arXiv subjects

Chao Wu

Publications and source records attributed to Chao Wu.

At least 19 recordsLinked to original sources

arXiv2026

Massless-Massive Amplitude Correspondence III: Massive Amplitude Bases in the SMEFT

We develop a systematic correspondence between massless contact amplitudes in an unbroken theory and massive contact amplitudes after spontaneous symmetry breaking. Our construction employs the spin-transversality (ST) massive amplitude basis, with the systematic high energy expansion through minimal-helicity-chirality (MHC) amplitudes. The resulting $U(2)=SU(2)\times U(1)_t$ description of a massive particle makes the semi-standard Young-tableau construction of massless Lorentz structures directly applicable to massive amplitudes. When the leading-order MHC component has a massless contact limit, it is one-to-one matched directly to its UV amplitude. Otherwise, five exceptional classes of ST amplitudes are identified, their first non-zero descendant components are matched through conserved current couplings to the massless contact amplitude. We apply the framework to the one-flavor electroweak sector of the Standard Model Effective Field Theory (SMEFT) through dimension eight, obtaining explicit relations between unbroken-phase Wilson coefficients and broken-phase ST amplitude coefficients for amplitudes with three to eight external particles.

hep-ph↗

arXiv2026

HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing

Robotic manipulation with dexterous hands is a cornerstone of Embodied AI, yet its progress is stifled by the high cost of collecting embodiment-aware teleoperation data. While abundant egocentric videos of human hands offer a scalable alternative, the profound discrepancies in appearance, articulation, and camera viewpoints between human and robotic data raise significant challenges for co-training. Though existing general image-editing models demonstrate strong capabilities, they lack necessary embodiment-specific priors to fully bridge this gap. In this work, we present HandEdit, a unified large-scale embodiment-aware image-editing dataset and benchmark specifically designed to transform human hands and arms into various dexterous robotic embodiments within egocentric frames. HandEdit comprises over 200M editing instances derived from five diverse source datasets, covering 26 distinct URDFs, including 13 hand-only and 13 hand-arm configurations. Alongside the dataset, we establish a unified benchmark protocol with two tracks: Hand-only and Hand-Arm, supporting URDF-conditioned evaluation. We conduct extensive evaluations of 11 representative image-editing baselines using a multi-dimensional metric suite, including generic similarity metrics, VLM-based judgment, and embodiment-aware metrics. HandEdit serves as a critical resource at the intersection of image editing and robotics: it advances embodiment-aware editing models while enabling scalable dexterous robotic learning from abundant human video data, paving the way for more generalizable Embodied AI.

cs.RO↗

arXiv2026

Moving the Safety Barrier: Dynamic Routing Adaptive Alignment Against White-Box Attacks

With the widespread deployment of large foundation models (LFMs) in open environments, safety threats are shifting from black-box jailbreaks toward white-box attacks that directly identify and disrupt internal safety neurons or routes. However, existing safety defenses often rely on static safety units or fixed refusal pathways, leaving models highly vulnerable to targeted route-level white-box attacks. For that, we propose dynamic routing adaptive alignment (DRAA), a framework that introduces dynamic compensatory routes to preserve robust refusal behavior when the safety route is compromised. Specifically, we first identify and localize the model's safety route by contrasting internal activations between safe and unsafe calibration samples. DRAA then masks this safety route to induce causal failure cases and selectively mines the resulting defense failures, thereby constructing failure-aware preference pairs. Extensive experiments demonstrate that DRAA effectively restructures the underlying pathway dependence of model safety, substantially improving robustness against route-level white-box attacks, while preserving general utility.

cs.CR↗

arXiv2026

SERL-SQL: Selective Hindsight Distillation for Text-to-SQL Reinforcement Agentic Learning

Recent Text-to-SQL systems increasingly rely on multi-turn interaction, execution feedback, and reinforcement learning. However, most existing methods use execution correctness only as a trajectory-level reward, which provides limited guidance for identifying the SQL decisions responsible for success or failure. We propose SERL-SQL, a selective execution-grounded reinforcement learning framework for multi-turn Text-to-SQL agents. SERL-SQL samples on-policy SQL interaction trajectories and uses a training-only teacher to re-score student actions with execution feedback. The resulting teacher--student likelihood gap is converted into bounded, masked weights that reweight GRPO advantages only on SQL and tool-action tokens. In this way, task rewards preserve the optimization direction, while execution hindsight provides localized credit assignment. Experiments on BIRD, Spider, and cross-domain benchmarks show that SERL-SQL achieves competitive performance, reaching 76.56% execution accuracy on BIRD-Dev and 89.92% on Spider-Test. Moreover, our reward-based selection strategy closely approaches the oracle Best-of-N upper bound and consistently outperforms consistency-based selection, showing that SERL-SQL produces high-quality candidates that can be reliably identified by lightweight execution-grounded rewards. Our code will be released at https://github.com/Ffunkytao/SERL-SQL.

cs.CL↗

arXiv2026

EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration

Robust embodied robots should be able to recover from failures and retry tasks in order to operate reliably in unstructured and noisy real-world environments. Achieving this capability requires training policies on data that captures recovery behaviors. However, collecting such data through robot teleoperation is difficult to scale, as it is time-consuming to induce diverse failure states, perform corrective actions, and reset the environment. This challenge is further exacerbated by the high diversity of failure modes, which demands substantially more recovery data than success demonstrations. In this work, we show that egocentric human data capturing failure recovery processes provides a scalable alternative. By efficiently arranging task-level failure configurations and recording short recovery segments, human operators can generate more than 10x as much valid recovery data per hour compared to robot teleoperation under our protocol. To address the embodiment gap between human and robot, we propose EgoRecovery, a co-training framework for learning recovery behavior, where human recovery demonstrations are aligned to a compact corrective-intent space shared with robot data, which captures the timing and magnitude of correction. Only a small number of robot recovery demonstrations are required to connect this intent to executable robot actions. At deployment, a learned recovery gate predicts when correction is needed from robot observations and activates the corrective intent only in recovery states. Experiments on real-world recovery tasks show that EgoRecovery improves success from failure starts over robot-only recovery, direct co-training with human recovery data, and direct intent-transfer baselines.

cs.RO↗

arXiv2026

A Compact 3D-Printed Soft Finger with Cyclic Hydraulic Actuation

Hydraulic soft fingers offer compliant and gentle manipulation, but their practical deployment is limited by bulky fluidic hardware, fabrication complexity, and insufficient design validation. This paper presents a compact 3D-printed soft hydraulic finger driven by a miniature cyclic peristaltic loop. The finger integrates compliant bellows, rigid connectors, and fluidic ports, while an Abaqus fluid-structure model is used to guide selection of wall thickness, pitch angle, and bellows length. The selected design is validated through baseline-corrected chamber-pressure measurements and vision-based angle tracking. Results show that the quasi-static finite-element model captures the main pressure-angle trends, with remaining offsets mainly attributed to bonding-induced stiffness and hydraulic losses. Vision-feedback control further enables repeatable angle tracking over a large bending range. Finally, grasping tests on fragile and deformable objects, including tofu and blueberries, demonstrate gentle, slip-free contact without visible damage. Overall, this work establishes a reproducible pipeline from FEA-guided design to closed-loop validation for compact 3D-printed hydraulic soft fingers.

eess.SY↗

arXiv2026

Massive On-shell Splitting Functions in Spinor-Helicity Formalism

Collinear splitting functions govern parton evolution, parton showers, and resummation at high-energy colliders. While on-shell spinor-helicity methods have successfully yielded massless QCD splitting functions, a complete on-shell construction for massive particles, systematically incorporating finite-mass effects, is less developed. We present an on-shell constructive formalism for massive collinear splitting functions based on Soper-Weinberg collinear spinors, whose transformation properties follow from a light-front Galilean subgroup of the Poincar\'e group. Decomposing massive momenta and spinors with respect to fixed lightlike vectors $n$ and $\bar n$ makes the expansion in the alignment regime $m<p_T\ll p_+$ manifest. The leading-order structures are matched to massless three-point amplitudes, while an additional Higgs momentum along $\bar n$ probes the subleading spinor components and relates them to massless four-point amplitudes. We derive the complete set of leading and subleading massive splitting functions for all Standard Model particles and establish a systematic matching dictionary between massless and massive coupling coefficients at both the three- and four-point levels. Higher-point splitting functions are obtained through the recursive bootstrap relation with a universal substitution rule as a consequence of the Galilean symmetry. This constructive framework extends naturally to effective field theory operators and higher perturbative orders, providing a flexible computational tool for precision collider physics and parton shower development.

hep-ph↗

arXiv2026

KbSD: Knowledge Boundary aware Self-Distillation for Behavioral Calibration in Agentic Search

Agentic search equips large language models with dynamic retrieval abilities, but existing reinforcement learning methods remain limited by reward sparsity in knowledge boundary calibration -- deciding when to trust parametric memory, when to rely on retrieved evidence, and when to abstain. Binary rewards can penalize undesirable outcomes, but provide little guidance on the reasoning process required to make calibrated decisions across different knowledge states. To address this, we propose KbSD (Knowledge boundary Self-Distillation), a framework that tackles this limitation through dense token-level supervision, outcome-level sparse rewards, and quadrant-adaptive optimization. KbSD constructs a hint-augmented teacher, architecturally identical to the student, that receives explicit knowledge boundary signals -- including parametric certainty, retrieval quality, and ground-truth answers -- to generate calibrated reasoning demonstrations. This information-asymmetric self-distillation enables dense supervision without requiring a larger external model. To further account for the heterogeneous reasoning distributions across knowledge states, we introduce a quadrant-adaptive distillation objective: reverse KL for concentrated integration, forward KL for diverse refusal, and Pareto-optimal bidirectional KL for asymmetric quadrants requiring both precision and coverage. Experiments on multiple benchmarks show that KbSD consistently improves both task accuracy and hallucination mitigation over strong baselines, with the largest gains appearing in the challenging quadrants where sparse rewards are least informative.

cs.CL↗

arXiv2026

A Gossiping Protocol for Sparse Ad-Hoc Radio Networks

We study the problem of gossiping (all-to-all information exchange) in ad-hoc radio networks. Such a network is represented by a strongly-connected directed graph with $n$ vertices, whose topology is initially unknown to the protocol. In 2004, Gasieniec, Radzik, and Xin gave a $\tilde O(n^{4/3})$-time deterministic protocol for this problem, and closing the gap between their upper bound and the $\tilde\Omega(n)$ lower bound on the time complexity of gossiping remains a central open problem. We develop a deterministic protocol for gossiping in ad-hoc radio networks that achieves running time $\tilde O((mn)^{3/5})$ for directed graphs with at most $m$ edges. Our protocol improves on the $\tilde O(n^{4/3})$ bound when $m = O(n^c)$, for $c < 11/9$. We also present a $\tilde O(\Delta^{1/2} n)$-time gossiping protocol for $\Delta$-regular graphs.

cs.DS↗

arXiv2026

Sliding ferroelectricity tunable conventional and anomalous spin Hall effects in bilayer 1T'-WTe2

The spin Hall effect, recognized for its high-speed, low-power, and highly controllable characteristics, is a key enabler for next-generation memory and logic devices. However, a primary challenge lies in achieving 180$^{\circ}$ magnetization switching without an external magnetic field in spin-orbit torque devices. Here, we propose a method to tune the conventional and anomalous spin Hall effects by the intrinsic sliding ferroelectricity. Importantly, the anomalous spin Hall effect can enable the field-free switching of perpendicular magnetization. We find a substantial anomalous spin Hall conductivity of $\sigma_{xy}^{y}$ = 45.62 ($\hbar$/e)S/cm and $\sigma_{yx}^{y}$ = 56.84 ($\hbar$/e)S/cm in monolayer 1T'-WTe$_2$. These values are significantly enhanced to $\sigma_{xy}^{y}$ = -96.77 ($\hbar$/e)S/cm and $\sigma_{yx}^{y}$ = 104.03 ($\hbar$/e)S/cm in the bilayer 1T'-WTe$_2$. More interestingly, the sliding ferroelectricity enables reversible switching of the signs and magnitudes for both the conventional and anomalous spin Hall conductivities. This originates from the fact that the sliding ferroelectric markedly shifts the relative spin Berry curvature contributions from the valence and conduction bands around the $\Gamma$-X path. Our findings not only reveal a strong coupling between sliding ferroelectricity and spin transport, but also propose a strategy for the nonvolatile electrical control of spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Fast Optical Variability of the TeV Blazar PKS 1725+123 Observed by SVOM-VT and Insights from Multi-wavelength Follow-up Observations

PKS 1725+123 is a flat-spectrum radio quasar (FSRQ) with a redshift of $z=0.586$. The detection of this object in the TeV band was reported by the MAGIC telescopes and H.E.S.S. in August 2025. Subsequently, we promptly initiated Target-of-Opportunity observations using the Space-based multi-band astronomical Variable Objects Monitor (SVOM) satellite. By analyzing the observational optical data from SVOM-VT and comprehensively examining the Fermi-LAT and Swift-XRT observational data, it was found that the source is in a high-flux state across the optical, X-ray, and GeV $\gamma$-ray bands around the time of the TeV detections. Its optical flux reaches a historically unprecedented high level and shows significant variability on timescale as short as minutes. The variability is accompanied by changes in the color index, exhibiting a bluer when brighter behavior during the high-flux state. Based on the simultaneous multi-wavelength data, we construct the broadband spectral energy distribution (SED) of the source in the high-flux state. PKS 1725+123 demonstrates a remarkably high synchrotron peak frequency, which is distinctly different from that of other FSRQs. We propose a two-zone spine-sheath jet model to reproduce this SED. The optical--X-ray emission is generated by the synchrotron process of the relativistic electrons within a compact zone. The inverse Compton (IC) scattering processes of the same electron population contribute to the low-energy end of the Fermi-LAT spectrum, while the high-energy end of the Fermi-LAT spectrum is ascribed to the IC scattering of the synchrotron photons within the compact zone by the higher-energy electrons in an extended region.

astro-ph.HE↗

arXiv2026

GRB 250424A: A Case Study of Energy Injection with Multiwavelength Observations

We present a comprehensive multiwavelength analysis of the long-duration gamma-ray burst (GRB) 250424A. Our dataset spans from the prompt gamma-ray emission to late-time optical monitoring, including spectra obtained with the Keck 10\,m telescope. We find that the afterglow light curves display a prominent, simultaneous shallow decay phase in both X-ray and optical bands, followed by an achromatic transition to a standard decay regime. The broadband spectral energy distributions are well-modeled by a single power-law function, indicating a common synchrotron origin for the emission across frequencies. We interpret the afterglow evolution within the framework of a relativistic forward shock refreshed by continuous energy injection. This scenario successfully reproduces the observed temporal and spectral behavior, yielding an isotropic equivalent kinetic energy of $E_{\rm K,iso} \approx 5.5 \times 10^{52}$ erg and an injection index of $q\approx 0.34$ in a constant-density circumburst environment. The shallow decay phase is consistent with sustained energy injection lasting $\sim$ 9 ks. Despite the relatively low redshift, late-time optical observations reveal no distinct supernova component; however, our derived upper limits do not strictly rule out the presence of a typical GRB-associated supernova.

astro-ph.HE↗

arXiv2026

EvoMemNav: Efficient Self-Evolving Fine-Grained Memory for Zero-Shot Embodied Navigation

Building memory is essential for long-horizon planning in zero-shot embodied navigation. Detector-centric scene graphs often compress observations into sparse nodes, discarding fine-grained visual evidence and accumulating noise, while 3D reconstruction-based methods remain computationally prohibitive. We present EvoMemNav, an efficient, self-evolving, fine-grained memory framework for zero-shot embodied navigation. EvoMemNav constructs a Visual-Semantic Memory Graph (VSMGraph) that keeps raw views as first-class memory and organizes them with lightweight semantic cues and topological relations into a room-view-object hierarchy, preserving fine-grained details for disambiguation and Stop verification. To scale to growing memory, we introduce a budgeted coarse-to-fine policy: a coarse stage compresses the search space into promising regions, and a fine stage invokes a VLM only for targeted verification and decision. Beyond static memories, EvoMemNav performs reflection-driven write-back after each subtask, updating graph-attached priors that encode accumulated environmental knowledge to refine future decisions without retraining. Experiments on GOAT-Bench and HM3D across object, text-description, and image-goal modalities show consistent gains in SR/SPL, with better multi-instance disambiguation, fewer premature stops, and stronger zero-shot generalization.

cs.CV↗

arXiv2026

Revisiting the Voltage-Source Behavior: Why Impedance Magnitude of Grid-Forming Converter Rises Near Fundamental Frequency?

Grid-forming (GFM) converters are generally expected to exhibit low impedance near the fundamental frequency due to their voltage-source behavior. However, an impedance peak and a negative-resistance region are consistently observed in this range, which contradicts this expectation and lacks a clear physical explanation. This paper reveals that these phenomena originate from the inherent dynamics of the active power control loop, where the mapping from power disturbance to the synchronous angle inherently involves an integrative action, intrinsically preventing a positive-resistance characteristic near the fundamental frequency. This finding explains why existing grid codes in China, the United States, and Europe exclude a narrow band around the fundamental frequency in impedance-based evaluations. It is further shown that the width of the excluded frequency band (e.g., +/- 3~5 Hz) is governed by the power-to-frequency dynamics. Based on this insight, a quantitative index is proposed to determine the exclusion bandwidth from the corner frequencies of the impedance magnitude curve. The proposed index provides a concise and theoretically grounded criterion for voltage-source assessment and impedance standardization of GFM converters.

eess.SY↗

arXiv2026

GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization

Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Existing VLAs rely on end-to-end supervision to implicitly enable the action decoding process to learn task-relevant features. However, without explicit guidance, these models often overfit to spurious correlations, such as visual shortcuts or environmental noise, limiting their generalization. In this paper, we introduce GuidedVLA, a framework designed to manually guide the action generation to focus on task-relevant factors. Our core insight is to treat the action decoder not as a monolithic learner, but as an assembly of functional components. Individual attention heads are supervised by manually defined auxiliary signals to capture distinct factors. As an initial study, we instantiate this paradigm with three specialized heads: object grounding, spatial geometry, and temporal skill logic. Across simulation and real-robot experiments, GuidedVLA improves success rates in both in-domain and out-of-domain settings compared to strong VLA baselines. Finally, we show that the quality of these specialized factors correlates positively with task performance and that our mechanism yields decoupled, high-quality features. Our results suggest that explicitly guiding action-decoder learning is a promising direction for building more robust and general VLA models.

cs.RO↗

arXiv2026

SVOM/VT: Instrument Overview, Science Objectives, and First-Year Performance

The 44-cm Visible Telescope (VT) aboard the Space-based Variable Objects Monitor (SVOM) is a dual-band (400-650 nm and 650-1000 nm) instrument designed to detect and characterize the optical counterparts of gamma-ray bursts (GRBs) and other high-energy transients. This paper presents the VT's design, scientific objectives, observing strategies, and both space- and ground-based data processing pipelines, along with its first-year in-orbit performance. In-orbit commissioning tests confirm a sensitivity of 22.5 AB mag (300 s exposure), extendable to $\sim\!24$ AB mag through stacking. This performance enables the VT to monitor over 100 GRBs in its first year with an exceptional $\sim\!80\%$ detection rate for \textit{SVOM}/ECLAIRS-triggered bursts and ToO-observed bursts from other missions (e.g., \textit{Swift, Fermi, Einstein Probe (EP)}), outperforming \textit{Swift}/UVOT's $\sim\!40\%$ detection rate. Beyond its exceptional detection efficiency, the VT played a key role in identifying high-redshift GRBs-most notably GRB 250314A (z = 7.3). Its deep upper limits at long wavelengths (up to 1 $\mu$m) were pivotal in guiding follow-up observations with large ground-based telescopes, enabling crucial near-infrared (NIR) detections. With its rapid response, deep sensitivity, and real-time processing capabilities, the VT is a key instrument for GRB research in \textit{SVOM}-era, enabling critical studies of GRB optical afterglows, circumburst environments, relativistic jet dynamics, and the origins of optically dark bursts.

astro-ph.HE↗

arXiv2026

SVOM Science User Support Services at Chinese Science Center

The Chinese-French SVOM (Space-based Multi-band Astronomical Variable Objects Monitor) mission is dedicated to the study of gamma-ray bursts (GRBs) from the distant universe. A key component of the SVOM Chinese Ground Segment, the Science User Support Services (SUSS) provides comprehensive support for the mission's scientific operations. SUSS consists of two integral pillars: a suite of specialized software tools that automate key workflows, and a dedicated User Support Team that delivers expert-led, human services. These human-delivered services include operational coordination across telescope networks, direct technical assistance to astronomers, user training, and proactive problem-solving throughout the observation lifecycle. This paper focuses on the organization of SVOM scientific operations and the role of SUSS in facilitating these tasks. We provide a detailed description of the SUSS software architecture and its functionalities, encompassing the General Platform, the Burst Advocate (BA) support tools for GRB counterpart identification, the Target of Opportunity (ToO) support tools, and the General Program (GP) support tools. The structure and services provided by the user support team at the Chinese Science Center (CSC) are also elaborated. Furthermore, we evaluate the performance of SUSS during its first operational year, assessing its effectiveness in fulfilling user requirements. The evaluation offers valuable insights to guide future user support strategies and software enhancements, ultimately enabling better service for the SVOM scientific community.

astro-ph.IM↗

arXiv2026

SVOM/VT: Preliminary Calibration Analysis

We present the in-orbit calibration of the Visible Telescope (VT), one of the key instruments aboard the Space Variable Objects Monitor (SVOM) mission for gamma-ray burst (GRB) studies. Using Gaia Data Release 3 (DR3) as a reference, the VT achieves an astrometric precision better than 0.03'' for bright stars, degrading to ~0.25'' for faint targets. Shortly after launch, contamination was detected, reducing system transmission by ~40%. An initial bake-out successfully restored performance, but gradual recontamination caused transmission to decline by ~20% over the following 100 days before stabilizing. Despite this effect, routine standard star observations maintain precise zero-point calibration, ensuring a photometric stability of 0.02 mag. Using synthetic stellar spectra, we derived photometric transformations to the Gaia, SDSS, and Johnson-Cousins systems with typical residuals of 0.03 mag. These results demonstrate the VT system's capability and reliability in calibrating GRBs and other transients.

astro-ph.HE↗