arXiv subjects

Yong Zhang

Publications and source records attributed to Yong Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Anchor 1: Precise and Stable Humanoid Control for Whole-Body Manipulation

Humanoid controllers have acquired increasingly diverse motor skills, yet translating these capabilities into precise and stable whole-body manipulation remains challenging. Manipulation tasks are sensitive to execution deviations -- even small errors or unintended steps can cause failure. Introducing Anchor 1, a whole-body controller for precise and stable whole-body manipulation, together with a benchmark spanning diverse coordination demands. Anchor 1 adopts calibrated sparse motion references to preserve task-relevant human motion structure without online joint-space retargeting, leaving kinematic freedom for precise control. To guide this freedom towards stable, physically plausible whole-body coordination, we shape the training distribution through offline curation of robot motions paired with these calibrated sparse references. We further introduce explicit error feedback decoupled from motion encoding, improving control precision while supporting flexible command interfaces. To achieve a systematic evaluation of these capabilities, a benchmark is proposed to assess precision, sustained execution stability, and reachability across diverse whole-body coordination demands. Evaluations in simulation and on hardware show stronger precision and stability across all evaluation tracks. Under the most strict real-world global scoring setting, Anchor 1 achieves a 30\% to 80\% decrease in tracking error with fewer look-ahead inputs, and without retargeting compared to all existing controllers. Downstream experiments demonstrate more efficient teleoperated data collection and higher task success using the same deployment pipeline as well. We believe Anchor 1 supports precise and stable whole-body control as a practical basis for humanoid task execution.

cs.RO↗

arXiv2026

RV2GC: Reconstructing the Garbling Boundary in a RISC-V Garbled Processor

Programmable garbled-circuit execution must balance the benefits of cross-program reuse against the cost of repeated work during secure execution. A complete garbled processor provides a fixed circuit for many programs, but its evaluator repeatedly visits processor-wide structures even when only a small part performs the current protected computation. We present RV2GC, which reconstructs the garbling boundary around a fixed library of RISC-V instruction circuits. Public scheduling and operand routing operate outside the circuit topologies; persistent label-encoded state connects independently invoked garbled instruction circuits and instruction-specific garbling-free paths through a common interface. A Conditional-execution Stack and protected commits support secret-dependent branches, bounded loops, and secret-indexed memory access under public scheduling, keeping instruction and memory-address sequences independent of private inputs. Across nine benchmarks, measured ARM2GC-to-RV2GC runtime ratios range from 12.81 to 4,043. In terms of circuit size, the largest listed instruction circuit has 3,386 gates, corresponding to 2.044% and 0.393% of the 256-byte and 4,096-byte Amber processors, respectively. Additional comparisons quantify schedule storage, communication, and the cost of secret-dependent execution.

cs.CR↗

arXiv2026

Bounds on Odd and Odd-Even Induced Subgraphs

Let $G$ be an $n$-vertex graph and let $\ell:V(G)\to\mathbb{F}_2$ prescribe degree parities. A set $S\subseteq V(G)$ is $\ell$-admissible if every $v\in S$ has degree congruent to $\ell(v)$ modulo $2$ in $G[S]$. Let $h_\ell(G)$ be the maximum order of an $\ell$-admissible set, set $f_{\mathrm{oe}}(G):=\min_\ell h_\ell(G)$, and write $f_o(G):=h_{\mathbf{1}}(G)$, where $\mathbf{1}(v)=1$ for every $v\in V(G).$ We prove three main results for graphs without isolated vertices. First, by extending Zeng's odd-cut method to arbitrary parity prescriptions and introducing a one-sided completion lemma, we show that $h_\ell(G)\ge 2n/11$ for every $\ell$. Consequently, $f_{\mathrm{oe}}(G)\ge 2n/11$, improving the previous bound $2n/21$. Second, for bipartite graphs we derive lower bounds on $f_o(G)$ in terms of the $\mathbb{F}_2$-rank of the bipartite adjacency matrix and combine them to obtain \[ f_o(G)\ge \left(\frac14+\frac1{256}\right)n=\frac{65}{256}n. \] Thus, in the bipartite case, the factor $2$ in Scott's bound $f_o(G)\ge n/(2χ(G))$ can be replaced by $128/65<2$. Finally, a fourth-moment argument gives, for $α=α(G)\ge2$ and $δ(G)\ge 1$, \[ f_o(G)\ge \fracα{2}+\frac{\log_3α}{8} -\frac14\log_3\log_3\sqrtα. \] We also construct bipartite graphs satisfying \[ f_o(G)\le \frac{α(G)}2+\log_2\!\bigl(α(G)+1\bigr)+\frac12, \] showing that the logarithmic additive improvement over Scott's bound $f_o(G)\geα(G)/2$ has the optimal order of magnitude.

math.CO↗

arXiv2026

String Amplitudes on Integer Rectangles

Hidden zeros of tree-level string amplitudes of colored scalars lie on loci specified by integer-valued dimensionless momentum contractions arranged in rectangles on the kinematic mesh. We study the same kinematic loci for open-string amplitudes with gluons, fermions and higher-spin states at arbitrary finite mass levels. Disk monodromy relations yield $\textit{zero-locus factorization}$: at regular points where the monodromy system is invertible, the amplitude is a weighted sum of physical three-point amplitudes sewn to complete on-shell $(n-1)$-point amplitudes. Hidden zeros follow when the external wavefunctions annihilate all contributing three-point couplings. In the low-energy limit, the massless zero-integer branch recovers the corresponding field-theory factorization. For closed strings, including gravitons, persistent cubic zeros in both chiral sectors remove the disk poles, allowing the Kawai--Lewellen--Tye momentum-kernel zero to imply amplitude zeros. The operator product expansions of external vertices give a common worldsheet origin for these conditions across spins and mass levels.

hep-th↗

arXiv2026

The ALMA-QUARKS Survey: Discovery of Superfine Dusty Fibrils inside Massive Star-forming Clumps

We report the discovery of more than 320 superfine dusty filamentary structures (fibrils) inside 121 massive star forming clumps that are located in widely different Galactic environments (Galactocentric distances of ~1.2 - 12.9 kpc). These fibrils are identified from the 1.3-mm continuum emission in the ALMA-QUARKS survey, which has a linear resolution of ~900 AU for a source at ~3 kpc. The typical width of these fibrils identified in clumps is found to be ~0.01 pc, which is about ten times narrower than that of dusty filaments in nearby clouds identified by the Herschel Space Observatory. The mass (M) versus length (l) relation for these fibrils follows M ~ l^2, similar to that of Galactic filaments identified in space (e.g., Herschel) and ground-based single-dish (e.g., APEX) surveys. However, these fibrils in the ALMA-QUARKS survey are significantly denser (N_{H_2} = 10^{23} - 10^{24} cm^{-2}) than the filaments found in previous Herschel surveys (N_{H_2} = 10^{20} - 10^{23} cm^{-2}). This work contributes a large sample of newly found superfine fibrils in massive clumps, following the identification of large 0.1 pc-wide filaments and associated internal velocity coherent molecular fibers in nearby molecular clouds, further emphasizing the crucial role played by filamentary structures in star formation at various physical scales.

astro-ph.GA↗

arXiv2026

Beyond Policy Engines: Binding-Complete Execution Admission

Policy engines determine whether evaluated policies permit a request, but a correct ALLOW decision does not establish complete governance-source participation, exact context binding, or sufficient grounds for execution admission. This paper examines the gap between policy evaluation and execution admission. We introduce the Binding-Complete Execution Admission Contract (BCEAC), a governance-relative contract for independently determined source obligations, exact policy and contextual binding, and typed admission outcomes under explicit trust assumptions. We propose a conditionally engine-agnostic architecture in which participating backends must satisfy declared semantic profiles, adapter contracts, and contextual correspondence obligations; an Execution Boundary Language (EBL) authoring syntax remains optional. The architecture separates governance resolution, policy evaluation, independent admission verification, and downstream execution mediation. We investigate two distinct experimental tracks: a finite Governance Source Completeness study and an OPA/Cedar experiment with archived native-engine records examined by offline review. The OPA/Cedar archive comprises 72 scenarios and 144 strict case-engine records (120 native-invoked and 24 pre-native), with eight additional permissive controls outside the strict denominator. The offline review did not rerun the original native OPA/Cedar executables. The results provide bounded evidence while retaining historical failures and unresolved implementation defects. The work does not establish constitutional adequacy, general semantic refinement, authenticated currentness, machine-checked execution-safety theorems, or protected external effects. Its contribution is an explicit, falsifiable framework for determining what must be established between a policy decision and an execution-admission judgment.

cs.CR↗

arXiv2026

On the annular overdetermined problems with linear and power nonlinearities in $\mathbb{R}^2$

In this paper, we study two classes of partially overdetermined elliptic problems on planar annular domains in $\mathbb{R}^2$, with a fixed inner circle and a free outer boundary. The Dirichlet values are prescribed to be $1$ and $0$ on the inner and outer boundaries, respectively, while the squared normal derivative is constant on the outer boundary. For the linear law $Δu=-μu$, we establish local bifurcation under a Dirichlet nonresonance condition. For the power law $Δu=u-u^p$ with $p>1$, we take the mean outer radius as the bifurcation parameter and construct large positive radial solutions on thin annuli. The limiting profile solves $-V''=V^p$ on an interval, and the principal mixed Dirichlet--Neumann eigenvalue of its linearization determines a constant $κ_p>0$. For every fixed inner radius $λ>0$ and every sufficiently large integer $K$, we obtain a critical radius $R_K=λ+λκ_p/K+o(K^{-1})$ and a smooth local curve of positive solutions with noncircular, even, $2π/K$-periodic outer boundaries. The proof combines shape derivatives, spectral estimates, and the Crandall--Rabinowitz bifurcation theorem.

math.AP↗

arXiv2026

Organon : From Evolutionary Search to Recursive Self-Improving Agents for Scientific Discovery

Scientific discovery does not follow a single fixed methodology: research methods vary across domains and evolve through experimentation as investigations unfold. Although LLM-guided evolutionary search has advanced automated discovery, existing frameworks typically rely on fixed search procedures or restrict adaptation to predefined components, limiting their ability to address diverse research problems and adapt to challenges that emerge at runtime. We introduce Organon, a recursively self-improving agent for scientific discovery. During execution, a single persistent agent analyzes its accumulated experience and autonomously revises its own search strategies, population management, diagnostic tools, memory, and worker coordination. These revisions take effect at runtime, allowing the agent to continuously adapt its approach as research progresses. Importantly, the agent can also diagnose and correct flaws in its own adaptation procedures, enabling recursive improvement of the mechanisms that guide its future self-improvement. We evaluate Organon across 21 computational discovery tasks spanning algorithm design, GPU kernel optimization, mathematical construction, and systems optimization. On ALE-Bench Lite, Organon achieves the best performance among competing search frameworks on all 10 tasks, improving mean held-out performance by 20.5% over EvoX. On KernelBench, it achieves the best results on three of four workloads, with speedups of up to 1.67x over the strongest competing method. Analysis of the search trajectories further reveals how the agent develops task-specific search strategies, builds new diagnostic capabilities, and corrects or reverses its own earlier revisions. We implement Organon using DeepSeek Harness and Cordis's spatiotemporal composability. Code is available at https://github.com/cheikh025/Organon.

cs.NE↗

arXiv2026

BluePixel: Toward Native 4K Agentic Image Editing

Instruction-driven image editing has reached native 4K, yet how such editors are trained remains undisclosed, and pretraining one from scratch is beyond most teams. The task itself compounds the difficulty: a single end-to-end editor cannot make precise parametric adjustments, match specialized experts, or interpret the vague instructions users actually write. We argue that 4K editing is a system problem rather than a single-model problem, and present BluePixel, which couples a native-4K editing model with an agent harness; the complete training recipe is disclosed in this paper. The model makes 4K training practical: it turns a 1K-pretrained editor into a native-4K one through progressive VAE adaptation that keeps 4K training as affordable as 1K, absorbs a long tail of vertical editing skills through continual learning, and refines the aesthetic-fidelity trade-off with reinforcement learning. Distillation via source-anchored target projection (SATP) then compresses 50 denoising steps to 4, completing a 4K edit in 8.7 s on a single Kunlunxin P800 XPU while preserving task success rate and background consistency. The harness, BluePixel Agent, makes editing controllable: it decomposes user instructions into specialist and general operations, verifies every result with the same reward model that guided training, and retries within bounded attempts. On VINS-4KEval++ (3,305 native-4K cases), BluePixel ranks second among seven models, behind only GPT Image 2 in non-edited-region consistency.

cs.CV↗

arXiv2026

SiO Maser Line-width Diagnostics of AGB Stars and Red Supergiants: The RSG-adjacent Region in the FWZI--Luminosity Plane

We investigate whether SiO maser line profiles can distinguish asymptotic giant branch (AGB) stars from red supergiants (RSGs) and identify unusual AGB-classified sources near the RSG regime. We compiled 2,956 detected SiO maser spectra for 1,551 objects and constructed reference samples of 1,068 O-rich AGB stars and 36 RSGs. The full width at zero intensity (FWZI) was reliably measured for 1,020 AGB stars and 32 RSGs. AGB stars are concentrated at narrow line widths: 81.5% have FWZI <10 km s^-1, whereas 81.2% of RSGs have FWZI >15 km s^-1 and none has FWZI <10 km s^-1. Infrared two-color diagrams provide only partial separation. Combining FWZI with luminosities based primarily on quality-filtered Gaia/Bailer-Jones distances, with independent literature distances for several RSGs, reveals a weak tendency for wider SiO maser lines at higher luminosities but no clean boundary. We therefore define an empirical RSG-adjacent region in the FWZI-luminosity plane, 15 <= FWZI <= 26.6 km s^-1 and 3.9 <= log(L/L_sun) <= 4.9, to characterize the high-luminosity, broad-line tail of the AGB-classified sample. Ten sources fall within this region. Using infrared SEDs, DUSTY fits, OH/H2O maser information, and variability, we find that six are most naturally interpreted as luminous AGB-like objects, two warrant follow-up to assess possible high-initial-mass AGB, RSG-like, or SAGB-related interpretations, and two are less securely constrained because of poor DUSTY fits. The RSG-adjacent region is therefore a physically mixed screening space, not a definitive classification box, for selecting high-priority follow-up targets.

astro-ph.SR↗

arXiv2026

Nontrivial domains for the Bernoulli's overdetermined problem with small constant vorticity

In this paper, we first apply the implicit function theorem to find nontrivial solutions to the following Bernoulli's overdetermined problem \begin{equation} -Δu=γ\,\, \text{in}\,\,Ω\setminus\overline{D}, \,\, u=1,\,\,\partial_\mathbf{n} u=Q\,\,\text{on}\,\,\partial D, \,\,u=0\,\,\text{on}\,\,\partialΩ\nonumber \end{equation} for a fixed boundary $\partialΩ$ and a free boundary $\partial D$ in $\mathbb{R}^{N}$ with $N\geq2$, where $γ$ is a sufficiently small constant, the Bernoulli value $Q$ is determined by the construction, and $Ω\setminus\overline{D}$ is a bounded ring-shaped domain. In dimension $N=n+1$ with $n\geq2$, we further study the existence of a nontrivial unbounded domain $Ω\setminus\overline{D}$ in $\mathbb{R}^N$ such that the Bernoulli's overdetermined problem admits a solution. This unbounded domain we construct is periodic in one variable and radial in the other variables by bifurcation analysis.

math.AP↗

arXiv2026

PIVOT: Perplexity-Informed KD-to-RL Transition Scheduling for Vertical-Domain Few-Shot Distillation

Vertical-domain few-shot classification remains challenging for small language models, as limited supervision makes it difficult to acquire domain-specific decision knowledge. On-Policy Distillation (OPD) can improve teacher-guided adaptation by supervising student-generated rollouts, while GRPO-based reinforcement learning can further refine downstream predictions. However, existing KD-to-RL pipelines typically rely on globally fixed transition schedules, ignoring that different samples may require different amounts of teacher-guided acquisition before reward-driven refinement. We propose PIVOT (Perplexity-Informed Transition Optimization), a dynamic transition framework that routes samples between OPD and GRPO according to teacher-evaluated sequence perplexity. PIVOT moves low-perplexity samples to GRPO for reward-driven refinement while keeping high-perplexity samples under OPD for continued domain knowledge acquisition. Experiments on Banking77 and HWU64 show that PIVOT consistently outperforms continued OPD and globally synchronized OPD$\rightarrow$GRPO baselines under the same number of post-warm-up student optimization steps, achieving stronger downstream performance and more stable training dynamics.

cs.LG↗

arXiv2026

Evidence Coverage for Intent-Bound Execution: Scope, Obligations, and Cutoff Reasoning

A verifier may authenticate every available record and still lack grounds to call an execution account complete. Such a claim requires a justified account of which records were due for the execution being assessed. We present an analytical model for retrospective coverage of declared execution-evidence obligations. Its scope binds a structured Intent, an exact Candidate, a selected analytical attempt, an execution and evidence boundary, a stage horizon, a fixed record-obligation profile, a named verifier, and an assessment cutoff. Branch and trigger premises determine obligation instances; source competence, content, integrity, and object and stage bindings determine admissibility. We distinguish closure of the obligation inventory from closure of the relevant verifier view, and define three reporting results: COMPLETE_WITHIN_SCOPE, INCOMPLETE, and UNKNOWN. These results concern current coverage of obligations due at the cutoff. Execution progress, external outcome knowledge, and historical delivery timeliness are reported separately. Constructed service-principal-disablement cases demonstrate complete dispatch and refusal branches, a due but missing final-result record, subsequent coverage after late delivery, and the limits of extending one selected attempt's coverage to all attempts. The contribution is an execution-specific composition of scope, branch, horizon, obligations, admissibility, view, and cutoff. Completeness remains conditional on the declared profile and assessment premises.

cs.CR↗

arXiv2026

Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting

Accurately predicting mortality risk in intensive care unit (ICU) patients is critical for clinical decision-making. Large language models (LLMs) are increasingly explored for clinical prediction using structured medical data, but their outputs may exhibit demographic disparities. Mitigating such disparities without degrading predictive performance remains challenging. We systematically investigate demographic bias in LLM-based ICU mortality prediction and propose Case-Based Prompting (CAP), a training-free framework designed to improve the empirical balance between predictive performance and subgroup fairness. CAP retrieves clinically similar historical misprediction and demographic-sensitive cases with known outcomes and incorporates them as case-level contextual evidence. We further evaluate model discrimination, subgroup fairness, and feature-dependence consistency. On MIMIC-IV, CAP improved AUROC from 0.806 to 0.873 and AUPRC from 0.497 to 0.694 compared with the Baseline Prompt. CAP also reduced several subgroup disparities, particularly for sex and White-Black comparisons, although age-related disparities remained non-negligible. Controlled demographic-information ablations showed that removing demographic information did not uniformly improve both predictive performance and subgroup fairness, indicating distinct performance-fairness patterns rather than a uniform benefit from demographic suppression. Feature-dependence analysis showed generally consistent expressed clinical factor patterns across demographic subgroups. These findings suggest that CAP is a promising inference-time strategy for LLM-based clinical prediction under the evaluated retrospective setting. Further validation across additional LLMs, external datasets, and prospective clinical scenarios is required before deployment.

cs.LG↗

arXiv2026

Minimum Description Length based Granular-Ball Tree Regularization for Spectral Clustering

This paper proposes Minimum Description Length based Granular-Ball Tree Regularized Spectral Clustering (MDL-GBTRSC), a fixed-K method that uses granular balls as regional descriptors for sample-level affinity construction rather than as reduced graph nodes. A reciprocal-neighborhood continuity term is incorporated into an MDL-based refinement criterion to construct a granular-ball hierarchy, and the structural scales of the terminal regions are transferred to the original sample graph before spectral partitioning. Experiments on 18 real and 20 synthetic datasets, comprising 109,969 samples in total, compare MDL-GBTRSC with nine competing methods. On the 17 real datasets completed by all methods, MDL-GBTRSC achieves mean ARI and NMI values of 0.5998 and 0.6613, compared with the next-highest means of 0.5792 and 0.6285, respectively. On the 20 synthetic datasets, it achieves mean ARI and NMI values of 0.9618 and 0.9688, compared with the next-highest means of 0.7921 and 0.8306, and is best or tied on 15 datasets for both metrics. These results indicate that MDL-selected regional structure provides useful local information for affinity construction while preserving the resolution of the original spectral graph. Source code and experimental materials are available at https://github.com/AnonymousUser0816/MDL-GBTRSC.

cs.LG↗

arXiv2026

GAC-PINN: Geometry-Adaptive and Constraint-Enhanced Physics-Informed Neural Networks

For systems with steep gradients, sharp interfaces, or severe spatio-temporal coupling, Physics-informed neural networks (PINNs) suffer from spectral bias, geometric inflexibility, and boundary constraint conflicts, which undermine accuracy and convergence. To overcome these issues, we propose a geometry-adaptive and constraint-enhanced PINN (GAC-PINN). The framework comprises four components: a gradient-driven adaptive grid mapping (AGM) for diffeomorphic point concentration with Jacobian regularization, an adaptive bandwidth hard-constraint ansatz with spatially-varying boundary transition widths, a Gaussian Fourier feature mapping as a spectral preconditioner to further enhance high-wavenumber representation, and an operator-aware router that automatically selects the appropriate hard-constraint construction based on whether the governing PDE contains temporal derivatives. An AGM callback mechanism and a three-stage training strategy ensure stable coordination. Benchmarks including the viscous Burgers equation, a sharp-peaked 2D Poisson problem, and the Allen-Cahn phase-transition equation show that GAC-PINN attains relative (L^2) errors of ((1.747\pm 0.450)\times 10^{-4}), ((2.868\pm 0.947)\times 10^{-5}), and ((1.756 \pm 0.712)\times 10^{-3}), respectively, consistently outperforming the baselines. Ablation studies further reveal that AGM alone yields a substantially lower error than residual-based adaptive refinement (RAR), while RAR becomes beneficial only when combined with FFM, demonstrating a context-dependent module interaction. Convergence analysis verifies rapid error reduction and saturation with increasing resolution, establishing a practical adaptive framework for high-fidelity simulation of problems with localized sharp features in applied mechanics and computational physics.

math.NA↗

arXiv2026

Neumann eigenvalues of isosceles triangles: monotonicity and asymptotics

We prove Laugesen and Siudeja's conjecture on Neumann eigenvalues of isosceles triangles. After multiplication by the squared diameter, the first positive symmetric eigenvalue increases strictly with the apex angle $θ$, while the first antisymmetric eigenvalue decreases strictly up to the equilateral triangle and increases strictly thereafter. \rev{The proof combines a slice-average estimate of directional energies with the explicit equilateral eigenfunction.} A quadratic correction across slices also gives a two-term expansion for every fixed positive spectral index $k$ as $θ\downarrow0$, with relative correction $θ^2/6$ and remainder $O_k(θ^4)$.

math.SP↗

arXiv2026

Large-Amplitude Steady Solitary Water Waves with General Vorticity

We study two-dimensional steady solitary gravity water waves with general vorticity, allowing for overhanging free-surface profiles. The main challenges arise from the free boundary, the unbounded fluid domain, and the inherent complexity of the general vorticity setting. To address these, we introduce a conformal reformulation that reduces the problem to an equivalent system on a fixed strip, consisting of an overdetermined elliptic problem coupled with an elliptic boundary-value problem. This framework enables a local analysis without imposing restrictive assumptions on the vorticity. Using the center-manifold construction of Chen, Walsh and Wheeler \cite{chennonlinearity}, we first establish the existence of small-amplitude solitary waves for such non-trivial vorticity distributions. Subsequently, via a global analytic bifurcation argument, we prove the existence of continuous branches of large-amplitude solitary waves for analytic vorticity functions. Along these global solution curves, the free surfaces may develop overhanging profiles and need not remain graphs over the horizontal coordinate. Our results provide a new constructive framework for large-amplitude solitary waves with general vorticity, extending the existing theory beyond the classical settings of constant vorticity \cite{susannaarma} and non-overhanging profiles \cite{milesjma}.

math.AP↗