arXiv subjects

Yue Zhao

Publications and source records attributed to Yue Zhao.

At least 73 records · Page 4Linked to original sources

arXiv2026

Optical Counterparts to X-ray sources in LSST DP1

We present a crossmatch between a combined catalog of X-ray sources and the Vera C. Rubin Observatory Data Preview 1 (DP1) to identify optical counterparts. The six fields targeted as part of DP1 include the Extended Chandra Deep Field South (E-CDF-S), the Euclid Deep Field South (EDF-S), the Fornax Dwarf Spheroidal Galaxy (Fornax dSph), 47 Tucanae (47 Tuc) and science validation fields with low galactic and ecliptic latitude (SV\_95\_-25 and SV\_38\_7, respectively). We find matches to 2314 of 3830 X-ray sources. We also compare our crossmatch to DP1 in the E-CDF-S field to previous efforts to identify optical counterparts. The probability of a chance coincidence match varies across each DP1 field, with overall high reliability in the E-CDF-S field, and lower proportion of high-reliability matches in the other fields. The majority of previously known sources that we detect are, unsurprisingly, active galaxies. We plot the X-ray-to-optical flux ratio against optical magnitude and color in an effort to identify Galactic accreting compact objects using a {\em Gaia} color threshold transformed to LSST $g$--$i$, but do not find any strong candidates in these primarily extragalactic counterparts. The DP1 dataset contains high-cadence photometry collected over a number of nights. We calculate the Stetson $ J $ variability index for each object under the hypothesis that X-ray counterparts tend to exhibit higher optical variability; however, the evidence is inconclusive whether our sample is more variable over DP1 timescales when compared to field objects.

astro-ph.HE↗

arXiv2026

Fail-Aware and Explainable Test Oracle Prediction

Despite their central role in fault detection, test oracles remain challenging to construct effectively. Recent learning based methods address this challenge by automatically generating test assertions, yet even if syntactically correct, they are often ineffective in revealing bugs. Rather than generating assertions, this study explores a different approach by training a model to directly predict whether a given test prefix passes or fails. We present FOCAL, an emerging code LLM-based discriminative oracle predictor. It learns from labeled pairs of test prefixes and methods under test, employs losses that emphasize failing cases during training, and grounds its predictions in statement level behavioral evidence. Compared with the baseline method SEER, we substantially improve performance on failing cases for unseen projects and provide richer explanations. A preliminary evaluation on fault-detection benchmarks and automated test-generation artifacts shows that our approach is highly accurate within its training distribution and substantially improves failure detection on previously unseen projects where prior discriminative oracles collapse. Moreover, the highlighted statements are supported by behavioral explanation checks. These early results suggest that fail-aware discriminative oracle prediction can complement existing approaches such as fuzzing, search-based testing, and LLM-based test generation. These techniques produce test prefixes at scale but often lack fault oriented oracles. In future work, FOCAL could take generated test prefixes and attach fault-aware predicted oracles to them, turning high-volume input generation into executable tests that are more likely to expose semantic failures.

cs.SE↗

arXiv2026

Discovery of a symmetry-driven electronic cascade in a $d$-wave altermagnet

Altermagnets host magnetic compensation together with non-relativistic spin-split bands, a coexistence enabled by crystal symmetry. Yet whether and how crystal symmetry organizes collective electronic order remains largely unexplored. Here we uncover a symmetry-driven cascade of finite-$q$ charge order in a $d$-wave altermagnet Rb$_{1-δ}$V$_2$Te$_2$O, using phase-resolved scanning tunneling microscopy. A primary density-wave instability drives an initial electronic reconstruction, followed by the emergence of a nematic component and an off-axis modulation with wave vectors geometrically tied to the preceding orders. Phase-resolved spectroscopy distinguishes these components through separate contrast-inversion energies and maps out branch-selective spectral-weight redistribution within the off-axis mode. Together with doping and temperature evolution, these observations establish a highly coordinated hierarchy of coupled density-wave instabilities, consistent with successive symmetry lowering. This multi-component hierarchy can be well described in the Landau framework through sequential softening of the charge orders, where bilinear coupling to their compatible octupolar partners at the lower-symmetry stages enables mutual stabilization within an intertwined charge-multipole state. Such a transparent realization of a charge-order cascade shows how altermagnetic symmetry can extend beyond band formation to organize collective electronic order, offering a new perspective on emergent many-body states in correlated quantum materials.

cond-mat.mes-hall↗

arXiv2026

TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination

Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines. We identify a structural failure mode in sequential fine-tuning of shared-context teams: updating one agent shifts the team's context distribution, and when subsequent updates are evaluated on cached rollouts, this mismatch compounds. We formalize this as the compounding occupancy shift and prove that stale-occupancy evaluation incurs a penalty that scales quadratically with the number of agents. In contrast, intermediate-occupancy evaluation reduces this to linear scaling. We propose TeamTR, a trust-region framework that resamples trajectories after each component update and enforces per-agent divergence control, yielding rigorous per-update and per-stage improvement lower bounds. Experiments show that TeamTR outperforms single-agent and sequential baselines with 7.1% on average, mitigates coordination regressions, and supports plug-and-play component replacement. Code is available at https://github.com/Yydc/TeamTR.

cs.LG↗

arXiv2026

SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents

Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but correct, secure, and maintainable. We introduce SkillCenter, to our knowledge the largest open skill library for agents by total count: 216,938 structured skills across 24 domain bundles. A SkillGate-filtered pipeline contributes 114,565 source-grounded skills from peer-reviewed journals, ArXiv, and over 24,000 technical sources, integrated with 102,373 community skills from GitHub and the ClawHub marketplace. We present the end-to-end framework that builds the pipeline subset: multi-source acquisition, an LLM-based quality gate (SkillGate), template-driven generation, iterative source-grounding, and quality-controlled publishing. Source grounding is a traceability guarantee: each retained claim maps to an exact quotation in its source. All skills ship as offline-searchable SQLite FTS5 bundles.

cs.AI↗

arXiv2026

Hall Coefficient Sign Reversal Driven by Orbital-Selective Oxygen-Vacancy Scattering in Nickelate Films

Hall measurements in superconducting bilayer nickelate films show sign reversals that cannot be explained by rigid-band electron doping alone. We combine a DFT+CDMFT-derived correlated multi-orbital quasiparticle model with a $T$-matrix treatment of oxygen-vacancy scattering in a semiclassical Boltzmann transport framework. We find that multiband compensation is insufficient by itself: in-plane vacancies selectively suppress the transport channel dominated by the $d_{x^2-y^2}$ orbital and drive $R_H$ through zero, whereas inner-apical vacancies make $R_H$ more negative. These results identify pocket-resolved and orbital-selective oxygen-vacancy scattering as the microscopic origin of the Hall coefficient sign reversal and provide a framework for oxygen-stoichiometry-dependent transport in nickelate films.

cond-mat.supr-con↗

arXiv2026

Probing Quantum Geometric Phases via Scanning Tunneling Microscopy

The quantum geometric phase intrinsically dictates the geometry, topology, and many-body correlations of electronic wave functions. While quantum geometric phases are conventionally inferred through momentum-space probes or macroscopic transport measurements, their direct visualization and quantification in real space have historically been restricted by the spatial averaging of bulk techniques. Scanning tunneling microscopy and spectroscopy (STM/STS) circumvent this limitation, leveraging atomic-scale spatial resolution and high energy sensitivity to resolve local electronic phase profiles directly. This review highlights recent progress across four representative methodologies: probing the Aharonov-Bohm (AB) geometric phase via nanoscale real space interferometry; extracting the Berry phase from defect-induced quasiparticle interference and wavefront dislocations; reconstructing the complex phase structure in symmetric systems, such as magic-angle graphene, using order parameter decomposition; and mapping the phase textures and topological defects of pair density wave (PDW) and charge density wave (CDW) in unconventional superconductors utilizing the numerical 2D lock-in technique. Together, these developments show how quantum phases can be translated onto real space and locally resolvable observables. Phase-resolved STM imaging provides stringent constraints on topological states of matter, symmetry-breaking patterns, and strong electronic correlations, outlining a robust framework for in situ phase engineering in quantum materials.

cond-mat.mes-hall↗

arXiv2026

SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution

Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers emit only opaque binary labels, leaving agents unable to self-correct and operators unable to audit. We present SEVA, a structured verification agent that emits evidence alignments, step-by-step reasoning chains, calibrated confidence, and a six-category error diagnosis with actionable fixes. Training such an agent with RL is non-trivial: standard binary reward on multi-component output triggers advantage collapse -- within-group reward variance vanishes and the GRPO gradient disappears. We resolve this with a process reward that decomposes verification quality into five independent components weighted 70/30 toward process signals, restoring the gradient and inducing an implicit curriculum -- the agent first masters verification behavior (alignment 0.917 -> 0.997, format 72% -> 100%), then outcomes (F1 64.9 -> 69.0). Structured output further enables a Verify -> Reflect -> Probe -> Refine self-evolution loop, which over four rounds on a 7B model surfaces an unexpected structural finding: each round produces a benchmark-specialist, not a generalist (+15 pp on HaluEval, -10 to -14 pp on TruthfulQA in the same model, persistent at 4x data). On ClearFacts, SEVA-3B matches GPT-4o-mini (69.0 vs. 69.8 F1) while producing substantially richer, auditable output -- confirming a principle that should generalize: for any RL task with multi-component generation, reward granularity must match output granularity.

cs.CL↗

arXiv2026

Agent Safety Is Action Alignment

Large language models increasingly act as agents: they call tools, move money, delete records, and send messages on a user's behalf. To keep them safe, practitioners imported the chatbot-era recipe (train the model to refuse unsafe inputs) into the agentic setting, and treat the resulting capability loss as a manageable ``alignment tax.'' We argue this is a \emph{category error}. Refusal is a primitive for \emph{content safety}, where the harm is in the model's output and is therefore a learnable function of it. Agentic harm is different in kind: it lies not in any output but in the relation between the authority an action exercises and the authority the user granted, which is absent from the text the model sees. Importing content-safety methods into this regime does not trade capability for safety; it pays capability and buys negative security. We support this with three lines of evidence spanning the autonomy spectrum: defense-trained models learn surface patterns rather than intent; the same training collapses multi-step agents before any threat appears while leaving them exploitable; and even undefended frontier models exceed granted authority under ordinary use. We conclude that action safety cannot be installed in weights. It must be expressed as \emph{least privilege}, enforced \emph{outside} the model at the action boundary, and evaluated as \emph{action alignment} (a relational, deployment-conditioned property) rather than a refusal score.

cs.AI↗

arXiv2026

New Gauge Forces, Neutron Stars and Schwinger Neutrino Production

We investigate neutrino effects of new long-range forces arising from gauging $B-L$, $L_e-L_{μ/τ}$ or $L_μ-L_τ$ symmetries of the Standard Model. The leptonic potential generated by astronomical bodies, such as the Earth, the Sun or a neutron star, results in the Schwinger pair production of neutrinos charged under the new gauge symmetry. The oppositely charged particles accumulate in the potential well forming a degenerate Fermi gas, while equally charged particles fly away forming a steady flux of neutrinos. We find that, for the $B-L$ and $L_e-L_{μ/τ}$ forces, these effects are too weak to be observable. For the $L_μ-L_τ$ force these effects are significant in neutron stars if the gauge coupling is $g\gtrsim 10^{-18}$. The muonic force changes the element abundances of a neutron star in equilibrium and suppresses its $L_μ-L_τ$ charge. This invalidates the constraint on $g$ from neutron star mergers, at $g\gtrsim 10^{-17}$. Furthermore, for such values of $g$, the neutrino flux produced by the Schwinger effect could potentially be detected from a single young neutron star at a distance of $\simeq 100$ pc, with the typical neutrino energy $E_ν\sim 100$ MeV. A dedicated search for such a signal will reassert the bound $g\lesssim 10^{-18}$.

hep-ph↗

arXiv2026

EmoFSM: A Finite State Machine for Emotional Support Conversation

Emotional support conversation (ESC) aims to alleviate people's emotional distress through effective conversations. Although large language models (LLMs) have made remarkable progress in ESC, most of these studies may not define the diagram from a state-model perspective, thereby providing a suboptimal solution for long-term satisfaction. To address such an issue, we leverage the Finite State Machine (FSM) on LLMs, and propose a framework called EmoFSM. Our framework allows a single LLM to bootstrap the planning during ESC, and self-reason the seeker's emotion, support strategy, and the final response upon each conversation turn. Substantial experiments in ESC datasets suggest that EmoFSM outperforms many baselines, including direct inference, self-fine, chain of thought, finetuning, and externally supported methods, even those with many more parameters.

cs.CL↗

arXiv2026

FORTIS: Benchmarking Over-Privilege in Agent Skills

Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This layer is widely treated as an organizational abstraction, but we argue it is also a privilege boundary that current models routinely exceed. We present \textbf{FORTIS}, a benchmark that evaluates over-privilege in agent skills across two stages: whether a model selects the minimally sufficient skill from a large overlapping library, and whether it executes that skill without expanding into broader tools or actions than the skill permits. Across ten frontier models and three domains, we find that over-privileged behavior is the norm rather than the exception. Models consistently reach for higher-privilege skills and tools than the task requires, failing at both stages at rates that remain high even for the strongest available models. Failure is especially severe under the ordinary conditions of real user interaction: incomplete specification, convenience framing, and proximity to skill boundaries. None of these requires adversarial construction. The results indicate that the skill layer, far from containing agent behavior, is itself a primary source of privilege escalation in current systems.

cs.AI↗

arXiv2026

Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) has become a cornerstone of knowledge-intensive applications, including enterprise chatbots, healthcare assistants, and agentic memory management. However, recent studies show that knowledge-extraction attacks can recover sensitive knowledge-base content through maliciously crafted queries, raising serious intellectual property and privacy concerns. While prior work has explored individual attack and defense techniques, the research landscape remains fragmented, spanning heterogeneous retrieval embeddings, diverse generation models, and evaluations based on non-standardized metrics and inconsistent datasets. To address this gap, we introduce the first systematic benchmark for knowledge-extraction attacks on RAG systems. Our benchmark covers broad attack/defense strategies, representative retrieval embedding models, open/closed-source generators, (non) graph-based indexing, all evaluated under a unified experimental framework with standardized protocols across multiple datasets spanning diverse languages. By consolidating the experimental landscape and enabling reproducible, comparable evaluation, this benchmark provides actionable insights and a practical foundation for developing privacy-preserving RAG systems in the face of emerging knowledge extraction threats.

cs.CR↗

arXiv2026

Multimodal Generative Engine Optimization: Rank Manipulation for Vision-Language Model Rankers

Vision-Language Models (VLMs) integrate visual and textual knowledge into unified representations that increasingly underpin modern retrieval and recommendation systems. However, it remains unclear how reliably these models utilize their cross-modal knowledge when ranking multimodal items, and whether their knowledge grounding can be subverted. In this paper, we expose a fundamental vulnerability in how VLMs apply multimodal knowledge for product ranking: through Multimodal Generative Engine Optimization (MGEO), we show that an adversary can manipulate a VLM's ranking decisions by jointly crafting imperceptible image perturbations and fluent textual suffixes that exploit the model's internal cross-modal knowledge coupling. Using an alternating optimization strategy, MGEO targets the deep interactions between visual and linguistic representations within the VLM, achieving rank manipulations that substantially exceed those of unimodal attacks and heuristic baselines powered by strong commercial models. Our findings reveal that surface-level content quality is insufficient for rank promotion; instead, direct alignment with the model's internal knowledge utilization mechanism is required. These results raise important questions on the faithfulness and robustness of knowledge grounding in multimodal foundation models, and motivate future work on defense mechanisms for multimodal retrieval systems. Code is available at: https://github.com/glad-lab/MGEO

cs.CL↗

arXiv2026

EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets

Egocentric RGB-D videos offer a natural source of human dexterous manipulation demonstrations, but existing data is difficult to use for robot learning because object pose, geometry, and contact information are often missing or require pre-scanned object assets. We present EgoAERO, the first framework that learns dexterous manipulation from a single egocentric RGB-D human demonstration without object assets. EgoAERO reconstructs contact-consistent hand-object trajectories through asset-free object tracking and reconstruction, ego motion compensation, and adaptive contact optimization, then converts them into robot policies using two-stage residual learning. We further introduce an online quality assessment mechanism and construct EgoDex-R, a large-scale egocentric dataset with 4.3M RGB-D frames for dexterous policy learning. Simulation and real-world experiments show that EgoAERO enables single-demonstration dexterous manipulation and achieves downstream performance close to CAD-based reconstructions on HOI4D.

cs.RO↗

arXiv2026

Fast spectral separation method for kinetic equation with anisotropic non-stationary collision operator retaining micro-model fidelity

We present a generalized, data-driven collisional operator for one-component plasmas, learned from molecular dynamics simulations, to extend the collisional kinetic model beyond the weakly coupled regime. The proposed operator features an anisotropic, non-stationary collision kernel that accounts for particle correlations typically neglected in classical Landau formulations. To enable efficient numerical evaluation, we develop a fast spectral separation method that represents the kernel as a low-rank tensor product of univariate basis functions. This formulation admits an $O(N \log N)$ algorithm via fast Fourier transforms and preserves key physical properties, including discrete conservation laws and the H-theorem, through a structure-preserving central difference discretization. Numerical experiments demonstrate that the proposed model accurately captures plasma dynamics in the moderately coupled regime beyond the standard Landau model while maintaining high computational efficiency and structure-preserving properties.

math.NA↗

arXiv2026

Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs

Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA (Multi-Agent Memory Attack), a controlled evaluation framework for comparing topology-conditioned memory leakage in multi-agent LLM systems. MAMA operates on synthetic documents containing labeled Personally Identifiable Information (PII) entities, from which we generate sanitized task instructions. We execute a two-phase protocol: Engram (seeding private information into a target agent's memory) and Resonance (multi-round interaction where an attacker attempts extraction). Over 10 rounds, we measure leakage using a two-stage recovery criterion that combines exact-match extraction with LLM-based inference over the attacker's final output. We evaluate six canonical topologies (complete, circle, chain, tree, star, star-ring) across $n\in\{4,5,6\}$, attacker-target placements, and base models. Results are consistent: denser connectivity, shorter attacker-target distance, and higher target centrality increase leakage; most leakage occurs in early rounds and then plateaus; model choice shifts absolute rates but preserves broad structural trends; spatiotemporal/location attributes leak more readily than identity credentials or regulated identifiers. We distill practical guidance for system design: favor sparse or hierarchical connectivity, maximize attacker-target separation, and restrict hub/shortcut pathways via topology-aware access control. Our code is available at https://github.com/llll121/mama-eval.

cs.CR↗

arXiv2026

Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data

Identifying subtypes of complex conditions, such as Inflammatory Bowel Disease (IBD), often requires capturing latent patterns in longitudinal omics data. However, these data are typically high-dimensional, sparsely sampled, and irregularly observed over time, posing substantial challenges for conventional (bi)clustering and functional data analysis methods. We propose Tri-SfSVD, a unified sparse functional Singular Value Decomposition framework for discovering biclusters and triclusters in longitudinal data. Unlike existing functional biclustering methods that rely on ad hoc imputation or enforce restrictive shape-homogeneity assumptions, Tri-SfSVD integrates continuous trajectory estimation with simultaneous subject, feature, and temporal selection within a single optimization framework. By imposing sparse penalties across subjects, variables, and temporal subregions, the proposed method works directly on observed data to uncover localized structures at the subject, subject-feature, and subject-feature-time levels. Extensive simulations demonstrate that Tri-SfSVD outperforms existing approaches in high-dimensional settings. Applied to IBD multi-omics data, the method identified three biclusters linking sample clusters with distinct IBD-related clinical characteristics to microbial pathway groups associated with specific bacterial taxa, providing interpretable subject-pathway associations for characterizing disease heterogeneity. Applied to multi-channel EEG data, the method identified three triclusters linking sample clusters with distinct alcohol-related phenotypes to localized brain activity patterns, including subgroup differences separated by temporal subregions within the same spatial region.

stat.ML↗