arXiv subjects

Ke Wang

Publications and source records attributed to Ke Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Energy extraction from Kerr--Bertotti--Robinson black holes: polar region collapse and the capture of negative energy protons

The Kerr-Bertotti-Robinson black hole is an exact Einstein-Maxwell solution describing a rotating black hole in an asymptotically uniform magnetic field. It differs from the Kerr black hole in the Wald solution: the magnetic field lines are no longer cylindrical, with the constant-$A_ϕ$ lines saturating at large distances, and the interface $\vec{E}\cdot\vec{B}=0$ is no longer a cone but contracts with radius and eventually disappears. Determining the acceleration of charged particles pointwise, we find that protons are driven outward and electrons inward in the polar region, and vice versa in the equatorial region; the polar region shrinks as the magnetic field grows. Captured protons carry negative energy and angular momentum, whereas captured electrons carry positive energy and angular momentum. Fixing the capture domain by the gauge-invariant flux criterion and integrating over the $(r,θ)$ plane, we find that in weak magnetic fields the black hole gains mass, while in strong magnetic fields energy extraction occurs. Energy extraction can be achieved under the usual spherically symmetric density, without introducing the anisotropic density distribution required in the Wald solution for a Kerr black hole.

gr-qc↗

arXiv2026

When Better Gets Worse: Improvement Fidelity for Self-Improving Agents in Adaptive Worlds

Self-improving agents increasingly rely on proxy verifiers to choose policy updates, yet deployment can change the world in which those updates are evaluated. An update that looks better to the verifier can therefore become worse after deployment even when the verifier ranks policies well overall. We formalize this gap as Improvement Fidelity, which asks whether proxy improvements preserve the sign and ordering of deployment improvements over the updates an improvement process actually proposes. We show that global policy accuracy need not guarantee update fidelity: operator shift and deployment response can create update-level errors, while candidate margins determine whether those errors change the replacement decision. We introduce PIVOT-KG, a paired, decision-aware validator that allocates scarce high-fidelity evaluation according to the expected reduction in selection regret per unit cost. Across 90 held-out roots in Leduc, Kuhn, and Melting Pot, proxy and deployment optimal sets are disjoint in 51 cases. In an eight-candidate HighwayEnv stress test, PIVOT-KG reduces mean improvement-selection regret from 0.0435 under the exact Uniform validation rule to 0.0055 at the primary budget. Together, these results show why reliable self-improvement should evaluate proposed improvements in the worlds they induce, while providing a practical rule for allocating scarce deployment evidence when it can affect the replacement decision.

cs.AI↗

arXiv2026

GameSmith: Scaling Zero-Sum Self-Play with Synthesised Games

Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to general reasoning. Existing pipelines, however, rely on a handful of hand-written games and on advantage estimators tied to them, so both the supply of games and the advantage estimate on a large corpus limit scaling. We present GameSmith, which scales both. An LLM synthesises games inside templates whose reward, observation, and hidden-information code is fixed. Each game is verified by seed-paired replay and by an independent re-implementation of its specification, then expanded into encoding, rule, and size variants, giving 26 new games in 29 families and 336 verified variants from the three SPIRAL templates. The model is distilled from the self-play of a larger teacher (Qwen3-32B) on these games and then trained by self-play reinforcement learning with CRAB, an advantage estimator that credits each decision against a shrinkage estimate of the expected return of its position and is defined on every variant. On Qwen3-4B-Base, distillation raises the 13-benchmark mean pass@1 from 45.5 to 62.5, above the three SPIRAL games at the same budget, and self-play with CRAB reaches 64.9. The ordering holds across models, and transfer grows with the number of game families.

cs.LG↗

arXiv2026

Polynomial area growth for special Kähler metrics

Let $g=w|dz|^2$ be an affine special Kähler metric on a punctured disc and let $Ξ=q(z)\,dz^3$ be its associated holomorphic cubic differential. We prove that if the $g$-area of concentric punctured neighbourhoods grows at most polynomially, then $q$ is meromorphic at the puncture. More precisely, an area bound of order $\eps^{-N}$ forces every pole of $q$ to have order strictly less than $N+3$. Hence an essential singularity of the cubic differential rules out every polynomial area upper bound. The proof associates to $(g,Ξ)$ a curvature $-1$ conformal pseudometric and applies the Ahlfors--Schwarz lemma, followed by the submean inequality for holomorphic functions. We also discuss the identically zero cubic differential and the borderline logarithmic growth responsible for the strict pole-order bound.

math.DG↗

arXiv2026

Concentration of bounded sparse chaoses and sparse Khatri-Rao embeddings

We establish moment and mixed-tail inequalities for fixed-order decoupled homogeneous chaoses generated by independent, centered, sparse bounded random variables. Our bounds apply to arbitrary real rectangular coefficient tensors and describe the fluctuation scales through weighted slice and partition norms, with a Bennett-type logarithmic improvement in the largest-entry term. As an application, we derive guarantees for sparse Khatri--Rao embeddings that explicitly account for sparsity and input geometry.

math.PR↗

arXiv2026

Replica Thresholds for Stripeless Erasure Coding Based on Symmetric Block Designs

This paper investigates a fundamental question in stripeless erasure coding based on symmetric balanced incomplete block designs (SBIBDs): how many replicas per object are precisely required to guarantee recovery from any set of at most $p$ node failures? We refine the known sufficient recovery guarantee for the generalized SBIBD $(v,k,λ)$ construction. One replica is necessary and sufficient for $p=1$, and $q=λ(p-1)+2$ replicas ($q\le k$) guarantee recovery for $p\ge2$. Recovery takes one round for $p=2$ and at most two rounds in general. To study the tightness of this count, we define the universal replica threshold $q^\ast(A,p)$ for a fixed zero-diagonal SBIBD representative~$A$. We determine $q^\ast(A,p)$ for $p=1$ and $p=2$. For $p\ge3$, we identify pairwise separated sets and private target permutations as the structures determining whether the sufficient count is tight or can be further reduced. These conditions give exact thresholds for all but the case where $λ>1$ and $A$ contains no pairwise separated set of size~$p$. We improve the bounds for this remaining case and leave its exact threshold open. Finally, we give sufficient conditions for pairwise separated sets and show how affinity relabeling can realize private target permutations.

cs.IT↗

arXiv2026

Word Length Formulae, Normal Forms, Conjugation and Root-finding Algorithms in Surface Groups

In this paper, we mainly study the following symmetric presentation of the surface group $$π_1(Σ_g)=\left\langle c_1,\dots, c_{2g}\mid c_1\cdots c_{2g}c_1^{-1}\cdots c_{2g}^{-1}\right\rangle.$$ For every nontrivial element $x\in π_1(Σ_g)$ and $k\geq 2$, we obtain a uniform representative of the normal forms $\mathfrak{nf}(x^k)$ of $x^k$ under the length-lexicographical order: $$\mathfrak{nf}(x^k) = \overline{LW^{k-2}R}.$$ Building on this result, we establish a new relation among these normal forms, and then derive the following three formulae related to the word length: $|x^2|>|x|$; $|x^k|=(k-1)(|x^2|-|x|)+|x|$; $\lim_{k\to\infty}\frac{|x^k|}{k}=|x^2|-|x|$. Furthermore, we extend these results to obtain a coarser analogue for every minimal geometric presentation. We then define normal forms of conjugacy classes in $π_1(Σ_g)$ and provide a criterion for determining the conjugacy of group elements. As a consequence, we provide efficient algorithms for solving the root-finding and conjugacy problems. Finally, we present applications to the computation of several growth rates.

math.GT↗

arXiv2026

Beyond Lindblad Approximation: Rigorous Guarantees for Thermal and Ground State Preservation under System-Bath Interactions

We establish rigorous guarantees for quantum thermal and ground state preparation via system-bath interactions beyond the Lindblad approximation regime. In contrast to existing analyses, we show that the system-bath coupling strength need not vanish with the target accuracy: even when the coupling strength is chosen independently of the desired precision, the induced quantum channel admits the target thermal and ground states as approximate fixed points with arbitrarily small error. Our analysis develops new techniques to control all orders of the Dyson expansion and the associated multidimensional operator Fourier transforms, thereby going beyond the leading-order Lindbladian description. Building on these estimates, we derive an improved end-to-end complexity analysis and provide rigorous guarantees for thermalization and cooling outside the conventional Lindblad approximation regime. Numerical simulations further demonstrate the robustness of the system-bath interaction framework over a broad range of coupling strengths, including regimes where the Lindblad approximation is no longer accurate.

quant-ph↗

arXiv2026

Measuring vacancy-type defect density in monolayer semiconductors

Two-dimensional (2D) materials have attracted wide-spread interest due to their unique and tunable properties. Their optoelectronic, mechanical, and thermal properties are greatly influenced by crystal defects, which are, in turn, used to control these properties. However, experimental quantification of the density of defects, whether deliberately introduced or inherent, is very difficult in these atomically thin materials. Here we show that helium atom micro-diffraction can be used to measure the defect density in ~15x20um monolayer MoS2, a prototypical 2D semiconductor, quickly and easily compared to standard methods. We present a simple analytic model, the lattice gas equation, that captures the relationship between atomic Bragg diffraction intensity and defect density. The model, combined with ab initio scattering calculations, shows that our technique can immediately be applied to a wide range of 2D materials, independent of sample chemistry or structure. Additionally, wafer-scale characterization is immediately possible.

physics.app-ph↗

arXiv2026

From Phase Transition to Systemic Failure: A Decoupled Analytics Framework for GNN Robustness

Data quality is a major bottleneck for the reliable deployment of graph neural networks (GNNs) in real-world graph mining tasks. Among various sources of degradation, label noise and feature distribution shift (hereafter referred to as distribution shift) are two common yet fundamentally different challenges. To study their effects under controlled conditions, this paper constructs a synthetic homophilic graph regression benchmark in which the two factors can be manipulated separately. A total of 41 configurations and 410 runs are conducted to evaluate the behavior of representative GNN models under varying noise and shift conditions. The results show two distinct patterns. First, under additive label corruption, performance remains relatively stable over a broad range of noise settings and begins to deteriorate sharply only after an observed transition region around the 50 percent noise ratio. Second, under extreme feature distribution shift, all tested models suffer substantial degradation, with test MSE increasing by 48 times to 316 times and correlation dropping by 73 percent to 89 percent. These findings suggest that, in the present controlled setting, GNNs are considerably more tolerant to moderate label perturbation than to severe distribution mismatch. The study provides a controlled empirical baseline for understanding how data quality affects GNN-based graph mining systems and offers practical implications for deployment-oriented monitoring and model maintenance.

cs.LG↗

arXiv2026

Hindsight-Anchored Policy Optimization: Learning Through Hindsight with Thompson Sampling-Inspired Adaptive Gating

Reinforcement Learning with Verifiable Rewards improves reasoning in large language models, yet on-policy learning often suffers from cold-start challenges in sparse-reward settings. Recent mixed-policy approaches address this by combining off-policy teacher data with on-policy training. However, simply combining these introduce a persistent off-policy gradient mass that risks training collapse and instability. To address this challenge, we propose Hindsight-Anchored Policy Optimization (HAPO), a framework that allows teacher intervention to act as a temporary support. HAPO employs Beta-Binomial confidence gating, an adaptive gating mechanism that decides when to open the gate for teacher intervention. The intervention operates with Synthetic Success Injection, which replaces the group's lowest-reward rollout with a verified teacher trajectory. We also introduce adaptive threshold annealing, which gradually retracts the support and restores on-policy training within a finite horizon to mitigate persistent off-policy drift. We demonstrate that HAPO can be layered on top of existing mixed-policy methods in a generalizable manner. Across six math reasoning benchmarks and two model scales, HAPO improves the average accuracy of three major mixed-policy methods while maintaining training stability.

cs.LG↗

arXiv2026

Cognitive Link-Flexible FTN-OTFS Design for High-Mobility LEO Satellite Communications

Low Earth orbit (LEO) satellite links are challenged by pronounced signal-to-noise ratio (SNR) variation and severe Doppler shifts. Although mobility robustness is provided by orthogonal time frequency space (OTFS) modulation, the rate-reliability tradeoff of faster-than-Nyquist (FTN) signaling is constrained by fixed packing. In this paper, an SNR-aware flexible FTN-OTFS framework is proposed, in which the packing factor is adapted to changing link conditions under a prescribed reliability requirement. Link-state perception and packing decisions are supported by estimated SNR and offline reliability thresholds, with constant decision complexity achieved through a fixed-mode lookup table. Elevation-dependent propagation, fractional Doppler, and FTN-induced interference are incorporated into the signal model, while colored noise is accommodated by covariance-aware linear minimum mean-square error detection. Throughput and bit error rate are analytically characterized, and energy efficiency and peak-to-average power ratio are evaluated. Simulation results show that conservative packing preserves reliability under unfavorable conditions, while denser signaling improves throughput as the link strengthens. These findings highlight the potential of cognitive FTN adaptation for efficient future LEO wireless communications.

cs.IT↗

arXiv2026

Decoupling Error Attribution in Cloud-Native Graph-RAG: A Data Integrity Diagnostic Framework

Graph-RAG systems often assume pristine data quality, overlooking the severe impact of perturbations in cloud-native databases. This paper proposes a three-layer decoupled diagnostic framework to orthogonally attribute system errors to reasoning loss, Knowledge Graph (KG) defects, and Cypher generation errors. Evaluated on a spatio-temporal ecological KG of the Southeastern Tibet region with eight defect types, results reveal that data integrity, rather than algorithmic reasoning, is the dominant performance bottleneck, with structural defects degrading system accuracy from 0.93 to 0.39. Crucially, we observe a masking-like phenomenon termed the Parametric Knowledge Masking Effect (PKME), suggesting LLMs compensate for broken retrieval paths using internal memory. This shrinks apparent query generation errors by over 70 percent, obscuring actual storage deterioration and increasing the risk of false negatives for automated monitoring. This work provides a quantitative foundation for auditing and optimizing data integrity in cloud-based information fusion systems.

cs.IR↗

arXiv2026

Electrodynamic Signatures of Bogoliubov Fermi Surfaces in Moiré Graphene

Understanding the superfluid stiffness $D_s$ is a central problem in flat-band superconductivity. It is often interpreted together with spectroscopic probes such as tunneling, since both ultimately reflect the same superconducting quasiparticles. Their relationship is nevertheless complicated in flat bands, where quantum-geometric contributions to $D_s$ must be included. The situation in moiré graphene is particularly complex: tunneling experiments in some cases reveal an evolution between V- and U-shaped spectra together with a quite universal finite zero-bias conductance (ZBC). These two phenomena along with others have been argued to suggest the presence of a Bogoliubov Fermi surface (BFS), often generated by finite-momentum pair-density-wave (PDW) superconductivity. In this paper we calculate the superfluid stiffness in the presence of such a PDW. Virtual interband processes provide the familiar positive geometric contribution that gives phase rigidity to the flat-band condensate, allowing superconductivity to survive even in the presence of a BFS. At the same time, the multiband PDW pair structure enables the gapless BFS quasiparticles to respond to a phase gradient despite the negligible ordinary flat-band velocity. Their resulting counterflow reduces the stiffness even at zero temperature. As an experimentally accessible consequence, we predict a correlated evolution of the residual ZBC and the low-temperature stiffness: enhanced zero-bias spectral weight should accompany reduced phase rigidity. Observation of this correlation would support the presence of a BFS and indicate that the residual zero-energy states are intrinsic.

cond-mat.supr-con↗

arXiv2026

A Self-Adaptive First-Principles Approach for Magnetic Excited States

The profound impact of excited magnetic states on the intricate interplay between electron and lattice behaviors in magnetic materials is a topic of great interest. Unfortunately, despite the significant strides that have been made in first-principles methods, accurately tracking these phenomena remains a challenging and elusive task. The crux of the challenge that lies before us is centered on the intricate task of characterizing the magnetic configuration of an excited state, utilizing a first-principle approach that is firmly rooted in the ground state of the system. We propose a versatile self-adaptive spin-constrained density functional theory formalism. By iteratively optimizing the constraining field alongside the electron wave function during energy minimization, we are able to obtain an accurate potential energy surface that captures the longitudinal and transverse variations of magnetization in itinerant ferromagnetic Fe. Moreover, this technique allows us to identify the subtle coupling between magnetic moments and other degrees of freedom by tracking energy variation, providing new insights into the intricate interplay between magnetic interactions, electronic band structure, and phonon dispersion curves in single-layered CrI$_3$. This new methodology represents a significant breakthrough in our ability to probe the complex and multifaceted properties of magnetic systems.

cond-mat.mtrl-sci↗

arXiv2026

Enhancing Multimodal Emotion Recognition via Multi-Feature Encoding and Attention-Based Fusion

Multimodal emotion recognition has attracted growing interest due to its importance in human-computer interaction, remote education, and healthcare. This paper proposes a novel multimodal emotion recognition framework that integrates rich audio and visual feature extraction with an attention-based fusion strategy. For audio, we extract three complementary feature types: semantic embeddings from Wav2Vec2, MFCC features, and statistical acoustic descriptors such as pitch, energy, and rhythm. These are aligned and fused via a BiLSTM to capture temporal dependencies. For video, we propose a ResNet50-BiLSTM architecture that combines deep residual learning and sequential modeling to extract expressive spatiotemporal features from facial sequences. To enhance multimodal synergy, we introduce a feature-level fusion mechanism based on multi-head attention, allowing the model to adaptively weigh contributions across modalities. Experiments conducted on the MELD and IEMOCAP datasets demonstrate that our model significantly outperforms baselines in both accuracy and robustness. Furthermore, ablation studies show that the attention-based fusion strategy significantly improves performance in unbalanced data settings. Our findings suggest that the proposed framework effectively captures diverse emotional cues from speech and visual expressions, and offers a practical and generalizable approach for real-world multimodal emotion recognition tasks.

cs.CV↗

arXiv2026

OPUS-V2: Bridging the Gap between Sparse Points and Dense Voxels

The point-based occupancy prediction paradigm has achieved an attractive trade-off between accuracy and efficiency by modeling 3D space sparsely. However, its predictions inherently mismatch the dense voxel-based occupancy required by self-driving systems, necessitating hand-crafted heuristics during training and inference that limit final performance. To overcome these limitations, we propose OPUS-V2, a novel framework built upon the pioneering OPUS (occupancy prediction using a sparse set) point-based approach. OPUS-V2 incorporates a lightweight point-voxel transformation (PVT) module behind the decoder to adaptively map sparse predictions into the dense voxel space, eliminating the need for suboptimal operations and improving model accuracy. Furthermore, our architecture decouples feature and occupancy generation processes, allowing OPUS-V2 to adapt to arbitrary occupancy resolutions. OPUS-V2 achieves a state-of-the-art rayIoU of 44.0 on the Occ3D dataset. On the more challenging OpenOccupancy dataset, it attains a competitive 16.4 mIoU while running in real time at 20.6 FPS.

cs.CV↗

arXiv2026

Dense Cores in the Vicinity of an HII Region

Massive stars strongly influence their surroundings through radiative and mechanical feedback, but its effects on dense gas structures at sub-pc scales remain poorly constrained. We investigate how feedback from a newly formed massive star affects dense cores in the filamentary molecular cloud IRAS 18530+0215. We analyze ALMA Band 6 observations of 1.3 mm dust continuum and DCN, N$_2$D$^+$, and $^{13}$CS line emission, together with VLA K-band continuum and NH$_3$ observations. Dense cores are identified with astrodendro, and their temperatures, masses, velocity dispersions, and virial parameters are derived. The dynamical state of the ultra-compact H II region is examined through energy and pressure estimates. The H II region has a radius of $\sim$0.1 pc and an expansion velocity of $\sim$2.5 km s$^{-1}$, corresponding to a shell dynamical age of $\sim$0.06 Myr. DCN and $^{13}$CS cores are concentrated near the H II region, whereas N$_2$D$^+$ cores preferentially lie farther away. Core temperatures and velocity dispersions decrease with projected distance from the H II region. Virial parameters increase within the inner $\sim$0.3 pc but decline sharply beyond this scale, while core masses show no significant trend with distance. Strong star formation signatures are found at $\sim$0.2 pc, whereas more distant regions still host quiescent, cold dense cores. The compact H II region appears trapped or choked within $\sim$0.1 pc, while its feedback extends to at least $\sim$0.3 pc. Within this region, feedback enhances core velocity dispersions, gas temperatures, and virial parameters, with no evidence that it promotes the formation of more massive dense cores.

astro-ph.GA↗