arXiv subjects

Yang Shen

Publications and source records attributed to Yang Shen.

At least 19 recordsLinked to original sources

arXiv2026

Temporal Regression-Based Model-Free Sensorless Control of Permanent Magnet Synchronous Motor

To address the widespread sensitivity of surface-mounted permanent magnet synchronous motor (SPMSM) sensorless control to motor parameters, this paper proposes a temporal regression-based model-free sensorless control (TFC) method. First, voltage integrals and current increments over consecutive short intervals are stacked to construct a finite window regression, in which the unknown stator inductance appears as a common scalar coefficient. Second, a projection operator constructed from the stacked current increments eliminates the inductance term, and a least-squares formulation is developed to reconstruct the rotor flux vector. Meanwhile, the analysis of the projected regression and current-flux geometry establishes a two-dimensional direction vector whose components share a common amplitude containing the stator resistance and flux linkage. This amplitude cancels during position extraction. By setting the resistance reference to zero, the proposed method estimates the position without specifying the stator resistance, inductance, or flux linkage. Finally, experimental results verify the effectiveness of the proposed TFC method.

eess.SY↗

arXiv2026

Equivalent Flux Compensation for SPMSM Sensorless Control under Parameter Mismatch

Parameter mismatch is the main source of rotor position estimation error in sensorless control of surface-permanent magnet synchronous motors (SPMSMs). To this end, this paper proposes a simple yet efficient equivalent flux compensation (EFC) method that directly estimates the equivalent flux disturbance caused by parameter mismatches in real time. First, the equivalent flux disturbance caused by parameter mismatches is derived from a nonlinear flux observer. Second, a flux update law is proposed to minimize both magnitude and directional errors by leveraging geometric error together with the derived equivalent flux disturbance. To enhance numerical stability, a saturation function is introduced to improve gradient continuity in the update process. Additionally, Lyapunov analysis is employed to ensure the stability of the proposed update law, from which the corresponding error bounds and convergence properties are derived. Finally, experimental results validate that the proposed method fully compensates for the steady-state effects of resistance and flux mismatches, and partially mitigates the influence of inductance variation, effectively constraining the position estimation error within a relatively small range.

eess.SY↗

arXiv2026

MARS: Multi-stage Accelerated Read Stack for Large-buffer Buffered Reads

Large-buffer reads increasingly connect data-intensive applications to high-speed storage. They amortize system-call overhead and create a larger in-kernel window for organizing page-cache work and submitting I/O. However, Linux buffered read primarily exploits only the former benefit. Within a large read, its conventional interleaved path repeatedly switches among fine-grained page-cache operations, amplifying metadata and serial orchestration overheads and failing to consistently expose enough in-flight requests to modern parallel SSDs. We present MARS, a multi-stage accelerated read stack for synchronous large-buffer buffered reads. MARS treats each large-range read as one unit of work and stages page-cache operations by data structure and dependency. During I/O waits, it handles user-buffer page faults and performs reorderable data copies early. Opportunistic kernel workers then copy remaining data in parallel and, when the backend provides sufficient parallelism, optionally submit I/O in parallel. We implement MARS in Linux 6.6.58. For MiB-scale fio reads, MARS improves bandwidth by up to 6.56 times over Linux. On five NVMe SSDs in RAID0, it reaches 36.87 GiB/s for 128 MiB random reads, 4.44 times Linux. MARS also accelerates DuckDB/Parquet queries by 1.80--2.15 times and ExecuTorch model loading by 3.17--3.61 times.

cs.OS↗

arXiv2026

Beyond Peak Backlog: Conditional Energy and Temporal Geometry in Capacity-Constrained Delayed Bandit Optimization

What is the right delay complexity when a learner can track only $C$ pending feedback items and discarded feedback is permanently lost? Existing one-point bandit convex optimization guarantees in this model pay $\sqrt{Tσ_{\max}}$, where $σ_{\max}$ is the peak backlog, although unlimited tracking admits the sharper $\sqrt{d_{\mathrm{tot}}}$ dependence on total delay. We introduce a scheduler-side conditional-energy interface that separates rate adaptation from the one-point perturbation filtration and handles the dependent importance weights created by randomized admission. Under the same semi-clairvoyant oracle and pathwise hard-capacity contract, this yields an untuned learner whose delay term scales as $O(\sqrt{E_C d_{\mathrm{tot}}})$, with only an explicit restart factor $E_C$; a public constant-factor peak bound removes this factor while $d_{\mathrm{tot}}$ remains unknown. Under strong convexity, the same interface yields the temporal cost $H_A(d)=\sum_t σ_t/(A+t)$. Two delay vectors with identical delay multisets, $d_{\mathrm{tot}}$, $σ_{\max}$, and capacity can nevertheless have polynomially different minimax regret, showing that timing matters under curvature even when aggregate delay summaries agree. Finally, a continuous hard family converts tracking capacity into a zeroth-order query budget and gives a complementary capacity-starvation lower endpoint. The upper bounds require $C\ge \ln T+1$ and do not constitute a complete capacity minimax characterization.

cs.LG↗

arXiv2026

Bound states in doped charge transfer insulators

Understanding the physics of doped charge transfer insulators is the most important problem in high-temperature superconductivity. In this work, we show that an in-gap bound state emerges from the localized hole of the doped charge transfer insulator. We propose an approximate ground state wavefunction based on one localized Zhang-Rice singlet and the Neel state. By calculating the excitation states with one hole added and removed from this ground state, we successfully identify the existence of bound states inside the charge transfer gap. This feature is further confirmed by a Lanczos calculation based on matrix product states (MPS) for a system of $4\times4$ CuO$_2$ unit cells. How these bound states evolve into metallic states is further discussed. Our findings identify the key component of recent STM results on lightly doped Ca$_2$CuO$_2$Cl$_2$ and provide a new understanding of hole-doped charge transfer insulators.

cond-mat.supr-con↗

arXiv2026

Reactive polar mesogenic self-assembly approach enables domain-programmable polymer ferroelectrics

Ferroelectric polymers combine switchable polarization with the processability of soft materials, but their development has been dominated by poly(vinylidene fluoride) and related fluoropolymers, whose crystalline polar phases restrict mechanical compliance and domain design with spatial precision. Here we establish a generic design principle for creating intrinsically flexible ferroelectric liquid-crystal polymers through reactive polar mesogenic self-assembly. The approach creates polyfluoroalkyl-free polymer films in which robust ferroelectric order arises from liquid-crystalline molecular organization rather than crystalline phase formation. By transferring ferroelectric order from fluid mesogenic states into polymer networks, the resulting materials combine mechanical adaptability with programmable polar architectures. Especially, the photoalignment technology enables these polar states to be organized into pixelated domain architectures. This work establishes a design space towards soft ferroelectric polymers that integrate molecularly programmed polar order, mechanical tunability and environmentally conscious chemistry, expanding the design space of adaptive materials for flexible electronics, wearable systems and soft robotics.

cond-mat.soft↗

arXiv2026

Representation Handoffs for OpenArm-Based Laboratory Mobile Manipulation

Open-source robotics and foundation models have lowered the barrier to embodied AI, yet language-guided laboratory automation still requires reliable alignment from instructions and observations to safe actions. This field report presents an OpenArm-based mobile manipulation prototype for laboratory-style tasks, built by integrating dual OpenArm manipulators with a mobile base, vertical slide, RGB-D sensing, lidar-based mapping, ROS2/MoveIt execution, and profile-defined skill interfaces. The system is organized around representation handoffs: natural language requests are constrained into registered skill calls, sensor observations are grounded into maps and object poses, object priors provide role and skill constraints, and runtime bindings compile validated skills into executable motion goals. We use dry-run traces and startup checks to evaluate this integration path, showing how the prototype exposes missing calibration, incomplete object assets, and unfinished real-scene visual grounding as explicit deployment blockers. These intermediate representations serve as practical debugging interfaces for integrating language, perception, planning, and robot safety in embodied systems.

cs.RO↗

arXiv2026

Spectral gaps for noncompact hyperbolic surfaces with linearly many cusps

We construct complete finite-area noncompact hyperbolic surfaces with linearly many cusps and a uniform spectral gap. More precisely, for every $θ>0$, we construct a sequence $S_{g,n(g)}\in\mathcal{M}_{g,n(g)}$ such that $\lim\limits_{g\to\infty}\frac{n(g)}{g}=θ$ and the spectrum of the Laplacian has a uniform gap above zero. The construction is based on explicit expanding $(1,3)$-graphs, viewed as combinatorial skeletons for pants decompositions. We also establish a Steklov-type upper bound showing that expansion cannot persist when the number of boundary vertices is much larger than the genus.

math.DG↗

arXiv2026

Lithium as a Signpost for Compact Object Binary Candidates in the LAMOST Medium Resolution Survey

Binary systems with black hole or neutron star companions are often associated with lithium enhancement. Gaia NS1, a recently discovered neutron-star binary with a lithium-enhanced main-sequence companion, demonstrates the potential of lithium as a signpost for identifying compact object binaries in existing spectroscopic surveys. In particular, we aim to use lithium as a signpost to find compact object binaries similar to Gaia NS1 in the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) Medium Resolution Survey (MRS). From LAMOST MRS, we selected 4441 metal-poor main-sequence stars like Gaia NS1, measured the Li 6707 Å equivalent width, and then identified a sample of 33 stars with strong Li absorption. We used radial velocity variation and astrometric binarity signatures from Gaia, narrowing the sample to 3 candidates. We identified one of these candidates as an eclipsing binary and demonstrated that massive companions for the other two are unlikely via follow-up spectroscopy.

astro-ph.SR↗

arXiv2026

PertReason: A Knowledge-Grounded Benchmark and Framework for Cell-State-Conditioned Mechanistic Reasoning of Perturbation Effects

Evaluating machine learning in scientific domains requires separating correct predictions from correct reasons under realistic distribution shifts. We introduce PertReason, a knowledge-grounded benchmark and framework suite for cell-state--conditioned reasoning about perturbation effects. At its core, PertReasonQA is a benchmark that tests whether models can generate mechanistically faithful explanations while remaining robust to complex shifts, such as new cells and unseen perturbations. PertReasonQA combines single-cell genetic and chemical perturbation data across multiple cellular contexts with knowledge graphs, and dynamically conditions pathways on cell-specific basal states to avoid generic memorization. Evaluations on state-of-the-art models reveal systematic gaps between predictive accuracy and mechanistic reasoning. Specifically, these models exhibit failure modes largely invisible to standard benchmarks, such as deriving correct answers through flawed logic, ignoring cellular context, and generating directionally inconsistent mechanisms. As a reference probe of the benchmark, we present PertReasonLM, a large language model trained to align outcome predictions with context-specific mechanistic reasoning. Our model targets the identified failure modes by grounding rationales in context-specific pathways and tightening agreement between outcomes and mechanisms. Together, we provide a diagnostic framework for exposing and mitigating failures in faithful reasoning in data-rich scientific systems.

cs.LG↗

arXiv2026

Vanishing Theorems and Complex Structures on Non-Classical Flag Domains

We prove that every nontrivial line bundle on a compact quotient of a non-classical flag domain has no nonzero global sections. The proof first establishes the Green--Griffiths--Kerr conjecture by showing that the curvature of every nontrivial locally homogeneous line bundle has a negative direction, and then extends this property to arbitrary line bundles by decomposing their curvature into a homogeneous part and a seminegative correction term. We also establish several equivalent geometric and root-theoretic characterizations of non-classical flag domains. As consequences, their compact quotients are not in Fujiki class $\mathcal C$, contain no nonzero effective divisors, admit no nonconstant meromorphic functions, and have algebraic dimension zero. When $D=G_\R/V$ is non-classical and $G_\R$ is of Hermitian type, we construct another natural $G_\R$-invariant complex structure on the underlying differentiable manifold of $D$. The resulting classical flag domain has projective compact quotients. Thus the same differentiable manifold admits two invariant complex structures with opposite algebro-geometric behavior: one gives a projective manifold, whereas the other gives a non-classical quotient with the vanishing and non-algebraicity properties above.

math.AG↗

arXiv2026

Sections of Hodge bundles I: Global theory and applications to period maps

We study global sections of Hodge bundles arising from two complementary constructions: a deformation-theoretic construction, which yields global geometric consequences for period maps, and a construction from the matrix representation of the image of the period map, which provides an explicit Euclidean realization. Combining these perspectives, we prove that the image of the lifted period map on the universal cover is contained in a complex Euclidean subspace of the period domain, thereby giving a partial solution to a conjecture of Griffiths on the global behavior of period maps. As an application, we construct a global complex affine structure on the Teichmüller space of Calabi--Yau type manifolds.

math.AG↗

arXiv2026

Sections of Hodge bundles II: Deformation of $(p,p)$-classes and applications to Kähler geometry

Let $(X,ω_0)$ be a compact Kähler manifold and $\mathcal X\to B$ its Kuranishi family, where $B$ may be singular and $\dim_{\C}B\ge1$. Using explicit sections of Hodge bundles, we define an intrinsic period map and a Hodge map parametrizing nearby $(p,p)$-classes. For deformations over irreducible analytic bases, we introduce two flat extensions of Kähler cones defined by the reference and moving Hodge connections. The extension associated with the reference connection admits explicit positive representatives and yields uniform upper semicontinuity, while that associated with the moving connection identifies the Kähler cones away from a countable union of proper analytic subsets and admits an explicit expression in terms of the period map and the Beltrami differential. These constructions provide a description of Kähler cones through analytic cycles and yield both local and large-scale Kähler stability without assuming unobstructedness. As further applications, we generalize Green's density criterion to strong algebraic approximation and to the approximation of real $(p,p)$-forms. We also obtain an intrinsic analytic description of Hodge loci, leading to a Beltrami-differential criterion for the variational Hodge conjecture.

math.AG↗

arXiv2026

Socratic agents for autonomous scientific discovery in high-dimensional physical systems

The automation of scientific discovery has reached an inflection point. While AI systems now operate instruments, optimize parameters and generate hypotheses, most remain procedural: they execute workflows fixed by human designers. True autonomous science demands epistemic autonomy--the capacity to construct, challenge and revise physical explanations in response to evidence. Here we introduce AHOIS, a multi-agent AI scientist that embeds Socratic midwifery into closed-loop experimentation. A physics-critic agent interrogates hypotheses through causal questioning, constraint checking, counterexample generation and falsification-criteria formulation. We evaluate AHOIS on a real multimode-fibre optical platform, a high-dimensional system with complex wave transformations, indirect detection, environmental drift and multi-modal acquisition. Without prior encoding schemes, classifiers or speckle models, the system autonomously proposed and validated a random-interference encoding hypothesis, discovered task-adaptive sparse-measurement strategies, diagnosed distinct failure modes (encoding instability, fluorescence contamination and detector noise) and translated a published imaging protocol into an executable workflow on a non-original configuration. The discovered encoding yielded 16x16 measurements with effective rank 56.9 and classification accuracies of 76.97% on MNIST and 83.17% on Fashion-MNIST. Ablations show that Socratic interrogation improves physical consistency, hypothesis completeness, uncertainty calibration and experimental-plan validity. These results establish a route from workflow automation towards evidence-grounded, self-correcting autonomous discovery in complex physical environments.

cs.AI↗

arXiv2026

SeekRBP: Leveraging Sequence-Structure Integration with Reinforcement Learning for Receptor-Binding Protein Identification

Motivation: Receptor-binding proteins (RBPs) initiate viral infection and determine host specificity, serving as key targets for phage engineering and therapy. However, the identification of RBPs is complicated by their extreme sequence divergence, which often renders traditional homology-based alignment methods ineffective. While machine learning offers a promising alternative, such approaches struggle with severe class imbalance and the difficulty of selecting informative negative samples from heterogeneous tail proteins. Existing methods often fail to balance learning from these ``hard negatives'' while maintaining generalization. Results: We present SeekRBP, a sequence--structure framework that models negative sampling as a sequential decision-making problem. By employing a multi-armed bandit strategy, SeekRBP dynamically prioritizes informative non-RBP sequences based on real-time training feedback, complemented by a multimodal fusion of protein language and structural embeddings. Benchmarking demonstrates that SeekRBP consistently outperforms static sampling strategies. Furthermore, a case study on Vibrio phages validates that SeekRBP effectively identifies RBPs to improve host prediction, highlighting its potential for large-scale annotation and synthetic biology applications.

q-bio.GN↗

arXiv2026

Recency/Frequency Adaptive KV Caching for Large Language Model Serving

Key-value (KV) caching is a powerful technique for accelerating large language model inference and generation. Inference workloads are large and diverse, which makes them difficult to cache effectively. Existing cache management strategies adopt the least-recently-used policy for evicting cache blocks. However, LRU leads to multiple unrelated workloads flushing each other's caches. To address this, we integrate adaptive caching that dynamically allocates cache space between recently and frequently occurring KV blocks. Evaluations show that it improves the KV cache hit rate by up to 10.8% and reduces time to first token by up to 12.6% over naive vLLM on synthetic document question answering workloads, and 2.1% and 2.0% respectively on real-world conversation workloads. The method generalizes well to batch inference and demonstrates clear interpretability while effectively accommodating diverse workloads.

cs.DC↗

arXiv2026

STC: Reversible Digit-Context Decomposition for BWT-Family Text Compression

Burrows-Wheeler-transform-based compressors rely on local context regularity, but structured text also contains dates, counters, identifiers, coordinates, and other digit runs whose values vary differently from their surrounding tokens. STC is presented as a new algorithm found by the authors through the self-evolving AI system zeelin. It is a practical BWT-family compressor that separates this source of variation before the component BWT stage. It replaces digit runs in the main stream with an unambiguous placeholder and stores the removed digits in length- and context-conditioned side streams. The side streams use stable bucket ordering and compact digit packing, so the decoder can reconstruct the original run order from the normalized main stream without storing a separate permutation. The resulting components are encoded by a fixed internal BWT/M03-style component coder. On enwik9, STC produces a 157,388,188-byte archive with a 183,174-byte decoder source package, giving a local LTCB-style total of 157,571,362 bytes. A full-enwik9 same-coder ablation shows that the digit-context decomposition reduces the archive by 2,629,561 bytes relative to the no-split control. The result is locally verified by full decode and SHA-256 matching; official benchmark status requires independent maintainer-side verification.

cs.IT↗

arXiv2026

Nearly optimal spectral gaps for random Belyi surfaces

In this paper, we show that a random hyperbolic surface in the Brooks-Makover model has a spectral gap greater than $\left(\frac{1}{4}-\frac{c}{\log n}\right)$ for some universal constant $c>0$ , confirming the nearly optimal spectral gap conjecture in this model.

math.SP↗