arXiv subjects

Fei Ding

Publications and source records attributed to Fei Ding.

At least 19 recordsLinked to original sources

arXiv2026

Cooling-path-selected self-assembly of telecom InAsP quantum dots

Epitaxial quantum dots are leading solid-state sources of quantum light, but reproducible growth on indium phosphide (InP) remains challenging because dot-formation windows are narrow and emission wavelengths are difficult to predict. Here we report a new epitaxial route for InAsP quantum dots on InP(001), in which the cooling path selects the final morphology without intentional deposition of group-III dot material. High-temperature As exposure forms a shallow near-surface InAsP layer through As-P exchange; the cooling path and continued As supply determine whether three-dimensional islands form. Surface-sensitive spectroscopy shows near-surface As enrichment, while cross-sectional STEM/EELS identifies graded, As-rich InAsP islands. The capped emitters span 1.2-1.6 micrometers, with single-photon emission observed up to 100 K. Across 20 wafers, the surface-effective exchange dose D provides a practical calibration for targeting the median emission wavelength. These results establish cooling as an active epitaxial step for telecom InAsP quantum-dot formation and wavelength control.

quant-ph↗

arXiv2026

What the Specification Did Not Say: Specification-Oracle Gaps in Test-Graded Coding-Agent Benchmarks

Coding-agent benchmarks often describe a task in natural language but grade it with tests taken from the upstream project. Passing those tests can require information the description never states: a constant's name, a module path, an exact message. We call this a specification-oracle gap: where it exists, a benchmark also measures whether an agent reproduces upstream implementation details. We formalise the gap, including the inconsistencies that chains of dependent tasks add, and build a static audit for five languages that classifies every name-, path- and literal-level dependency of every scored test (held-out precision 89.2%, recall 92.9%). On SWE-Milestone (98 graded milestones, 35 published runs), 25 milestones contain a test that depends on an unstated, non-inferable name or path. Build units with such a dependency fail in 80.6% of runs, against 48.4% for comparable units without one, and withholding a single hidden name from the reference stops its unit's tests from compiling in 126 of 128 cases. The leaderboard's ranking is robust to removing the affected tests, but they shift scores within repositories and favour agents that reproduce upstream names. Across four further benchmarks, prevalence appears to follow construction: a third or more of the tasks are affected where specifications are release notes or LLM summaries, almost none where tasks are screened or written for their tests; how much a gap costs depends on how the harness grades. We release the audit, corrected filter lists and specification addenda.

cs.CL↗

arXiv2026

When KL Regularization Misfires in Group Policy Optimization

Why does removing reference-policy KL regularization sometimes improve group policy optimization? This motivates studying how reference-policy information should enter group-relative updates. We analyze seven potential failure modes in the interactions between KL and rewards: residual KL updates after reward clipping, after gradient cancellation, and in groups with identical rewards; KL growth with response length and an imbalance in its relative contribution; KL concentration on a small number of tokens; and sampling noise when k1 is incorporated into rewards. We propose Zero-Sum Calibrated Policy Optimization (ZCPO), which uses relative drift measured by conditional KL to calibrate within-group reward coefficients and integrates them into the base surrogate. Mathematical reasoning experiments and ablations support this design's effectiveness in our settings.

cs.LG↗

arXiv2026

ROAM: Reinforcement Learning Optimized Adaptive Moving Target Defense for Smart Grid

Smart grids rely on distributed phasor measurement units (PMUs) and wide-area communication networks whose data-acquisition pipelines are vulnerable to stealthy false data injection (FDI) attacks that evade conventional bad-data detection (BDD). Moving target defense (MTD) counters this exposure by continuously remapping sensor identities, but existing schemes apply static or pre-scheduled randomization and are each constrained by a single failure mode: seed-based methods are fast yet fragile to key compromise, while machine learning (ML) based prediction methods are resilient yet incur prohibitive latency. Neither adapts its randomization to live threat evidence. This paper presents ROAM, a deep reinforcement learning (DRL) driven MTD framework in which a deep Q-network (DQN) agent continuously re-optimizes the sensor-to-ID mapping at the aggregation layer. At each decision step, the agent selects the deception coverage, flags the specific sensors to remap, and sets the split of the remapped sensors between seed-based and ML-prediction-based recovery, without modifying PMU hardware and while preserving IEEE C37.118 compatibility. On held-out BDD-compliant FDI scenarios across the IEEE 14-, 57-, and 118- bus systems, ROAM keeps the percentage of stealthy attacks below 3% while sustaining detection rates above 93%. Against an adaptive soft actor-critic (SAC) attacker that observes the defender's configuration, ROAM keeps stealthy attacks below 2.6% with detection above 92%. Hardware-in-the-loop testing on an OPAL-RT platform confirms real-time feasibility, with sub-18ms average shuffle execution and an average reshuffle blind window of 2.6% of the 3-s decision cycle.

eess.SY↗

arXiv2026

Metasurface-on-Lithium-Niobate Architecture for Spin-decoupled Second Harmonic Wavefront Shaping

Nonlinear metasurfaces (NMSs) enable simultaneous generation and manipulation of harmonic light with ultrathin nanophotonic platforms. However, conventional NMSs typically adopt a monolithic single-layer configuration in which frequency conversion, polarization selection and wavefront engineering are intertwined to simultaneously occur within the same nonlinear meta-atoms, making these functionalities intrinsically interdependent and imposing constraints on material choice, structural symmetry, and the available geometric parameter space. Here, we propose and experimentally demonstrate a metasurface-on-lithium-niobate architecture that physically separates harmonic light generation and wavefront manipulation. In this platform, a z-cut thin-film LN layer serves as a spin-selective second-harmonic source through its intrinsic bulk second-order nonlinearity, while an overlaid dielectric metasurface subsequently and independently molds wavefronts of generated harmonic fields. The two-step process, by virtue of harmonic generation being decoupled from wavefront shaping, removes symmetry constraints associated with conventional nonlinear meta-atoms and enables the direct transfer of well-established linear metasurface design strategies to harmonic wavefront engineering. As proof-of-concept demonstrations, we realize spin-decoupled second-harmonic beam steering and spin-multiplexed holography, achieving high-contrast routing of harmonic signals with orthogonal spin states into distinct diffraction channels and high-fidelity reconstruction of independent holographic images. By redirecting the harmonic wavefront control from the meta-atom engineering to the modular system-level design, our approach establishes a versatile route toward integrated nonlinear meta-optics and provides a scalable platform for advanced optical functionalities.

physics.optics↗

arXiv2026

Sound Constructive Refinement from Production Envelopes to Executable Manufacturing Schedules

Production planning and execution systems can interpret capacity, compatibility, material, and timing commitments differently. We treat this semantic boundary as a constructive refinement problem in which a rolling-horizon planner emits a machine-day production envelope - an explicit contract fixing production, order-fulfillment, mold-state, inventory, outsourcing, and unmet-demand commitments - and a constructive procedure refines it into order-machine-day allocations. The procedure maintains fulfillment, production, and capacity residuals. A zero fulfillment residual certifies that the allocation preserves the envelope's capacity, compatibility, and delivery-window semantics; a nonzero residual records the product, machine, mold, date, and quantity that blocked realization. Under divisible quantities, exact arithmetic, shared allocation semantics, no additional admissibility filter, and a matched-progress condition, the procedure discharges all fulfillment residuals; stricter execution policies are diagnostic extensions rather than completeness tests. We evaluate a factory-provided anonymized instance with 37 product types, 150 orders, and 8.35 million requested units, a 5,400-run synthetic matrix, and dynamic shocks. Under explicitly declared accessory-supply, initial-inventory, effective-capacity, due-window, divisible-allocation, and mold-state assumptions, the industrial scenario reports 100% on-time delivery, no shell outsourcing, and 1.9-4.6% changeover-driven capacity loss. Under a strict in-house accessory interpretation, the same data expose a load ratio of 8.0763 and nonzero residuals. The synthetic matrix locates the load boundary of the guarantee.

cs.CE↗

arXiv2026

The Thousand-Graph Hypothesis: A Testable Hypothesis of Task-Conditioned Relation Materialization in Repository-Level Code Reasoning

Large software repositories are often beyond model context limits. Training repository knowledge into models is costly and quickly stale, while local retrieval can miss scattered requirements, and explicit relation graphs add ongoing maintenance burden. We propose an entity-only external interface with task-conditioned relation materialization during inference. A two-layer index separates global routing from local entity focus and is evaluated on DeepSeek-V4-Flash and SWE-bench Verified. The base, one-layer, and two-layer conditions achieve 92.1%, 94.2%, and 95.6% success, respectively, under zero pre-built entity-relation edges.

cs.SE↗

arXiv2026

Advantage Scale Calibration Imbalance in Group-Relative Optimization under Low-Variance Rewards: Diagnosis and Bounded Recovery

In verifier-style RLVR, group-relative optimization often treats advantage scale as an implementation detail. This paper separates two low-variance cases: sub-resolution jitter that should not become a preference signal, and credible but small cardinal gaps that should be learned without distorting KL calibration. We propose an advantage-scale three-way calibration interface: the same within-group scale denominator simultaneously determines the reward-branch strength, prompt-level batch weight, and the effective KL calibration induced when the reward branch is re-expressed on the original cardinal scale. This interface explains why RLOO / Dr.GRPO can let credible small gaps become KL dominated, whereas GRPO's standard-deviation denominator can amplify tiny gaps without bound. Based on this interface, we further introduce the Reward-Resolution Protocol and MaxNorm-AC, respectively filtering sub-resolution gaps and providing bounded cardinal recovery on credible nonzero gaps. Across dense / MoE architectures and math / code reasoning, MaxNorm-AC improves over the strongest robust-scale baseline while truncating the low-variance inverse-scale tail.

cs.CL↗

arXiv2026

Can Large Language Models "Hyper-Thread"?

Large language models generate tokens sequentially, but can they execute multiple tasks concurrently while forming each token? Broader attention allocation may provide a mechanism for such task concurrency. Existing approaches to scaling inference primarily rely on longer generations, more samples, or additional verification stages, while attention dispersion is often treated as a signal of interference or error. Task concurrency within serial generation therefore remains underexplored. We propose the Model Hyper-Threading Hypothesis and evaluate its predictions using multiple coordinated tasks that share state within the same problem. We design three conditions (Baseline, Serial Functional Scheduling, and Concurrent Functional Loading) and evaluate their benefits and costs using accuracy, output-token distributions, and attention metrics. On an AIME 2025 development set, Concurrent Functional Loading achieves the highest accuracy. Relative to Serial Functional Scheduling, its typical output length is similar and it is shorter on most problems, while exhibiting greater attention dispersion and higher task-relevant coverage, albeit with a heavier output-length tail. Within-step concurrency and its causal mechanism still require direct tests. Our results show that more dispersed attention can coexist with higher accuracy, providing preliminary behavioral and correlational evidence for the hyper-threading hypothesis. These findings motivate a shift in perspective on inference scaling from "generating more tokens" toward "having each generation step carry more tasks," pointing to a new avenue for improving reasoning performance.

cs.CL↗

arXiv2026

The QTF-Backbone: Proposal for a Nationwide Optical Fibre Backbone in Germany for Quantum Technology and Time and Frequency Metrology

The recent breakthroughs in the distribution of quantum information and high-precision time and frequency (T&F) signals over long-haul optical fibre networks have transformative potential for physically secure communications, resilience of timing infrastructure (such as that supporting Global Navigation Satellite Systems (GNSS)) and fundamental physics. To date, these capabilities remain confined to isolated testbeds, with quantum and T&F signals accessible, for example in Germany, to only a few institutions. In this white paper we propose the QTF Backbone: a dedicated national fibre-optic infrastructure in Germany for the networked distribution of Quantum and T&F signals using dark fibres and specialised hardware. The QTF Backbone is planned as a four-phase deployment over ten years to ensure scalable, sustainable access for research institutions and industry. The concept builds on successful demonstrations of time and frequency distribution at high Technology Readiness Levels (TRLs) across Europe, including PTB-MPQ links in Germany, REFIMEVE in France, and the Italian LIFT network. The QTF Backbone will enable transformative Research and Development (R&D), support a nationwide QTF ecosystem, and ensure the transition from innovation to deployment. As a national and European hub, it will position Germany and Europe at the forefront of quantum networking, as well as T&F transfer.

physics.ins-det↗

arXiv2026

I Seek You in Videos: Identity-Conditioned Queries for Person-Centric Video Reasoning

Real-world video reasoning often involves multimodal, multi-source inputs, whereas existing video reasoning tasks typically assume a simplified video-text setting, limiting identity matching and person-centric reasoning. To bridge this gap, we introduce the Identity-conditioned Queries (ICQ) task, in which models are required to jointly associate and interpret an input video and a reference image of a person, and leverage this conditioning to address identity grounding, behavior understanding, and temporal reasoning, among other challenges. Building on ICQ, we present ISYV (I Seek You in Videos), a systematic solution comprising three components: (1) ISYV-Bench, a challenging evaluation benchmark with 1,377 real-world complex videos and 1,377 question-answer pairs, organized into six difficulty levels spanning capabilities from identity recognition to causal reasoning; (2) ISYV-75K, a large-scale training set of 75K high-quality samples constructed via automated annotation, multi-stage verification, and manual review; and (3) ISYV-Framework, containing an ICQ-oriented model and training strategy for learning to exploit informative video shots without additional shot-level annotations. Extensive experiments show that both mainstream closed-source and open-source MLLMs struggle on ISYV-Bench, especially in cross-domain identity matching and long-horizon tracking. ISYV-Model outperforms strong baselines and in some aspects approaches closed-source performance. Overall, ISYV provides a unified task definition, scalable datasets/benchmarks, and modeling insights for person-centric video reasoning.

cs.CV↗

arXiv2026

LATTICE: Constraint-Directed Scheduling, Memory Planning, and Pipeline Refinement for NPUs

General-purpose NPUs execute fine-grained command DAGs across heterogeneous compute and memory-transfer engines backed by finite, explicitly managed on-chip memories. This execution model creates a directed dependency between scheduling and memory planning: different legal topological orders induce different lifetime overlap, placement opportunities, and spill behavior, while a materialized layout introduces physical-address reuse constraints absent from the input precedence DAG. Command order therefore shapes the feasible memory plan, and the realized plan in turn defines the legal space for subsequent timing refinement. We present LATTICE, a deterministic constraint-directed compiler pipeline. Memory-Pressure-Aware Topological Scheduling reshapes lifetime geometry before address binding; Deterministic Linear Repackaging materializes tiered placement, spill/reload events, and plan-induced reuse constraints; and Critical Path Enhancement recovers pipeline parallelism while preserving the selected memory plan. Every accepted schedule passes independent memory and timing verification. Across six artifact-provided command traces labeled as derived from a Da Vinci NPU flow, LATTICE achieves the best or tied-best result in all 24 evaluated workload-metric comparisons. Relative to the best evaluated baseline for each workload and metric, it reduces peak memory, extra DDR traffic, spill count, and modeled makespan by 18.3% lower, 20.4% lower, 14.1% lower, and 16.3% lower, respectively. Plan-preserving CPE further reduces makespan by 12.1% over Freeze while leaving placement and memory traffic unchanged, establishing the static memory plan as a verifiable scheduling contract between memory planning and pipeline optimization.

cs.NI↗

arXiv2026

Electro-Optic Active Metasurfaces for High-Speed Photonic Applications

Metasurfaces are artificially engineered ultrathin nanostructured surfaces, capable of flexibly manipulating light-matter interactions on compact platforms, and thereby of great significance for a wide range of applications within modern optics and photonics, including communications, computing, sensing, and quantum technologies. However, the inherently static nature of conventional metasurfaces severely limits their functionalities and thus range of possible applications. Benefiting from integration of the metasurface platform for shaping optical wavefronts with ultrafast electro-optic (EO) materials, active EO metasurfaces have emerged as a frontier research direction targeting advanced photonic devices. This paper systematically reviews the latest progress in this field, featuring a comprehensive comparison of performances and application scenarios of mainstream EO materials such as lithium niobate, barium titanate and organic EO polymers. Modulation mechanisms based on the Pockels and Kerr effects along with the corresponding active metasurface implementations are summarized. Furthermore, improvements in modulation efficiency enabled by advantageously exploiting resonant structural designs and associated phenomena, including Fabry-Perot resonances, Mie resonances, surface plasmon polaritons, quasi-bound states in the continuum, surface lattice resonances, and guided-mode resonances, are presented and summerized in detail. Current challenges related to metasurface design, nanofabrication, performance and heterogeneous integration are also discussed. Finally, future research directions are outlined, highlighting interdisciplinary developments, novel material engineering, and AI-assisted design as key pathways to enable practical use of active EO metasurfaces in modern optics and photonics, including quantum information technologies.

physics.optics↗

arXiv2026

FilmBench: A Film-Grade Benchmark for Cinematic Video Generation

Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw prompts from web sources or LLM templates and score them with untrained, generic multimodal models. More fundamentally, their evaluation taxonomies remain rudimentary (overall visual quality, coarse text alignment and temporal smoothness) rather than the professional Cinematic Language criteria by which films are actually made and judged, so they assess basic video plausibility rather than film-grade craft. We introduce FilmBench, a text-to-video (T2V) and reference-to-video (R2V) benchmark grounded in the professional Cinematic Language of the film- academy tradition and co-developed with directors and faculty from the Beijing Film Academy and the Hujing Digital Media & Entertainment Group film studio. It rests on three choices. First, prompts are reverse-engineered from clips of award-winning films spanning 20 cinematic genres and chosen by professional directors, so every prompt is anchored to a verified live-action reference; the prompts follow real shot lists, and most script multiple shots (1,056 of the 1,169 prompts are multi-shot), unlike prior single-clip benchmarks. Second, evaluation follows a three-level Cinematic taxonomy of 3 axes, 12 components and 35 (T2V) +3 (R2V-only) sub-metrics. Third, we develop an in-house expert-grade automatic evaluation agent and open-source its core suite of Cinematic Language operators (FilmOps). Benchmarking leading video generation models (9 for T2V, 7 for R2V), the evaluator reproduces the human model ranking at model-level Spearman \r{ho} = 0.95 (T2V) and 0.96 (R2V). Scores fall well below prior web-style benchmarks, with two consistent gaps in dynamic aesthetics and a marked single- to multi-shot performance drop that widens for weaker models.

cs.CV↗

arXiv2026

On the Impossibility of Unbiased and Length-Invariant Policy Optimization with Outcome Rewards

Group Relative Policy Optimization (GRPO) is the dominant reinforcement learning algorithm for training reasoning capabilities in large language models, notably adopted by DeepSeek-R1. The recent improvement Dr. GRPO (COLM 2025) identifies the response-level length bias caused by per-trajectory length normalization in GRPO and proposes removing this normalization, claiming the resulting optimizer is "unbiased." We show that this claim is incomplete. Specifically, we establish an impossibility theorem: under the standard outcome reward + GRPO setting, no length-based weighting scheme can simultaneously achieve the following two properties. (P1) Gradient unbiasedness: the gradient estimator is an unbiased estimate of the true policy gradient. (P2) Length invariance: each trajectory's effective contribution to the gradient is independent of its token length. GRPO approximately satisfies P2 but violates P1; Dr. GRPO satisfies P1 but violates P2. We characterize the complete tradeoff spectrum via the parametric family f_alpha(L) = L^{alpha - 1}, where alpha = 0 recovers GRPO, alpha = 1 recovers Dr. GRPO, and provide quantitative analysis showing that Dr. GRPO's length bias can cause longer trajectories to dominate gradient updates by a factor proportional to the length ratio. Our results reveal that neither algorithm is universally "done right"; they occupy opposite ends of a fundamental and unavoidable tradeoff.

cs.LG↗

arXiv2026

Calibration of systematic distortions in quantum emitter localization microscopy for deterministic nanophotonic fabrication

Quantum photonic technologies greatly benefit from quantum light emitters with high brightness, indistinguishability, and reliable polarization characteristics. Achieving optimal performance relies on the accurate localization of emitters and their deterministic integration into tailored photonic structures with nanometer-scale accuracy. Although marker-based photoluminescence imaging techniques can achieve statistical fitting uncertainties below 10 nm, the ultimate integration yield is often limited by uncorrected systematic distortions in custom cryo-optical setups that compromise metrological accuracy. Here, we present an in situ calibration protocol that uses lithographically defined gold nanodisk arrays as references to calibrate optical distortions with a Zernike vector-field model. On held-out validation patterns beyond the calibration dataset, this correction reduces the residual systematic bias to 5.3 nm with a 2D scatter of 24.6 nm across the analyzed field of view. Furthermore, we demonstrate that applying this correction to the deterministic fabrication of circular mesa structures around semiconductor quantum dots reduces the variance in emission polarization by 49%, indicating improved registration accuracy. This calibration strategy offers a practical route to high-yield deterministic integration of quantum emitters into scalable quantum photonic circuits.

physics.optics↗

arXiv2026

Skin Lesion Classification Based on ResNet-50 Enhanced With Adaptive Spatial Feature Fusion

Skin cancer classification is challenging due to high inter-class similarity, intra-class variability, and artifacts in dermoscopic images. To address these issues, we propose an improved ResNet-50 with Adaptive Spatial Feature Fusion (ASFF), which adaptively integrates multi-scale semantic and surface features to refine representations and reduce overfitting. The ResNet-50 model is enhanced with an adaptive feature fusion mechanism to achieve more effective multi-scale feature extraction and improve overall performance. Specifically, a dual-branch design fuses high-level semantic and mid-level detail features which use global average pooling and fully connected layers to produce spatial weights, and emphasizes lesion-relevant regions. Evaluated on a balanced subset of ISIC 2020 (3,297 images, randomly selected from the original dataset), the ASFF-based ResNet-50 outperforms multiple CNN baselines, achieving 93.182% accuracy with superior precision, recall, specificity, and F1. It also reaches 0.9670 AUC (P-R) and 0.9717 AUC (ROC). Grad-CAM visualizations show more accurate focus on lesion areas.The proposed model also generalizes well to ISIC 2019 external validation, outperforming the ResNet-50 baseline. These findings demonstrate that the proposed approach provides a more effective and efficient solution for computer-aided skin cancer diagnosis. The generation codes, weights and confusion matrices are open sourced in https://github.com/Grapesea/ASFF-ResNet50-enhanced.

cs.CV↗

arXiv2026

ThinkReset: Learnable Intermediate Interface Construction for Bounded-Context Long-Horizon Reasoning

Long chain-of-thought reasoning improves performance on complex problems, but it also introduces redundancy accumulation, context overflow, and error anchoring. We argue that under bounded context windows, the core bottleneck is not trajectory compression or test-time control, but the absence of a reusable intermediate interface that can replace discarded history and support continued solving. We further identify a key failure mode of outcome-reward-driven long-chain reinforcement learning: when the model has not solved the task before the window is nearly exhausted, the final-answer reward encourages premature guessing rather than continued careful reasoning. We propose ThinkReset, a text-space instantiation of this view. ThinkReset explicitly constructs reusable intermediate interfaces through interface writeback and reset, and directly optimizes post-reset continuation success. Across multiple long-horizon reasoning benchmarks, this perspective consistently improves success rates under fixed context windows.

cs.AI↗