arXiv subjects

Zhiyuan Lin

Publications and source records attributed to Zhiyuan Lin.

At least 19 recordsLinked to original sources

arXiv2026

MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning

AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. Yet no existing benchmark evaluates both simultaneously. We introduce MemoryDocDataSet, a synthetic benchmark of 50 micro-worlds and 1,000 QA pairs in which each instance comprises 3-5 personas, a temporal event graph spanning months of activity, 3-5 real long documents (20,000-50,000 tokens each sourced from the Caselaw Access Project), multi-session conversations grounded on those documents, and 20 question-answer pairs across five reasoning categories. The defining feature is the Hybrid source tag: questions requiring a system to first navigate conversation history to identify which document is relevant, then extract the answer from within that document. Hybrid questions account for 75.1% of the dataset. Dataset quality is characterised through a prompt-sensitivity self-consistency analysis using LLM-as-judge, yielding a median Cohen's $\kappa = 0.634$ across all 50 micro-worlds. We evaluate six baseline configurations spanning truncated context, long-context LLMs, retrieval-augmented generation (RAG), and memory systems. The best baseline (RAG-Both) achieves 0.358 overall F1 and 0.342 on Hybrid. Document-only retrieval (RAG-Doc) collapses to 0.267 on Hybrid despite achieving 0.453 on Doc-only questions, demonstrating a clear joint-retrieval gap that motivates architectures unifying conversational memory with long-document navigation. We release the dataset, generation pipeline, and all baseline implementations.

cs.CL↗

arXiv2026

When Does Persona Prompting Actually Help? A Retrieval and Metric Analysis of Expert Role Injection in LLMs

Persona prompting is widely used to steer large language models, yet its practical value remains unclear. Prior work often evaluates persona prompting using aggregate scores, making it difficult to determine whether expert-role prompting consistently improves response quality or instead changes responses along different quality dimensions. We study this question through a controlled comparison of four prompting conditions across 1,140 open-ended questions spanning 38 expert roles and six domains: no role prompt, a generic domain-expert prompt, embedding-based role retrieval, and a hybrid retrieval method combining embedding search with LLM-based role selection. Aggregate results show only small overall differences between conditions. However, metric-level analysis reveals a consistent tradeoff that aggregate averages obscure: role prompting systematically increases expertise depth while reducing clarity. These effects are highly conditional rather than universal. Role prompting performs best on advisory questions and in domains such as medicine and psychology, where structured expert framing and risk communication are intrinsically valuable. In contrast, baseline prompting performs better on conceptual and explanatory questions in finance, legal, science, and technology domains, where concise plain-language explanation is more important. We further show that hybrid retrieval significantly improves over embedding-only role selection, although better role retrieval does not eliminate the broader expertise-depth versus clarity tradeoff. Overall, our findings suggest that persona prompting primarily reshapes response characteristics rather than broadly improving capability, and that multi-metric evaluation is necessary for understanding its effects.

cs.AI↗

arXiv2026

Toward Secure Operation and Management (O&M) of Satellite Constellations: Efficiency, Resilience, and Reliability in a Network Perspective

Satellite constellations equipped with Inter-Satellite Links and onboard packet switching enable real-time Operation and Management across globally distributed satellites, but also broaden the attack surface and introduce unprecedented cybersecurity threats. Existing efforts mainly focus on cryptography for single-satellite point-to-point links, without considering constellation-level security. To address this gap, this article extends security research in two directions: from individual satellites to constellation-wide architectures, and from isolated cryptography to system-level security incorporating efficiency, resilience, and reliability. These extensions raise three key questions: how to design efficient security mechanisms for dynamic constellation topologies with adaptive onboard routing; how a constellation O&M system can recover resiliently under worst-case failures of onboard security functions; and how to improve the reliability of onboard security functions under stringent resource constraints. To address these challenges, we first construct a constellation-wide hybrid security framework that protects semantically sensitive content fields using End-to-End encryption, while safeguarding routing-related fields through Moving Target Defense. Next, we introduce a ciphered-mode and safe-mode management mechanism with an M-delayed fallback that balances recovery timeliness and exploitability. Finally, we propose security-aware routers that manage plaintext/ciphered modes and coordinate access to a shared pool of onboard cipher modules, enabling redundancy sharing across multiple endpoints and extending secure operation duration in ciphered mode. These solutions comply with existing standards defined by organizations including DVB and the CCSDS, while translating conceptual security principles into practical system-level mechanisms.

cs.CE↗

arXiv2026

What Training Data Teaches RL Memory Agents: An Empirical Study of Curriculum Effects in Memory-Augmented QA

Reinforcement learning (RL) has emerged as a viable recipe for training LLM agents to reason over external memory banks in multi-session dialogue. Existing work trains exclusively on a single benchmark, leaving open how the composition of training data shapes the skills a memory agent acquires. We present a controlled empirical study that holds architecture, RL algorithm, and all hyperparameters fixed and varies only the training curriculum across three conditions: in-domain (LoCoMo), mixed-benchmark (LoCoMo + LongMemEval), and out-of-domain (LongMemEval only). Across two benchmarks and ten question types, curriculum composition acts as a fine-grained lever on specialization rather than a uniform scaling factor on performance. The mixed curriculum yields the strongest overall F1 on both evaluation sets. Training on a narrow out-of-domain set transfers a targeted skill - temporal reasoning - despite weak aggregate performance. Per-type differences substantially exceed aggregate differences, indicating that single-number benchmark comparisons systematically underreport curriculum effects. We further report two practical lessons from adapting GRPO to a single-GPU regime: cross-benchmark mixing requires filtering format-specific noise from memory banks to preserve training signal, and binary exact-match reward produces no learning signal at the small group sizes (G = 4) required on one GPU, motivating continuous reward functions in this regime.

cs.CL↗

arXiv2026

Preparing squeezed, cat and GKP states with parity measurements

Bosonic modes constitute a central resource in a wide range of quantum technologies, providing long-lived degrees of freedom for the storage, processing, and transduction of quantum information. Such modes naturally arise in platforms including circuit quantum electrodynamics, quantum acoustodynamics, and trapped-ion systems. In these architectures, coherent control and high-fidelity readout of the bosonic degrees of freedom are achieved via coupling to an auxiliary qubit. When operated in the strong dispersive regime, this interaction enables parity measurements of the mode which, in combination with phase-space displacements, constitute a standard experimental tool for full Wigner-function tomography. Here, we propose a protocol based on displaced parity measurements that allows for the preparation of a variety of bosonic quantum states. As a first example, we demonstrate the generation of squeezed states, achieving up to ~9 dB of squeezing after only three parity measurements, and show that the protocol is robust against experimental imperfections. Finally, we generalize our approach to the preparation of other paradigmatic bosonic states, including cat and Gottesman-Kitaev-Preskill states.

quant-ph↗

arXiv2025

Generative Spectrum Cartography: Unified Reconstruction and Active Sensing via Diffusion Models

High-fidelity spectrum cartography is important for spectrum monitoring and wireless situational awareness, especially in satellite-based wide-area sensing scenarios where measurements are sparse, noisy, and often low-bit quantized. In such settings, two coupled challenges arise: accurate reconstruction from severely incomplete measurements and efficient allocation of additional sensing resources under a limited sensing budget. Existing methods usually address these problems separately, and, for reconstruction, they often rely on priors that are insufficiently expressive under sparse and quantized measurements. This paper proposes Generative Spectrum Cartography (GSC), a diffusion-based posterior inference framework for spectrum cartography with uncertainty-aware active sensing. Specifically, spectrum map recovery is formulated as a Bayesian inverse problem under a learned diffusion model prior, and closed-form posterior mean updates are derived for both linear and quantized measurement models. By embedding these updates into the reverse diffusion process, GSC enables gradient-free and measurement-consistent posterior sampling without relying on computationally costly likelihood-gradient guidance. The resulting posterior samples are further used to estimate spatial uncertainty and to guide diversity-aware selection of additional measurement locations for active sensing. Experiments on simulated electromagnetic maps and a high-fidelity simulated satellite monitoring scenario show that GSC achieves higher PSNR, lower LPIPS, and more efficient sensing than representative baseline methods under sparse, noisy, and low-bit quantized measurements.

cs.IT↗

arXiv2025

NeutrSHINE: a high repetition rate ultrafast neutron source driven by SHINE electron beam

Neutrons serve as unique probes for exploring the microscopic structure of matter, with the performance of a neutron source fundamentally governing the depth of scientific exploration and the breadth of industrial applicability. To address application demands including nuclear data measurement in the ultra-high-energy region, fundamental particle physics research, highly efficient non-destructive neutron testing, and extreme environment simulation, an ultrafast neutron source driven by the 8 GeV electron beam from the Shanghai high-repetition-rate extreme light facility (SHINE) was conceptually proposed, named NeutrSHINE. Using multidisciplinary simulation tools, key neutronic parameters, thermal behavior of high-power neutron targets, and the factors affecting the time resolution of the source were analyzed. The results affirm the technical feasibility and promising application prospects of the NeutrSHINE concept.

physics.acc-ph↗

arXiv2025

Pre-emptive parametric kill switch for evaporative atomic sources in vacuum

A robust pre-emptive kill switch for cold atom experiments is introduced to significantly reduce costly system reassembly or replacement. The design incorporates upper (alarm) and lower (evaporation) event detection mechanisms based on predefined thresholds. Meanwhile, a duty cycle timing methodology is used to avert unintentional activation of the dispenser in circumstances where pulse signals occur. The circuit employs generic components, a modular design, and formalized logic, ensuring cost-effectiveness, making the design suitable for school laboratories and other research environments. This design is highly versatile and can be applied to other sensitive devices beyond dispensers, such as heating filaments, titanium sublimation pumps, tungsten lamps, and comparable systems.

physics.atom-ph↗

arXiv2025

GSTM-HMU: Generative Spatio-Temporal Modeling for Human Mobility Understanding

Human mobility traces, often recorded as sequences of check-ins, provide a unique window into both short-term visiting patterns and persistent lifestyle regularities. In this work we introduce GSTM-HMU, a generative spatio-temporal framework designed to advance mobility analysis by explicitly modeling the semantic and temporal complexity of human movement. The framework consists of four key innovations. First, a Spatio-Temporal Concept Encoder (STCE) integrates geographic location, POI category semantics, and periodic temporal rhythms into unified vector representations. Second, a Cognitive Trajectory Memory (CTM) adaptively filters historical visits, emphasizing recent and behaviorally salient events in order to capture user intent more effectively. Third, a Lifestyle Concept Bank (LCB) contributes structured human preference cues, such as activity types and lifestyle patterns, to enhance interpretability and personalization. Finally, task-oriented generative heads transform the learned representations into predictions for multiple downstream tasks. We conduct extensive experiments on four widely used real-world datasets, including Gowalla, WeePlace, Brightkite, and FourSquare, and evaluate performance on three benchmark tasks: next-location prediction, trajectory-user identification, and time estimation. The results demonstrate consistent and substantial improvements over strong baselines, confirming the effectiveness of GSTM-HMU in extracting semantic regularities from complex mobility data. Beyond raw performance gains, our findings also suggest that generative modeling provides a promising foundation for building more robust, interpretable, and generalizable systems for human mobility intelligence.

cs.LG↗

arXiv2025

Sculpting Topological Modes on Photonic Chips by Artificial Gauge Fields

Significant efforts have been devoted to manipulating topological states, which often manifest as localized modes at interfaces between distinct topological phases. In this work, we demonstrate a versatile approach to sculpting topological modes (TMs) into any desired shapes by incorporating various artificial gauge fields (AGFs), including scalar, vector, and imaginary gauge potentials, and leveraging the power of artificial neural networks (ANNs). These AGFs enable precise tuning of the dissipation of the TMs across that of bulk modes, facilitating a transition from localized to fully delocalized states. Moreover, ANNs allow precise engineering of these eigenmodes to achieve tailored profiles of topological states, which remain spectrally isolated within the bandgap and exhibit minimal loss compared to other modes. Our theoretical results are experimentally validated on silicon photonic platforms, demonstrating flexible manipulation of TM profiles. This approach enables the design of topological states with customized properties, offering significant potential for diverse applications in photonics and beyond.

physics.optics↗

arXiv2025

Train Unit Scheduling with Unit Ordering under Platform-Feasible Operations

In passenger railways where coupling and decoupling occur at platforms, a rolling-stock plan may be circulation-feasible but station-infeasible when the within-formation order of identified units causes blockage. We study the Train Unit Scheduling Problem under platform-feasible operations, where units cannot overtake or be resequenced without authorised shunting or resequencing. We formulate, to the best of our knowledge, the first single-stage unit-level integer linear programming model for this setting. It tracks identified units on a unit-indexed connection network and jointly determines the movements, coupling and decoupling decisions, and within-formation positions of train units, so every feasible integer solution provides a blockage-free schedule under the modelled restrictions. We further derive an exact fixed-assignment characterisation of orderability. Active coupling and decoupling requirements induce trip-wise precedence digraphs, while continuation arcs impose pairwise carry-over consistency. An assignment is orderable if and only if these digraphs admit a continuation-consistent family of topological orders. This yields a Train Unit Scheduler with Ordered Units (TUSOU), an exact branch-and-bound-and-cut train unit scheduler developed by us using ordering certification, lazy recovery of ordering constraints and activated cycle inequalities. Experiments on five real-world-derived TransPennine Express instances show that TUSOU produces certified blockage-free schedules, solves all instances to zero reported gap under solver tolerances, and outperforms direct full-model Gurobi baselines. Certification rejects 39 of 59 integer assignment-candidate encounters, showing that orderability should be embedded in optimisation rather than treated as post-processing.

math.OC↗

arXiv2025

Ultrahigh threshold nonstabilizer nonlinear quantum error correcting code

We introduce a novel type of quantum error correcting code, called the spinor code, based on spaces defined by total spin. The code is a nonstabilizer code, and is also a nonlinear quantum error correcting code, meaning that quantum information is encoded in a parameterized family of quantum states, rather than a linear superposition of code words. Syndrome measurements are performed by projecting on states with differing total spin, with an associated correction to map states back to the maximum total spin space. We show that the code is asymptotically capable of protecting against any single qubit Pauli error for Gaussian distributed states such as spin coherent state. We directly evaluate the performance under the depolarizing channel, considering various cases, with and without initialization and measurement errors, as well as two qubit errors. We estimate the code-capacity threshold to be in the range of 32-75%, while the phenomenological threshold is in the range 9-75%.

quant-ph↗

arXiv2025

Macroscopic quantum teleportation with ensembles of qubits

We develop methods for performing quantum teleportation of the total spin variables of an unknown state, using quantum nondemolition measurements, spin projection measurements, and classical communication. While theoretically teleportation of high-dimensional states can be attained with the assumption of generalized Bell measurements, this is typically experimentally non-trivial to implement. We introduce two protocols and show that, on average, the teleportation succeeds in teleporting the spin variables of a spin coherent state with average zero angular error in the ideal case, beating classical strategies based on quantum state estimation. In a single run of the teleportation, there is an angular error at the level of ~ 0.1 radians for large ensembles. A potential physical implementation for the scheme is with atomic ensembles and quantum nondemolition measurements performed with light. We analyze the decoherence of the protocols and find that the protocol is robust even in the limit of large ensemble sizes.

quant-ph↗

arXiv2024

Bound-extended mode transition in type-II synthetic photonic Weyl heterostructures

Photonic structures with Weyl points (WPs), including type-I and type-II, promise nontrivial surface modes and intriguing light manipulations for their three-dimensional topological bands. While previous studies mainly focus on exploring WPs in a uniform Weyl structure, here we establish Weyl heterostructures (i.e., a nonuniform Weyl lattice) with different rotational orientations in the synthetic dimension by nanostructured photonic waveguides. In this work, we unveil a transition between bound and extended modes on the interface of type-II Weyl heterostructures by tuning their rotational phases, despite the reversed topological order across the interface. This mode transition is also manifested from the total transmission to total reflection at the interface. All of these unconventional effects are attributed to the tilted dispersion of type-II Weyl band structure that can lead to mismatched bands and gaps across the interface. As a comparison, the type-I Weyl heterostructures lack the phase transition due to the untilted band structure. This work establishes a flexible scheme of artificial Weyl heterostructures that opens a new avenue towards high-dimensional topological effects and significantly enhances our capabilities in on-chip light manipulations.

physics.optics↗

arXiv2024

Observation of topology transition in Floquet non-Hermitian skin effects in silicon photonics

Non-Hermitian physics has greatly enriched our understanding of nonequilibrium phenomena and uncovered novel effects such as the non-Hermitian skin effect (NHSE) that has profoundly revolutionized the field. NHSE is typically predicted in systems with nonreciprocal couplings which, however, are difficult to realize in experiments. Without nonreciprocal couplings, the NHSE can also emerge in systems with coexisting gauge fields and loss or gain (e.g., in Floquet non-Hermitian systems). However, such Floquet NHSE remains largely unexplored in experiments. Here, we realize the Floquet NHSEs in periodically modulated optical waveguides integrated on a silicon photonics platform. By engineering the artificial gauge fields induced by the periodical modulation, we observe various Floquet NHSEs and unveil their rich topological transitions. Remarkably, we discover the transitions between the normal unipolar NHSEs and an unconventional bipolar NHSE which is accompanied by the directional reversal of the NHSEs. The underlying physics is revealed by the band winding in complex quasienergy space which undergoes a topology change from isolated loops with the same winding to linked loops with opposite windings. Our work unfolds a new route toward Floquet NHSEs originating from the interplay between gauge fields and dissipation effects and offers fundamentally new ways for steering light and other waves.

physics.optics↗

arXiv2023

A Mobile Data-Driven Hierarchical Deep Reinforcement Learning Approach for Real-time Demand-Responsive Railway Rescheduling and Station Overcrowding Mitigation

Real-time railway rescheduling is an important technique to enable operational recovery in response to unexpected and dynamic conditions in a timely and flexible manner. Current research relies mostly on OD based data and model-based methods for estimating train passenger demands. These approaches primarily focus on averaged disruption patterns, often overlooking the immediate uneven distribution of demand over time. In reality, passenger demand deviates significantly from predictions, especially during a disaster. Disastrous situations such as flood in Zhengzhou, China in 2022 has created not only unprecedented effect on Zhengzhou railway station itself, which is a major railway hub in China, but also other major hubs connected to Zhengzhou, e.g., Xi'an, the closest hub west of Zhengzhou. In this study, we define a real-time demand-responsive (RTDR) railway rescheduling problem focusing two specific aspects, namely, volatility of the demand, and management of station crowdedness. For the first time, we propose a data-driven approach using real-time mobile data (MD) to deal with this RTDR problem. A hierarchical deep reinforcement learning (HDRL) framework is designed to perform real-time rescheduling in a demand-responsive manner. The use of MD has enabled the modelling of passenger dynamics in response to train delays and station crowdedness, and a real-time optimisation for rescheduling of train services in view of the change in demand as a result of passengers' behavioural response to disruption. Results show that the agent can steadily satisfy over 62% of the demand with only 61% of the original rolling stock, ensuring continuous operations without overcrowding. Moreover, the agent exhibits adaptability when transferred to a new environment with increased demand, highlighting its effectiveness in addressing unforeseen disruptions in real-time settings.

eess.SY↗

arXiv2021

Thermal divergences of quantum measurement engine

A quantum engine fueled by quantum measurement is proposed. Under the finite-time adiabatic driving regime, the conversion of heat to work is realized without the compression and expansion of the resonance frequency. The work output, quantum heat, and efficiency are derived, highlighting the important role of the thermal divergence recently reappearing in open quantum systems. The key problem of how the measurement basis can be optimized to enhance the performance is solved by connecting the thermal divergence to the nonequilibrium free energy and entropy. The spin-engine architecture offers a comprehensive platform for future investigations of extracting work from quantum measurement.

quant-ph↗

arXiv2021

Suppressing coherence effects in quantum-measurement based engines

The recent advances in the study of thermodynamics of microscopic processes have driven the search for new developments in energy converters utilizing quantum effects. We here propose a universal framework to describe the thermodynamics of a quantum engine fueled by quantum projective measurements. Standard quantum thermal machines operating in a finite-time regime with a driven Hamiltonian that does not commute in different times have the performance decreased by the presence of coherence, which is associated with a larger entropy production and irreversibility degree. However, we show that replacing the standard hot thermal reservoir by a projective measurement operation with general basis in the Bloch sphere and controlling the basis angles suitably could improve the performance of the quantum engine as well as decrease the entropy change during the measurement process. Our results go in direction of a generalization of quantum thermal machine models where the fuel comes from general sources beyond the standard thermal reservoir.

quant-ph↗