↗

arXiv2026

Iterate to Differentiate: Enhancing Discriminability and Reliability in Zero-Shot TTS Evaluation

Reliable evaluation of modern zero-shot text-to-speech (TTS) models remains challenging. Subjective tests are costly and hard to reproduce, while objective metrics often saturate, failing to distinguish SOTA systems. To address this, we propose Iterate to Differentiate (I2D), an evaluation framework that recursively synthesizes speech using the model's own outputs as references. Higher-quality models exhibit greater resilience to the distributional shift induced by iterative synthesis, resulting in slower performance degradation. I2D exploits this differential degradation to amplify performance gaps and reveal robustness. By aggregating objective metrics across iterations, I2D improves discriminability and alignment with human judgments, increasing system-level SRCC from 0.118 to 0.464 for UTMOSv2. Experiments on 11 models across Chinese, English, and emotion datasets demonstrate that I2D enables more reliable automated evaluation for zero-shot TTS.

cs.SD↗

arXiv2026

Expansion of Momentum Space and Full 2$π$ Solid Angle Photoelectron Collection in Laser-Based Angle-Resolved Photoemission Spectroscopy by Applying Sample Bias

Angle-resolved photoemission spectroscopy (ARPES) directly probes the energy and momentum of electrons in quantum materials, but conventional setups capture only a small fraction of the full 2$π$ solid angle. This limitation is acute in laser-based ARPES, where the low photon energy restricts momentum space despite ultrahigh resolution. Here we present systematic studies of bias ARPES, where applying a sample bias expands the accessible momentum range and enables full 2$π$ solid angle collection in two dimension using our 6.994 eV laser source. An analytical conversion relation is established and validated to accurately map the detector angle to the emission angle and the electron momentum in two dimensions. A precise approach is developed to determine the sample work function which is critical in the angle-momentum conversion of the bias ARPES experiments. Energy and angular resolutions are preserved under biases up to 100 V, and minimizing beam size is shown to be crucial. The technique is effective both near normal and off-normal geometries, allowing flexible Brillouin zone access with lower biases. Bias ARPES thus elevates laser ARPES to a new level, extending momentum coverage while retaining high resolution, and is applicable across a broad photon-energy range.

cond-mat.supr-con↗

arXiv2026

Probing small-scale anisotropic inflation with stochastic gravitational-wave background

In June 2023, multiple pulsar timing array (PTA) collaborations provided evidence for the existence of a stochastic gravitational-wave background (SGWB). As a significant source of the SGWBs, scalar-induced gravitational waves (SIGWs) receive extensive attention. We explore the influence of anisotropic primordial power spectra on second-order SIGWs and derive explicit expressions for the energy density spectra. For specific anisotropic inflation models, we analyze the impacts of Finslerian inflation and gauge field inflation models on PTA and the Laser Interferometer Space Antenna (LISA) and generalize the findings to model-independent scenarios. Our results indicate that current PTA observations cannot rule out the existence of small-scale anisotropic primordial perturbations.

astro-ph.CO↗

arXiv2026

PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs

Large Language Models (LLMs) have demonstrated remarkable proficiency in automated text annotation within natural language processing. However, their deployment in clinical settings is severely constrained by strict privacy regulations and the prohibitive computational cost of processing voluminous unstructured Electronic Health Records (EHRs). In this study, we developed a resource-efficient preprocessing technique that can be adopted in existing LLM procedures. This approach is particularly useful for smaller LLMs, which are often more accuracy-challenged, and forms a compact LLM framework optimized for local deployment in computational environments with stringent privacy requirements and restricted access to high-performance GPUs (PrecLLM). The preprocessing step includes both regular expressions (regex) and Retrieval-Augmented Generation (RAG) to extract and highlight key information from unstructured clinical notes. Pre-filtering long and unstructured texts enhanced the performance of smaller LLMs on EHR-related tasks. Evaluation was performed on two distinct cohorts: a locally curated private EHR dataset from the EPIC system for a Head and Neck Cancer (HNC) cohort, and the publicly available EHR dataset (MIMIC-IV). Using MIMIC-IV, we further compared PrecLLM against fine-tuned LLMs. Results demonstrated that PrecLLM substantially enhanced the performance of the original smaller LLMs in terms of sensitivity, specificity, and F1 scores, making it well-suited for privacy-sensitive and resource-constrained applications. This study offers optimized LLM performance for local, secure, and efficient healthcare applications, and provides practical guidance for clinical LLM deployment while addressing challenges related to privacy, computational feasibility, and clinical applicability.

cs.AI↗

arXiv2026

Spin-Based True Random Number Generation Enabled by Voltage-Amplified Quantum Fluctuations

We investigate spin quantum-fluctuation effects that originate from the Heisenberg uncertainty principle during the dynamical cycle of disentanglement, entanglement, and re-disentanglement between itinerant electrons and localized magnetic moments mediated by the s-d exchange interaction. Beyond conventional deterministic spin-transfer torque, we analyze an intrinsic mechanism that transfers spin quantum fluctuations to a nanomagnet. By extending the Landau-Lifshitz-Gilbert equation to incorporate both quantum and thermal stochastic fields, we identify a temperature regime in which quantum fluctuations dominate the magnetization dynamics. We further show that voltage-controlled magnetic anisotropy exponentially amplifies spin quantum fluctuations, enabling binary readout through magnetoresistance in magnetic tunnel junctions. These findings provide a microscopic framework for fluctuation-driven spin dynamics and outline a device-level pathway toward spin-based quantum true random number generation.

cond-mat.mes-hall↗