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Hao Feng

Publications and source records attributed to Hao Feng.

At least 19 recordsLinked to original sources
arXiv2026

Joint Antenna Geometry and Transmit Covariance Design for Near-Field Multicast ISAC with Pinching Antenna Arrays

PASS provide a flexible waveguide-based architecture for reconfiguring wireless propagation environments and creating geometry-dependent radiating apertures. This paper investigates a near-field multicast ISAC system enabled by a lossy multi-waveguide PASS, where a base station transmits a common message to multiple communication users while simultaneously sensing one or multiple targets. The PA positions along the waveguides and the feed-domain transmit covariance matrix are jointly designed to improve sensing accuracy under multicast communication constraints. We first develop a near-field multicast ISAC signal model that accounts for waveguide attenuation, equal-radiated-power operation, geometry-dependent free-space propagation, and monostatic sensing. Then, we derive the FIM for target parameter estimation and obtain a compact projected-Jacobian representation by exploiting the block-diagonal PA transfer structure. This representation reveals how the PA geometry and transmit covariance jointly affect the CRB. Based on this structure, we further characterize the per-waveguide and cross-waveguide FIM contributions, the loss-aperture tradeoff, the identifiability condition, and the communication-sensing phase conflict. To minimize the CRB, we formulate a joint PA-position and transmit-covariance optimization problem subject to a multicast rate constraint, a feed-power budget, and PA deployment constraints. An alternating optimization algorithm is developed, where the covariance subproblem is solved as a semidefinite program and the PA-position subproblem is handled by waveguide-wise block coordinate descent.

cs.IT↗
arXiv2026

HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better

We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image translation, and multi-image document understanding within a single end-to-end VLM. Building upon the lightweight architecture of HunyuanOCR-1.0, HunyuanOCR-1.5 does not redesign the backbone, but systematically improves both efficiency and capability. For efficiency, we adapt DFlash to OCR decoding, significantly reducing the latency of long structured outputs such as dense documents, tables, and formulas while preserving output distribution. Powered by DFlash, HunyuanOCR-1.5 achieves a 6.37x Transformer inference speedup and a 2.14x speedup under vLLM, delivering the fastest inference among lightweight OCR VLMs. For capability, we propose Agentic Data Flow, an agent-driven data construction system that transforms model weaknesses into executable data requirements and autonomously performs material search, quality verification, and pipeline development. It substantially improves long-tail capabilities in ancient-script OCR, fine-grained chart and table parsing, multi-image text-centric QA, low-resource multilingual parsing, and document hallucination evaluation. HunyuanOCR-1.5 ranks among the top-tier end-to-end OCR solutions on OmniDocBench v1.6 while achieving new performance milestones across these long-tail tasks. Combined with an upgraded pretraining and post-training recipe, HunyuanOCR-1.5 further extends its capability in high-resolution, long-context, and multi-task scenarios. Experiments demonstrate faster inference, broader OCR capability coverage, and the deployment advantages of a lightweight end-to-end model. We will release the model weights and training code to support future research and real-world OCR applications.

cs.CV↗
arXiv2026

FuncRoom-Agent: Sequential Feed-Forward 3D Functional Indoor Scene Generation

We introduce Function-Room Generation, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than merely visually plausible layouts. Existing agentic and executable methods improve controllability, but often depend on costly test-time generate--evaluate--revise loops, making functional room generation slow and computationally expensive. We address this challenge with three technical contributions. First, we design a recursive domain-specific language to effectively organize the hierarchical object compositions required by functional rooms, from room structure and major furniture to dense support-surface and nested small objects. It represents rooms as staged executable programs with explicit geometric and functional relations. Second, we propose a sequential feed-forward scene construction framework that distills recursive construction traces into a scene construction expert. At inference time, the expert writes executable DSL code stage by stage, and a deterministic executor directly instantiates each stage without teacher agents, online critics, or iterative repair. Third, we introduce ScenePRM, an execution-grounded process reward framework that improves the expert through reinforcement learning with functional, geometric, relational, and future-constructability feedback. We further establish a function-oriented benchmark and show state-of-the-art performance on both general indoor scene generation and function-room generation, achieving stronger functional completeness, relation correctness, geometric executability, and generation efficiency.

cs.CV↗
arXiv2026

Current Status and Prospects of Neutron Detection and Neutron/Gamma Discrimination Technologies in Fusion Applications

Fusion research is progressing from physics-oriented experiments toward reactor-oriented engineering applications, placing increasing demands on the accuracy and reliability of neutron measurements. This review summarizes the current status and prospects of neutron detection and neutron/gamma discrimination technologies for fusion applications. The main sources, energy characteristics, and measurement requirements of fusion neutrons are first reviewed, with particular attention to 2.45 MeV D-D and 14.1 MeV D-T neutrons. Four principal neutron detection methods, including nuclear reaction, nuclear recoil, nuclear fission, and neutron activation, are discussed together with the operating characteristics and applicability of scintillation, gas, semiconductor, and other specialized detectors. The roles of neutron/gamma discrimination are then examined in plasma diagnostics, safe operation monitoring, radiation protection monitoring, and fusion reactor design. The review shows that different detection methods and detector types have distinct advantages and application boundaries, and no single detector provides an optimal solution for all fusion measurement tasks. Liquid scintillators and some organic crystals remain important for fast-neutron measurement and neutron/gamma discrimination, whereas gas and semiconductor detectors serve complementary roles in thermal-neutron monitoring, compact detection, radiation tolerance, and fast-neutron spectrometry. Future development is expected to focus on high-performance detectors, material damage assessment, intelligent real-time discrimination, and multi-detector coordination with system-level diagnostic integration.

physics.ins-det↗
arXiv2026

Metric Reconstruction from Timelike Entanglement Entropy

Timelike entanglement entropy (TEE) provides a Lorentzian boundary probe of bulk geometry, but its use for metric reconstruction depends on the holographic prescription and on the extremal-surface branch selected by that prescription. We study this inverse problem for strip-shaped TEE data and make these dependencies explicit. In the complex-valued weak extremal surface (CWES) prescription, the time-width dependence of TEE determines an Abel density $H(W)$ on a selected real branch; for Bañados-Teitelboim-Zanelli (BTZ) black holes this gives an analytic reconstruction of the blackening factor once the singularity endpoint fixes the radial origin. After developing a forward numerical method for the complex-coordinate prescription, we formulate it as the main reconstruction scheme for the asymptotically $AdS_{d+1}$ backgrounds with $d\geq 2$. On a chosen complex branch, the time-width dependence of TEE supplies the Abel input that determines the TEE-accessible density, while one additional geometric anchor is required to convert that density into a definite radial metric profile. With UV or horizon-scale anchoring and rational continuation from the reconstructed complex-path samples, the method reproduces the benchmark BTZ, four-dimensional Schwarzschild, and Reissner-Nordström (RN) blackening factors. However, the Gubser-Rocha example shows that a single strip observable with a nontrivial spatial warp factor determines only one functional combination of the metric functions.

hep-th↗
arXiv2026

Equivalence of Stability Criteria for Multi-Fluid Stars

We prove a static criterion for radial zero modes in relativistic multi-fluid stars whose isentropic barotropic components are separately conserved and interact only through gravity. For a regular multi-parameter equilibrium family, the particle-number map is rank deficient if and only if a nontrivial physical radial zero mode exists. The proof establishes a one-to-one correspondence between particle-preserving infinitesimal equilibrium deformations and zero-frequency radial perturbations. We also construct the self-adjoint form realization associated with the coupled pulsation operator, prove that it has compact resolvent, show that its eigenspaces coincide with the physical radial-mode spaces, and establish continuity of every ordered squared mode frequency along the equilibrium family. It follows that every boundary point of strict radial stability within the equilibrium family satisfies the static rank condition. A representative two-fluid example shows that the static criterion and the dynamical zero-mode calculation yield the same critical curve. The rank condition therefore provides an equilibrium-based method for locating radial zero modes and determining their multiplicity.

astro-ph.HE↗
arXiv2026

Finite-n Estimate of Dedekind Numbers by Layer-Ratio Monte Carlo

Dedekind's problem counts monotone Boolean functions, equivalently downsets of a Boolean lattice. We recast this enumeration as a finite layer-ratio reconstruction problem for the Whitney numbers of the ranked ideal lattice. An exact adjacent-layer double count expresses each layer ratio through local averages of the number of addable elements and the number of removable elements. Reversible fixed-layer Markov chains estimate these averages and hence estimate the Dedekind number $M(n)$. Backtests at $M(8)$ and $M(9)$ calibrate seed-level variability under the fixed protocol and measure the observed Monte Carlo budget scaling. The resulting estimate probes the Whitney-number sequence of the ideal lattice. Although these rows have previously been described empirically as unimodal, the high-precision $n=9$ estimate has a shallow two-shoulder feature around the central rank, contrary to that empirical description; $n=11$ and $n=13$ center-window estimates show a larger-contrast analogous pattern. The protocol estimate for $M(10)$ is \[ \widehat M(10)=(8.9360\pm0.0010)\times 10^{78}, \] where the displayed uncertainty is the budget-based forecast scale from the cross-$n$ scaling law under the production budget.

math.CO↗
arXiv2026

UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters

Text and formulas constitute the core informational components of many documents. Accurately and efficiently recognizing both is crucial for developing robust and generalizable document parsing systems. Recently, vision-language models (VLMs) have achieved impressive unified recognition of text and formulas. However, they are large-sized and computationally demanding, restricting their usage in many applications. In this paper, we propose UniRec-0.1B, a unified recognition model with only 0.1B parameters. It is capable of performing text and formula recognition at multiple levels, including characters, words, lines, paragraphs, and documents. To implement this task, we first establish UniRec40M, a large-scale dataset comprises 40 million text, formula and mixed samples, enabling the training of a powerful yet lightweight model. Secondly, we identify two challenges when building such a lightweight but unified expert model. They are: structural variability across levels and semantic entanglement between textual and formulaic content. To tackle these, we introduce a hierarchical supervision training that explicitly guides structural comprehension, and a semantic-decoupled tokenizer that separates text and formula representations. Finally, we develop a comprehensive evaluation benchmark covering Chinese and English documents from multiple domains and with multiple levels. Experimental results on this and public benchmarks demonstrate that UniRec-0.1B outperforms both general-purpose VLMs and leading document parsing expert models, while achieving 2-9x speedup, validating its effectiveness and efficiency. Codebase and Dataset: https://github.com/Topdu/OpenOCR.

cs.CV↗
arXiv2026

SkelGen4D: Weakly-Supervised Skeleton-Based 4D Generation for Text-Driven Mesh Animation

We study 4D generation to synthesize temporally coherent sequences of 3D geometry for animation and content creation. In contrast to existing SDS-based optimization methods and video-driven animation approaches, we adopt a skeleton-driven animation framework aligned with standard industrial pipelines, which enables explicit control and editing. To this end, we propose SkelGen4D, a weakly supervised feed-forward framework for text-driven mesh animation that generates explicit skeleton motions without requiring per-frame skeleton annotations. SkelGen4D first recovers temporally consistent pseudo-skeletons from animated meshes via differentiable fitting, and then generates text-conditioned skeleton motion sequences in a feed-forward manner, further refined with Motion-GRPO to ensure temporally coherent, physically plausible, and articulated animation. We evaluate our method on two large-scale benchmarks, Truebones Zoo and Diffusion4D. Our results show that our weakly supervised skeleton modeling matches or surpasses fully supervised baselines while scaling to diverse object categories for high-quality text-driven mesh animation. Further, our method supports flexible motion editing and is aligned with standard animation production pipelines.

cs.CV↗
arXiv2026

Charge functions for all dimensional partitions

The charge functions for n-dimensional partitions are known for n=2,3,4 in the literature. In a recent work, we gave the expression for arbitrary odd dimension; here we further conjecture a formula for all even-dimensional cases. This conjecture is proved rigorously for 6D, and numerically verified for 8D.

math-ph↗
arXiv2026

ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats

Charts are a primary medium for conveying quantitative and relational information, yet systematically evaluating chart parsing models remains difficult. Existing benchmarks focus on narrow chart types and leave diagrammatic structures such as flowcharts and mind maps largely unaddressed, while models produce outputs in incompatible formats, and datasets rarely include the printed or hand-drawn images encountered in practice. To address these issues, we introduce ChartArena, a comprehensive bilingual benchmark covering eight chart families spanning both numeric charts and diagrammatic structures, each evaluated across three visual scenarios: digital renderings, printed photos, and hand-drawn photos. The dataset is built via a human-agent collaborative annotation pipeline with multi-stage human verification to ensure annotation reliability. To enable fair cross-model comparison, we further design a format-agnostic evaluation protocol that maps heterogeneous outputs into two canonical semantic spaces, a normalized triple view and a directed graph view, and scores them with structure-aware metrics. Through extensive evaluation of 26 leading MLLMs, we observe three consistent findings: (i) frontier proprietary models such as Gemini 3.1 Pro lead overall, yet the strongest open-source systems are rapidly closing the gap; (ii) document parsing models handle numeric charts reasonably but fall sharply behind on diagrammatic structures; and (iii) expert chart parsers remain limited to narrow chart families. Across all models, radar charts and hand-drawn scenarios stay especially challenging. These findings show that ChartArena exposes clear capability gaps and provides a unified foundation for future progress. ChartArena is publicly available at https://github.com/pspdada/ChartArena.

cs.CV↗
arXiv2026

Hybrid Wireless-Fed Pinching-Antenna Systems with Residual Self-Interference-Aware Optimization

Pinching-antenna systems (PASS) have recently emerged as a promising solution for enhancing coverage in high-frequency wireless communications by guiding signals through dielectric waveguides and radiating them via position-adjustable antennas. However, their practical deployment is limited by waveguide attenuation and the need for physical line installation, which restrict flexibility and coverage extension. To address these challenges, this paper proposes a hybrid wireless-fed PASS architecture, where a base station equipped with an antenna array provides adaptive directional transmission to a full-duplex amplify-and-forward relay employing a horn antenna to feed the waveguide. This hybrid design balances beamforming flexibility and low-complexity directional waveguide interfacing. Residual self-interference (SI) at the full-duplex relay is explicitly modeled to capture practical system impairments. Under this framework, a total power minimization problem is formulated subject to a quality-of-service constraint at the user equipment, involving the joint optimization of the pinching-antenna position, the relay amplification gain, and the base station transmit power. By exploiting the structure of the end-to-end signal-to-noise ratio, the optimal pinching-antenna position is first obtained in closed form by balancing waveguide attenuation and free-space path loss. Closed-form expressions for the optimal relay gain and transmit power are then derived. Numerical results under the adopted system-level model demonstrate that the proposed scheme reduces total power consumption compared with conventional benchmark systems, while providing a more realistic and robust design by accounting for residual SI.

cs.IT↗
arXiv2026

MORE: A Multilingual Document Parsing Benchmark and Evaluation

Multilingual documents encapsulate rich regional cultures, scientific discoveries, and historical records. Parsing this content into structured, machine-readable formats is critical for unlocking global knowledge. However, existing benchmarks predominantly focus on high-resource languages like English and Chinese, creating an evaluation blind spot concerning model performance on other languages. While recent Vision-Language Models (VLMs) claim support for hundreds of languages, the lack of ground truth makes it impossible to empirically verify these capabilities. To bridge this gap, we introduce MORE, a large-scale benchmark designed for multilingual document parsing evaluation. MORE distinguishes itself through three key dimensions: (1) Unprecedented Scale: It covers 149 languages, making it the most linguistically diverse benchmark to date; (2) Structural Complexity: Unlike previous works, it extends evaluation beyond plain text to include structural elements such as code blocks, tables, and catalogs; and (3) Data Authenticity: All samples are curated from real-world documents via a model-assisted, human-refined annotation pipeline. We evaluate state-of-the-art models using MORE, establishing new performance baselines for long-tail languages and validating the benchmark's effectiveness in diagnosing model capabilities in realistic, diverse scenarios. The MORE dataset will be available at https://github.com/zimoqingfeng/MORE.

cs.CV↗
arXiv2026

StrucTab: A Structured Optimization Framework for Table Parsing

Table parsing aims to convert table images into structured, machine-readable representations, a task requiring the joint perception of complex spatial layouts and textual content. While recent vision-language models (VLMs) enable end-to-end parsing, they typically rely on direct supervision of the final output, thereby bypassing the explicit intermediate reasoning that is crucial for understanding complex table structures. Furthermore, attempts to optimize these models using reinforcement learning (RL) are often hindered by unstable or ambiguous reward designs, limiting potential performance gains. To address these limitations, we propose StrucTab, a table parsing model learned through intermediate structural supervision and reward decomposition. At the modeling level, by decomposing the parsing process into human-inspired subtasks, such as row-column counting and merged-cell analysis, StrucTab progressively unifies them through a sequential reasoning strategy. At the optimization level, we introduce Uni-TabRL, a unified RL framework that leverages decomposed rewards (validity, structure, and content) to provide stable and informative optimization signals. Finally, at the evaluation level, we present TableVerse-5K, a large-scale, challenging benchmark encompassing diverse, real-world table scenarios. Extensive experiments demonstrate the state-of-the-art performance of StrucTab across all evaluated public benchmarks and significant improvements on TableVerse-5K, validating the effectiveness of explicit structural modeling and decomposed reward optimization. Code and benchmark are publicly available at https://github.com/VirtualLUOUCAS/StrucTab.

cs.CV↗
arXiv2026

Phase-Aware Localization in Pinching Antenna Systems: CRLB Analysis and ML Estimation

Pinching antenna systems (PASS) have emerged as a promising architecture for high-frequency wireless communications. In this letter, we investigate user localization in PASS by jointly exploiting the received signal amplitude and phase information. A complex baseband signal model is formulated to capture free-space path loss, waveguide attenuation, and distance-dependent phase rotation between the user and each pinching antenna. Based on this model, we derive the Fisher information matrix and closed-form Cramer-Rao lower bound and position error bound. The derived analysis reveals that the phase-induced Fisher information decays with the fourth power of the user-antenna distance, whereas the amplitude-induced information decays with the sixth power, explaining the fundamental advantage of phase-aware localization in typical PASS deployments. A maximum likelihood estimator is then developed and implemented through a two-stage procedure combining coarse grid search and Levenberg-Marquardt refinement. Numerical results show that the proposed estimator achieves low positioning error and generally outperforms the considered benchmarks under different noise powers, numbers of pinching antennas, and user locations. In the considered scenario, the proposed method achieves sub-meter-level accuracy over the evaluated service area and yields substantially lower positioning error than the amplitude-only benchmark.

cs.IT↗
arXiv2026

Measured-Pattern-Aware Pinching-Antenna Systems With Coupling-Efficiency Optimization

Pinching-antenna (PA) systems have been widely investigated as a flexible architecture for waveguide-enabled wireless transmission. Existing analytical models, however, often rely on isotropic radiation assumptions and simplified couplingefficiency settings, which may overlook two practical design factors: the geometry-dependent radiation pattern of each PA and the sequential extraction of guided power along the waveguide. In this paper, we propose a measured-radiation-pattern-aware PA framework that incorporates an externally obtained radiation pattern, waveguide attenuation, and coupling-dependent power extraction. For a single PA, the resulting placement rule balances directional gain, waveguide loss, and free-space path loss, leading to a coupling-efficiency threshold for outperforming a fixed isotropic antenna. For multiple PAs, we study phase-matched placement and coupling-efficiency design under both uniform and independently controllable coupling. The uniform-coupling case yields a one-dimensional optimality condition and reveals that the preferred coupling efficiency decreases as more phasematched PAs participate in coherent combining. The independently controllable case admits a closed-form power-allocation structure, where stronger effective directional channels receive larger radiated power fractions. Numerical results based on a representative measured PA radiation pattern demonstrate the importance of jointly accounting for measured-radiation-patternaware placement and coupling-efficiency optimization.

cs.IT↗
arXiv2026

Revisiting Shadow Detection from a Vision-Language Perspective

Shadow detection is commonly formulated as a vision-driven dense prediction problem, where models rely primarily on pixel-wise visual supervision to distinguish shadows from non-shadow regions. However, this formulation can become unreliable in visually ambiguous cases, where similar dark regions may correspond either to cast shadows or to intrinsically dark surfaces, making visual evidence alone insufficient for establishing a stable decision rule. In this work, we revisit shadow detection from a vision--language perspective and argue that robust prediction benefits from an explicit semantic reference beyond visual cues alone. We propose SVL, a Shadow Vision--Language framework that uses language as an explicit semantic reference to disambiguate shadows from visually similar dark regions. SVL aligns global image representations with shadow-related text embeddings through scene-level shadow ratio regression, and transfers this semantic guidance to dense prediction via global-to-local coupling and local patch-level constraints. Built on a frozen DINOv3 image encoder, SVL learns only lightweight projection and decoding modules, yielding a parameter-efficient design with less than $1\%$ trainable parameters. Extensive experiments on multiple shadow detection benchmarks, including dedicated hard-case evaluations, suggest strong overall performance and improved robustness under visually ambiguous conditions. Code is available at https://github.com/harrytea/SVL.

cs.CV↗
arXiv2026

Manipulation of information flow and thermodynamic performance in nonreciprocal quantum dot information engines

Quantum information engines leverage information as a thermodynamic resource to facilitate energy conversion. In the operation of such engines, the information flow between the working substance and the controller is pivotal, however, strategies for its efficient manipulation remain largely unexplored. Here, we investigate an autonomous information engine based on a double-quantum-dot setup, where a downstream dot coupled to two reservoirs acts as the working substance, and an upstream dot coupled to a single reservoir serves as the controller. By extending the second law of thermodynamics to incorporate the effects of nonreciprocal couplings between the dots and their electronic reservoirs, we develop a thermodynamic framework that allows us to demonstrate that nonreciprocity can significantly modulate the inter-dot information flow, thereby providing a robust control mechanism. We show that the influence of nonreciprocity can be equivalently understood through a mapping to an effective reciprocal system upon a reparameterization of chemical potentials and the electron-electron coupling strength. We further analyze the impact of nonreciprocity on the engine's performance and operation regime. Our findings establish nonreciprocal coupling as an effective control knob for designing and optimizing quantum dot information engines, surpassing the capabilities of conventional reciprocal configurations.

cond-mat.stat-mech↗