arXiv subjects

Yang Yang

Publications and source records attributed to Yang Yang.

At least 19 recordsLinked to original sources

arXiv2026

GeoTrussRover: Morphological Computation with Contact-Semantic Control Primitives

Reconfigurable robots can change their contact geometry when a fixed body cannot negotiate an obstacle. A variable-geometry truss (VGT) distributes this shape change through a load-bearing structure, but coupling it to a mobile base creates a high-dimensional coordination problem. GeoTrussRover combines an electrically actuated VGT, a wheeled base, and contact-semantic morphology planning and control. We solve one source traversal and extract four contact-semantic primitives that describe coordination among 21 members. Physics-constrained projection adapts them to unseen step heights with the same contact topology. When every phase remains feasible, adaptation does not recompute the complete motion. If one phase violates the new physical constraints, only that phase is recomputed. A full-space QP then tracks the adapted motion and corrects member and wheel errors. For transfer from 0.10m to 0.075m, the method reduces objective-function evaluations by 63.7% relative to full recomputation. Contact-phase feasibility analysis covers step heights from 0.10 to 0.46m, or 1.08 to 4.97 wheel radii, with the upper value near the theoretical feasible boundary. The electric prototype traverses 2.11 wheel radii. The resulting low-dimensional representation stores task coordination in a hyper-redundant, load-bearing morphology and reuses it during locomotion.

cs.RO↗

arXiv2026

LightMedSeg-ISLES: Stroke Lesion Segmentation with 81x Fewer Parameters than nnU-Net

Large networks and ensembles often lead medical image segmentation challenges, but their storage and inference demands complicate deployment. We present LightMedSeg-ISLES, a 1.26-million-parameter pipeline for T1-weighted stroke lesion segmentation in ISLES'26. On a 146-case held-out cohort, flip test-time augmentation produces 0.618 mean Dice and 0.599 lesion-wise F1. A 102.35-million-parameter nnU-Net ResEnc-L produces 0.634 Dice and 0.544 lesion-wise F1 after size filtering. LightMedSeg therefore retains 97.5\% of nnU-Net's Dice with 81.4$\times$ fewer parameters while improving lesion-wise F1 by 0.055. Its four-pass TTA operating point requires 4.7$\times$ fewer FLOPs per standardized patch than nnU-Net. It also slightly exceeds filtered UNETR++ and nnFormer. Longer training and stronger augmentation add 0.0358 Dice without increasing capacity, establishing a strong single-checkpoint alternative to much larger models.

cs.CV↗

arXiv2026

Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling

To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) exploits waveform superposition over multiple-access channels (MACs) for analog model aggregation. However, coherent AirFL typically relies on stringent PHY-layer conditions such as accurate channel state information (CSI), tight time/frequency synchronization, and frequent transceiver calibration for signal alignment. However, these requirements, if not impossible to be met, incur substantial communication and computation overhead. In this paper, we propose a non-coherent AirFL (NCAirFL) protocol over a broadband single-antenna MAC, leveraging binary dithering, unbiased non-coherent detection, and long-term error feedback to waive the need for instantaneous CSI. For NCAirFL with general smooth non-convex objectives and a constant learning rate, we establish a convergence bound achieving the convergence rate in the same order of $\mathcal{O}(1/\sqrt{T})$ as communication-ideal FedAvg, where $T$ is the total number of communication rounds. To further improve communication efficiency under data and wireless resource heterogeneity, we also derive a lower bound on the expected single-round objective decrease in the global loss conditioned on device scheduling, building upon which a surrogate objective function is obtained for jointly optimal device selection and power control. Experimental results on MNIST and CIFAR-10 corroborate that NCAirFL achieves learning performance close to FedAvg in practical settings, with the proposed device scheduling policy substantially accelerating convergence.

cs.IT↗

arXiv2026

Near-Horizon Tidal Disruption Events

Tidal disruption events (TDEs) offer a unique dynamical probe of the spacetime geometry around supermassive black holes (SMBHs). While conventional TDEs occur around SMBHs of $M_\bullet \sim 10^6 - 10^7 M_\odot$, where stars are disrupted far from the event horizon, here we identify a special class of TDEs around rapidly spinning SMBHs with masses $M_\bullet \gtrsim 10^8 M_\odot$, where the tidal-disruption radius approaches the gravitational radius. We term these events "near-horizon TDEs" and, by calculating geodesics in Kerr spacetime, investigate how the proximity of the horizon modifies the debris evolution and subsequent fallback. We find that for stars encountering the SMBH on parabolic orbits, up to $\sim (90 \%-95 \%)$ of the stellar debris either plunges directly into the SMBH or escapes the system. Bound orbits, by contrast, retain a substantially larger fraction of the debris. Using the resulting debris distribution, we calculate the fallback rates and find that bound orbits produce intrinsically higher peak fallback rates and shorter decay timescales, by factors of $\sim 10^3$ relative to parabolic orbits. However, when the stellar orbital angular momentum is particularly low, the peak fallback rate can be substantially suppressed by debris lost to plunge orbits. This combination of rapid fallback and a mass deficit naturally explains overluminous TDEs such as ASASSN-15lh, which standard TDE models have struggled to reproduce. Our work establishes near-horizon TDEs as a new probe of strong-field gravity and a promising tool for identifying massive, rapidly spinning SMBHs.

astro-ph.HE↗

arXiv2026

A growth gap for anisotropic minimal graphs

We prove a growth gap for the gradient of entire anisotropic minimal graphs. For each $n\geq2$ and each smooth uniformly elliptic parametric integrand $\Phi$ on $\mathbb{R}^{n+1}$, there is an exponent $\alpha=\alpha(n,\Phi)>0$ such that every smooth nonaffine entire $\Phi$-minimal graph $u:\mathbb{R}^n\to\mathbb{R}$ satisfies $\sup_{B_R^n}|Du|\geq cR^\alpha$ for all $R\geq R_0$, for some constants $c>0$ and $R_0<\infty$ depending on the solution. In particular, gradient growth $o(R^\alpha)$ forces flatness, resolving a conjecture of Mooney and the author. The result holds in every dimension, without any assumption that $\Phi$ is close to the Euclidean area integrand.

math.AP↗

arXiv2026

From Topical Relevance to Answerability: Entailment Distillation for Conversational Retrieval

Existing conversational retrievers commonly treat topical relevance as a proxy for answerability. However, a passage that closely matches the dialogue context is not necessarily the one that supports the correct answer. We identify this mismatch as a systematic answerability gap. To address this issue, we propose CLEAR, a framework that shifts conversational retrieval from topical relevance to answerability. The core of CLEAR is entailment distillation, which transfers answer-passage entailment supervision into a cross-encoder reranker so that the reranker discriminates answer-supporting passages from topical distractors at inference time, without requiring answers. CLEAR is complemented by a passage-centric abductive recall module that brings low-similarity yet answerable passages into the candidate pool by inferring answerable queries from passages with an LLM. Across TopiOCQA, QReCC, and out-of-domain TREC CAsT datasets, CLEAR consistently improves top-ranked precision over strong query-rewriting and dense-retrieval baselines, with the largest gains observed in conversations involving heavier topical noise. Moreover, applying our reranker on top of an LLM-driven query rewriter yields further gains.

cs.IR↗

arXiv2026

A Halfspace Theorem for Global Anisotropic Perimeter Minimizers

We prove an arbitrary-dimensional halfspace theorem for global minimizers of a smooth uniformly elliptic anisotropic perimeter: if the nonempty boundary of a minimizing set is contained in a halfspace, then the set itself is a halfspace. No evenness of the integrand or regularity of the minimizing boundary is assumed. The proof combines wall contact with a plane-peeling argument for the complementary phase defect and a simultaneous second blow-down. We also give a direct, self-contained exterior-barrier proof of the two-dimensional stationary anisotropic statement. Its rigidity conclusion is already covered by Bergner's earlier halfspace theorem; the proof is retained because it works directly with the anisotropic Euler-Lagrange operator.

math.AP↗

arXiv2026

OptiXDE: A fast optical-inspired solver for differential equations

OptiXDE is a matrix-free spectral operator framework for differential equations on uniform grids and embedded domains. Inspired by angular-spectrum propagation in Fourier optics, it maps transform-diagonal spatial operators to analytical modal multipliers and composes them with physical-space operators for nonlinearities, geometry and boundary enforcement. A common transform--operator--inverse-transform backbone is demonstrated across transient diffusion, periodic and embedded-domain Poisson problems, the cubic nonlinear Schr"odinger equation, viscous Burgers dynamics, the two-dimensional Allen--Cahn equation and incompressible flows from the Taylor--Green vortex to embedded-cylinder vortex shedding. Transform-compatible linear problems are recovered near the floating-point limit, whereas errors on the singular L-shaped domain remain localized near the re-entrant corner and regularized interface. Nonlinear benchmarks recover second-order temporal convergence and the expected conservative or dissipative behavior, while incompressibility remains near round-off level during long-time vortex shedding. The matrix-free updates require $\mathcal{O}(N\log N)$ work and $\mathcal{O}(N)$ memory. Device-resident transform workloads reach $94.9\times$ GPU acceleration, and the complete embedded-cylinder solver achieves a $42.1\times$ CPU--GPU speedup under matched numerical settings. These results establish OptiXDE as a deterministic and extensible operator-centric framework for structured and embedded-domain differential equations.

math.NA↗

arXiv2026

H2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning

Tables are ubiquitous across diverse domains, yet reasoning over them remains a significant challenge for modern large language models (LLMs). Current approaches typically linearize tables into sequences, inherently overlooking their intrinsic two-dimensional and hierarchical structure. To address this, we propose H2Table (Hierarchical Hypergraph-Enhanced Table Reasoning), a novel framework that represents complex tables as hierarchical nested hypergraphs. To process this representation, we design a tailored hypergraph encoder to facilitate message passing between hyperedges (headers) and nodes (cells), thereby perceiving the semantic entailment relationships between them within complex tables. Furthermore, we introduce a set of learnable query vectors acting as a lightweight bridge to extract representative structural embeddings from the encoder into the LLM. Experimental results demonstrate that our approach effectively handles complex table question answering tasks with hierarchical nested headers. Notably, on the HiTab dataset, H2Table achieves an average improvement of 22.88% over state-of-the-art baselines on highly complex tables with a nesting depth of four. Our code is available at: https://github.com/lila120/h2table.

cs.AI↗

arXiv2026

LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information

Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG structure as discrete tokens or refine a restricted set of candidate entities, and these two directions have largely been studied separately. We propose CoSC for LLM-based KGC, which combines discrete structural coding with similar entity information. Specifically, an LLM generates an initial candidate entity ranking from discrete structural codes, after which information from entities with structures similar to that of the query entity refines the ranking. Experiments on FB15k-237 show that CoSC outperforms existing baselines on MRR and Hits@10 while remaining competitive on Hits@1.

cs.AI↗

arXiv2026

When and What to Teach: Budget-Aware Online Adaptation for Web Agents

Web agents have achieved significant success in automating complex internet tasks but deploying them in real-world environments requires continuous online adaptation. Given that deploying powerful proprietary models remains commercially cost-prohibitive, practitioners must rely on lightweight local models that evolve post-deployment via online teaching from a stronger teacher. However, standard interactive feedback imposes prohibitive costs. We show that conventional trajectory-level preference optimization wastes budget on both unresolvable episodes and redundant execution turns. To resolve these inefficiencies, we propose \textbf{Score-Guided Online Teaching with Budgeted Trajectory Trimming}, a budget-aware framework that systematically orchestrates \textbf{when} and \textbf{what} to teach. Specifically, our framework integrates a solvability-aware teacher gate to dictate \textbf{when} to query the teacher model and a score-guided turn selection mechanism to decide \textbf{what} informative turns to retain. Extensive experiments on MiniWoB and TimeWarp demonstrate that our method achieves comparable first-pass success while reducing teacher calls by 22.6\% and student training compute by 52.1\% on average. Our code is available at https://github.com/zjw131f1fc/budgeted-online-teaching.

cs.AI↗

arXiv2026

Two-dimensional quantum Griffith singularity in three-dimensional ZrN$_x$ superconducting films

We report the experimental observation of two-dimensional (2D) quantum Griffiths singularity (QGS) in $\sim$200-nm-thick epitaxial ZrN$_x$ superconducting films. The films possess a rock-salt structure and are three-dimensional (3D) with respect to superconductivity. For each film with $x \gtrsim 1.30$, the low-temperature magnetoresistance isotherms under fields perpendicular and parallel to the film plane cross over at a broad magnetic field range independently rather than at a single crossing point. Despite the macroscopic 3D nature of the superconductivity, the magnetoresistance isotherms at selected adjacent temperatures follow the theoretical prediction of power-law scaling for 2D superconducting systems, rather than that for 3D systems. The effective critical exponent $z\nu$, obtained by analyzing the magnetoresistance isotherms using the 2D power-law scaling, increases with decreasing temperature and diverges as the quantum phase transition is approached. In addition, the resistivity data near the superconductor-insulator or superconductor-metal transitions obey an activated scaling form that describes the quantum phase transition of 2D superconducting systems governed by an infinite-randomness critical point. The QGS in the ZrN$_x$ films is attributed to quenched disorder induced by intrinsic defects, such as Zr vacancies and N interstitials, which creates spatially inhomogeneous superconducting rare regions. The dynamics of these rare regions, which may exhibit effective 2D characteristics near the quantum critical point, dominate the transport properties of the system near the quantum phase transition. Our results provide compelling evidence for the existence of QGS in 3D superconductors and highlight the crucial role of disorder-induced inhomogeneity in determining the critical behavior of quantum phase transitions.

cond-mat.supr-con↗

arXiv2026

Report Supervision

Segmentation models can surpass radiologists, classification models, and vision-language models in tumor detection. Importantly, segmentation models outline tumors, allowing radiologists to better verify and trust the AI output. Their main limitation is the scarcity of tumor masks: creating one 3D tumor mask takes up to 30 minutes, so most public CT datasets contain only a few hundred masks, and even the largest private datasets contain only a couple of thousand. Tumor masks are not produced in clinical routine, but radiology reports are. Public datasets contain tens of thousands of CT-Report pairs, and hospitals contain hundreds of thousands. These reports describe tumors in detail, providing large-scale, informative training data. Here, we introduce Report Supervision (R-Super), a training framework that uses reports to directly supervise and improve tumor segmentation. R-Super introduces new loss functions that teach segmentation models to segment tumors that match report descriptions of tumor count, sizes, and locations. Reports are only used for training. We evaluated R-Super on kidney and pancreatic tumor segmentation, exploring diverse training data sizes, up to 41,418 CT-Report plus 3,488 pancreatic tumor CT-Mask pairs. On external validation, R-Super increased tumor detection F1-Score and segmentation DSC by up to +15% with respect to mask-only training. It also surpassed alternative methods such as CLIP and multi-task learning. Leveraging numerous readily available reports to supplement scarce masks, R-Super strongly improves AI performance when very few training masks are available (e.g., 50), and when many masks are available (e.g., 3,488), unlocking scale in tumor segmentation.

cs.CV↗

arXiv2026

Energy Partition in AGN-driven Bubbles of NGC 4438: From Nuclear Bubbles to a Galaxy-scale Outflow

Jets launched by accreting supermassive black holes represent a major mode of active galactic nucleus (AGN) feedback. However, how their energy is divided among bulk kinetic motion, thermal gas, magnetic fields, cosmic rays (CRs), and radiation - and how this distribution changes with spatial scale - remains poorly constrained. NGC 4438 provides a unique laboratory for probing this evolution, hosting two 200-pc-scale nuclear bubbles and a lopsided ~10 kpc galaxy-scale outflow plausibly associated with the same AGN. We present a multi-wavelength analysis to investigate the morphology, radiation mechanisms, and energetics of these structures. Joint radio-X-ray modeling shows that the non-thermal emission in the nuclear bubbles may require two distinct populations of cosmic-ray electrons, suggesting that in addition to shock acceleration at the bubble rim, the highest-energy particles may be linked to acceleration processes closer to the unresolved central engine. A spatially resolved energy inventory reveals that bulk kinetic energy dominates the current energy budget of the nuclear bubbles, while roughly half of the injected energy has already been transformed into thermal, CR, and magnetic energy, as well as radiative losses. Across all bubble sizes, the thermal and magnetic pressures are consistent within the uncertainties, implying that magnetic fields remain dynamically significant on all examined spatial scales. Furthermore, the empirical correlation between radio luminosity and jet power, established for kiloparsec-scale jet bubbles (MerloniHeinz2007), matches the energetics of the galaxy-scale outflow but substantially overestimates the power of the 200-pc-scale nuclear bubbles, underscoring the scale dependence of jet energy dissipation.

astro-ph.HE↗

arXiv2026

Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning

Robot co-design via bi-level optimization couples within-lifetime controller learning for fitness evaluation with cross-generational morphological evolution. Prior work has established that well-adapted morphology facilitates faster control learning, a property termed morphological intelligence. Yet how control learning reciprocally shapes morphological evolution remains unexplored. This paper examines both directions for a holistic account of brain-body interplay. We first show that morphological contributions to control learning decouple into two orthogonal dimensions. We formalize the convergence speed as morphological intelligence and identify the performance ceiling as a complementary quantity termed true potential. A concise functional relation is then established to jointly characterize both quantities from individual learning curves, which, when aggregated at the population level, capture evolutionary profiles. Through extensive experiments on simulated voxel-based soft robots, we reveal that premature fitness evaluation systematically underestimates true potential and biases selection towards fast learners. This restricts design space exploration, compromising both optimization efficiency and morphological diversity. Notably, the widely recognized morphological Baldwin effect emerges as an artifact of this bias rather than a general evolutionary tendency. We therefore propose AdaControl, which monitors disproportionate selection for morphological intelligence during evolution and allocates minimally sufficient control learning for unbiased fitness evaluation. With AdaControl, a simple genetic algorithm rivals state-of-the-art generative-model-based co-design methods in discovering diverse high-performing designs while cutting computation by up to 80% versus exhaustive control.

cs.RO↗

arXiv2026

PhysECD: A Physics-Constrained E(3)-Equivariant Framework for Electronic Circular Dichroism Spectrum Prediction

The electronic circular dichroism (ECD) spectrum is a primary experimental probe for assigning the absolute configuration of chiral molecules, yet interpreting a measured spectrum requires time-dependent density functional theory (TDDFT) calculations that can cost hours per molecule and must be repeated for every candidate stereoisomer and conformation. We present PhysECD, a physics-constrained, parity-aware E(3)-equivariant framework that bypasses computationally expensive TDDFT and predicts ECD spectra directly from the 3D structure of an individual conformer. Instead of regressing the spectrum as an opaque sequence, PhysECD predicts the physical quantities that generate it: per-state excitation energies and electric and magnetic transition dipoles. These quantities determine the rotatory strength R -- the dot product of the two dipoles, a pseudoscalar that reverses sign under mirror reflection -- and yield the final spectrum through a differentiable Gaussian-broadening formula derived from the underlying physics. The parity structure of the equivariant features guarantees the correct chiroptical symmetry: reflecting a molecule exactly negates the predicted spectrum. On the CMCDS dataset, PhysECD attains a per-molecule spectral Pearson correlation of 0.642 (mean) / 0.822 (median), substantially exceeding prior learned predictors while remaining physically interpretable. Experiments across multiple backbones further show that the framework is backbone-agnostic, paving the way for real-time assignment of absolute configuration.

physics.chem-ph↗

arXiv2026

DART-SD: Diamond-topology Aware Retrieval and Tuning for Self-Distillation of Multi-Turn Tool-Calling Agents

Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fundamentally limited by the reliance on full-length trajectory imitation. For tasks involving multiple order-independent sub-goals, the optimal solution space forms a vast combinatorial diamond lattice. Forcing this rich topology into monolithic trajectories causes a severe topological collapse, indiscriminately penalizing valid alternative explorations and severely degrading policy diversity. To address this, we propose DART-SD (Diamond-topology Aware Retrieval and Tuning for Self-Distillation), a novel framework that shifts the paradigm from global forcing to topology-guided localized correction. DART-SD first models the execution process as a converging Interaction-State Transition Graph (ISTG), faithfully capturing the inherent diamond topology of successful and failed exploratory paths. During autonomous rollouts, the framework identifies the Critical Topological Breakpoint (CTB) and retrieves success-supported recovery references. Finally, we introduce a progressive self-distillation paradigm through CTB-guided localized supervision, ensuring that the training loss is calculated exclusively on the generated recovery steps while strictly protecting the valid reasoning prefix from destructive gradient updates. Experiments on complex multi-turn tool-calling benchmarks demonstrate that DART-SD significantly outperforms traditional full-trajectory baselines.

cs.CL↗

arXiv2026

From Storage to Access: Verifiable Activation of Parametric Knowledge in LLMs via Explicit Priming and Implicit Reasoning

Although Large Language Models (LLMs) encode rich factual knowledge in their parameters, reliably recalling and verifying such knowledge remains a key bottleneck in factual question answering. Existing end-to-end methods entangle knowledge elicitation with reasoning, making it difficult to determine whether correct answers arise from parametric knowledge or the input context. To address this challenge, we propose VAKE (Verifiable Activation of Parametric KnowledgE), a two-stage reinforcement-learning framework that externalizes latent parametric knowledge through explicit Priming and transfers the acquired elicitation capability to implicit Reasoning. Given a query and an insufficient retrieved subgraph, the Priming policy explicitly inserts bridging triples as verifiable evidence, with supervision provided by rewards derived from answers generated by a separate frozen model over the augmented subgraph. Building on the policy learned during Priming, the Reasoning stage trains the model to answer from the original input, testing whether the capability acquired through explicit knowledge elicitation transfers to implicit reasoning. Experiments across seven benchmarks and models from 3B to 14B show that VAKE consistently outperforms standard baselines, including when transferring directly from HotpotQA to OOD datasets. LLM-based evaluation further shows that over 80% of the inserted triples provide factual bridging knowledge not derivable from the retrieved context, while more than half elicit knowledge inaccessible through direct prompting. These results suggest that VAKE activates latent parametric knowledge rather than copying the input context or memorizing dataset-specific associations.

cs.CL↗