arXiv subjects

Yibo Wang

Publications and source records attributed to Yibo Wang.

At least 19 recordsLinked to original sources

arXiv2026

A Unified Timescale Relation for Quasi-Periodic Eruptions and Repeated Nuclear Transients

Quasi-periodic eruptions (QPEs) and recurrent nuclear transients (RNTs) exhibit recurrent high-amplitude flares from galactic nuclei, yet their characteristic timescales remain poorly understood. In this work, we compile a sample of these systems and investigate empirical scaling relations between flare timescales and black hole masses. We find that the recurrence timescale exhibits a positive but highly scattered dependence on black hole mass in the combined QPE and RNT sample, approximately following $t_{\rm rec}\propto M_{\rm BH}^{1.19^{+0.52}_{-0.47}}$ with an intrinsic scatter of 0.95 dex. Remarkably, we uncover a tight nearly linear relation between recurrence time and flare timescale for QPEs and RNTs, described by $t_{\rm rec}\propto t_{\rm rise}^{1.00\pm0.07}$ with an intrinsic scatter of 0.29 dex. This relation extends across timescales from hours for QPEs to months and years for nuclear transients. We further find that QPEs and RNTs approximately follow a common empirical relation between recurrence time and flare rise time, although the physical origin of this relation remains uncertain. Our findings reveal a common phenomenological timescale link across RNTs, providing a practical framework for characterizing their temporal behavior.

astro-ph.HE↗

arXiv2026

Learning Compositional Spatio-Temporal Video Grounding with Synthetic Curriculum

Despite the impressive progress of recent MLLMs on spatio-temporal video grounding (STVG), existing evaluations and training data focus primarily on simple queries. They largely overlook the compositional queries prevalent in real-world scenarios, where a target must be disambiguated by jointly reasoning about its attributes and relations to other entities. To bridge this gap, we propose Compositional Spatio-Temporal Video Grounding (CompSTVG), a task that requires models to process complex textual queries where every intertwined attribute and relational cue is essential for disambiguation. To facilitate this task at scale, we build a synthetic data engine that leverages a spatio-temporal scene graph as a difficulty measure and casts difficulty-controlled query synthesis as a constraint programming problem, producing difficulty-graded data for both evaluation and training. Built on this engine, we introduce STVG-CompBench, a benchmark stratified by explicit difficulty levels that jointly capture temporal complexity and spatial interference. Evaluating 11 representative STVG models on STVG-CompBench reveals that current models perform poorly on compositional queries, exhibiting a sharp performance drop that is typically obscured by overall dataset-level averages. We further construct synthetic training data and propose CurrSTVG, a curriculum reinforcement learning framework that delivers consistent gains, with the largest improvements observed on the most challenging compositional queries.

cs.CV↗

arXiv2026

Random attractors and almost-sure stability under discretization of a stochastic autoparametric system

For a stochastic autoparametric block-and-pendulum system, the long-time dynamics exhibit two fundamental features: the almost-sure stability of the single mode solution, characterized by its Lyapunov exponent, and the global asymptotic dynamics when this single mode solution loses stability. This naturally raises the question of whether these dynamical features are preserved under discretization, since such preservation is essential for the resulting discrete system to faithfully capture the qualitative behavior of the continuous system. To address this question, we first establish the existence of a random attractor for the continuous system subject to multiplicative stochastic excitation, providing a rigorous characterization of the global asymptotic dynamics. We then propose a numerical discretization that induces a discrete random dynamical system and prove the convergence of its random attractor to the continuous one as the step size tends to zero. In addition, we show that the numerical Lyapunov exponent of the single mode solution has the same sign as its continuous counterpart for sufficiently small step sizes, thus preserving the corresponding almost-sure stability or instability classification. These results demonstrate that the proposed discretization captures both the global asymptotic dynamics and the stability characteristics of the underlying stochastic autoparametric system.

math.DS↗

arXiv2026

CAFE: Self-Improving Search Agents Need Co-Evolving Feedback

Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongoing trajectory before those errors compound. Treating corrective feedback as a learned in-trajectory intervention couples the two roles: the agent must decide when to request and use feedback, while the critic must infer useful corrections from outcome-confounded rollouts whose failure patterns shift as the agent improves. We introduce CAFE (Coupled Agent--Feedback Evolution), a framework in which a shared-parameter model alternates between search-agent and critic roles. CAFE initializes feedback-conditioned recovery from trajectories built around the base agent's own failures, then couples online and offline optimization. During online RL, a comparative feedback estimate uses a prompt-level call--skip success gap to shape request returns, while feedback-aware advantage shaping reweights token advantages before and after feedback. Offline, rollout-derived preference optimization learns feedback from matched successful and unsuccessful trajectories. On seven agentic search benchmarks, CAFE outperforms the evaluated RL-based search agents on average, retains its gains across all six out-of-domain benchmarks, and reduces answer-level hallucinations. One-sided ablations show that improving only the agent or only the critic eventually plateaus, whereas alternating the two updates continues to improve performance. These findings suggest that a self-improving search agent needs feedback that co-evolves with the policy it guides.

cs.AI↗

arXiv2026

AffectOmni: RL-Verifiable People-Centric Grounded Affective Reasoning for Social and Art-Related Scenes

Multimodal large language models (MLLMs) achieve strong performance on VQA and scene understanding, yet affective reasoning remains vulnerable to shortcut behavior. Models may predict correct answers while neglecting people-centric cues such as micro expressions and body language, which weakens traceability and external verification. Prior reinforcement learning approaches mainly reward context or logical coherence without explicitly enforcing attention to human evidence. In addition, LLM as a Judge scoring often suffers from score clustering, which reduces reward discriminability. We propose AffectOmni, a GRPO trained framework for verifiable affective reasoning. AffectOmni introduces People Focus and Temporal Order rewards to encourage people-centric evidence selection and temporally structured reasoning, and it adopts within-group comparative scoring to produce more stable and discriminative reward signals. For verification, a Thinking Summarizer converts free form rationales into executable evidence instructions, which are grounded into pixel level evidence regions via SAM3 to provide an externally auditable interface outside the training loop. Experiments on IntentBench, Daily Omni, and WorldSense show consistent improvements over open source 7B scale baselines, including gains of 4.66% on emotion recognition and +14.29% on temporally sensitive tasks. Code is available at https://github.com/eliot127825-rgb/AffectOmni_nobody.

cs.AI↗

arXiv2026

Higher Chern--Simons Theory in $2n+2$ Dimensions for Balanced 2-term $L_\infty$-Algebras

We construct a semistrict higher Chern--Simons (HCS) gauge theory in $2n+2$ dimensions associated with balanced 2-term $L_\infty$-algebras. Starting from the homotopy Maurer--Cartan theory, we first introduce 2-term $L_\infty$-algebra gauge theory, and show that there is a four-dimensional HCS construction. Then we extend invariant bilinear pairings to invariant multilinear forms of the appropriate degree, and define a $(2n+3)$-dimensional higher Pontryagin--Chern form, which is closed and invariant under infinitesimal gauge transformations. Its transgression yields an explicit $(2n+2)$-dimensional HCS form. We further establish a higher Chern--Weil theorem that generates higher transgression forms, and prove that the HCS theory is a distinguished instance of the higher transgression gauge theory. Finally, we apply the extended Cartan homotopy formula in this semistrict setting, and show that it is a common origin of both the higher Chern--Weil theorem and the associated triangle equation.

hep-th↗

arXiv2026

Morphology-Guided Deterministic Fabrication of Low-Noise High-Temperature Superconducting Quantum Interference Devices

Reproducible bicrystal high-temperature superconducting quantum interference devices remain limited by local variability along the grain boundaries that form the Josephson junctions. Here, we develop a site-selective fabrication workflow in which atomic force microscopy maps the intended junction region before lithography, quantifies an apparent grain-boundary width, rejects pore-rich segments, and writes a nearby registration mark for site-specific pattern alignment. The apparent grain-boundary width provides a practical morphology metric, with narrower regions consistently yielding larger critical currents and characteristic voltages. Iterative optimization within this workflow further improves junction and device performance, reaching a liquid-nitrogen-temperature field-noise level of 40 fT Hz^(-1/2). This strategy turns local grain-boundary heterogeneity from an uncontrolled source of variability into a basis for site-selective fabrication, providing a route towards scalable manufacturing of low-noise HTS SQUIDs with high uniformity.

cond-mat.mes-hall↗

arXiv2026

Personalized Recommendation Tool Learning via Autonomous Language Agents

Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks. To circumvent these limitations through architectural design rather than modifying the LLM itself, we propose an agent-based recommendation framework, memory-based $\textbf{P}$ersonalized $\textbf{R}$ecommendation $\textbf{T}$ool learning via autonomous language $\textbf{A}$gents (PRTA), in which an LLM acts as a central planner interacting with multiple recommendation models as tools. The LLM-based agent is responsible for high-level reasoning and personalized tool selection, while traditional recommendation models perform full-ranking scoring, leveraging their scalability in modeling behavioral patterns. To support personalized tool selection, we design reflection mechanisms that enable the agent to evaluate and compare tools for each user based on user profiles and candidate ranked lists. Extensive experiments across three public datasets demonstrate the superiority of \modelname over traditional recommendation and LLM-based baselines in improving full-ranking recommendation performance.

cs.IR↗

arXiv2026

Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning

Egocentric videos of human manipulation provide scalable supervision for embodied intelligence, yet existing resources rarely combine low-cost continuous capture, manipulation-level structured annotations, and reusable tools for robot learning. We present Open-AoE, an open, community-oriented egocentric manipulation dataset and toolchain spanning the full pipeline from smartphone capture to model training. Its first release contains approximately 2,000 hours of manipulation video collected in natural environments by 500+ contributors using 400+ smartphones. The dataset provides text annotations, MANO-based hand poses, camera trajectories, and temporally localized atomic actions. Open-AoE further includes a data processing pipeline that transforms raw recordings into structured samples through temporal action segmentation, semantic annotation, hand reconstruction, and camera trajectory reconstruction. Meanwhile, we provide a separate downstream toolchain supports visualization, cross-embodiment retargeting, model-specific data conversion, and training recipes for VLA policies, WAMs, and World Models. By integrating scalable capture, structured processing, and downstream adaptation, Open-AoE reduces the barriers to both data contribution and reuse, providing practical open infrastructure for embodied model training, human-to-robot transfer, and world modeling.

cs.RO↗

arXiv2026

Radio and X-ray flux rebrightening six years after outburst in a partially-obscured extreme changing-look AGN

SDSS J1548+2208 is a unique partially-obscured nuclear transient that exhibits multiwavelength outbursts in mid-infrared, X-ray and radio. We present the results from multiwavelength photometric and spectroscopic follow-up observations with a time span of ~2500 days since its discovery. We find that the mid-infrared and X-ray emission (with a hard X-ray spectrum) are still in a high flux level relative to the pre-flare state, suggesting a sudden increased, and possibly long-sustained accreting activity from central black hole. This is supported by the slowly-evolving high-ionization coronal lines. The mid-infrared color turns blue slowly in the rising phase, which is distinct from stellar tidal disruption events (TDEs). All these properties point to the origin of outbursts from an extreme changing-look AGN and the scenario with a normal TDE seems disfavored. The radio spectral energy distribution (SED) in ~0.65-15 GHz is unusual, displaying a double-peak feature with distinct variability characteristics. In addition, we find evidence for the late-time radio rebrightening more than six years since the initial outburst, as well as a possibly new X-ray flare, though the significance for the latter is not high. The peculiar radio flux and SED evolution could be explained by a nascent outflow expanding into and shocking circumnuclear diffuse medium filled by denser clouds. In this case, SDSS J1548+2208 represents a rare changing-look AGN which can launch radio outflows. Continued multiwavelength observations are required to map the dust and gas distribution on pc-scales, providing new insights into the environmental properties that could regulate AGN changing-look phenomenon.

astro-ph.HE↗

arXiv2026

Generation and Characterization of Surface-Attached Ultrathin Liquid Sheets for Grazing-Incidence X-ray Scattering

Capturing the ultrafast structural dynamics that occur at the solid-liquid interface is key to understanding adsorption, desorption, diffusion, and aggregation processes in catalysis and interfacial chemical reactions. Hard-X-ray scattering in grazing-incidence geometry can, in principle, access interfacial structural changes with angstrom-scale structural sensitivity and ultrafast temporal resolution. However, the long optical paths of the optical pump and hard-X-ray pulses inside the liquid sample pose significant challenges to the temporal resolution, signal-to-noise ratio, and overall stability of such an experimental scheme. Here, we report a method for creating and characterizing ultrathin surface-attached free-flowing liquid sheets, whose submicrometer thickness enables ultrafast temporal resolution and reduces the bulk-liquid scattering contribution. The impinging-jet geometry produces stable micrometer-scale sheets whose morphology depends systematically on incidence angle, jet velocity, and capillary diameter. Gas-assisted shaping using a second capillary further narrows and thins the sheet, producing an extended ultrathin region and reducing the measured minimum thickness below 500~nm for acetonitrile. The resulting platform provides a reproducible, continuously flowing, surface-attached liquid geometry for grazing-incidence scattering experiments.

physics.chem-ph↗

arXiv2026

Modulation of anomalous Hall angle in a magnetic topological semimetal

The anomalous Hall angle ({\theta}A) is a measure of the efficiency of converting a longitudinal driving current to a transverse spin-polarized Hall current. For anomalous Hall sensing, a large anomalous Hall angle can improve the sensitivity of magnetic field detection. However, modulation of this angle is challenging and magnetic materials typically have low angles of 0.1 to 3{\deg}. Here, we report modulation of the anomalous Hall angle in the magnetic Weyl semimetal Co3Sn2S2. We propose that the angle parameter tan{\theta}A can be formulated as a function of the product of electrical resistivity and anomalous Hall conductivity. Our scheme was utilized to demonstrate the modulation of tan{\theta}A up to a magnitude of 0.46, corresponding to an angle of around 25{\deg}. Microfabricated anomalous Hall devices using Fe-doped Co3Sn2S2 single-crystalline nanoflakes exhibit a high Hall sensitivity of 7028 {\mu}{\Omega}ucm/T and a magnetic field detectability of 23.5 nT/Hz0.5 at 1 Hz.

cond-mat.mes-hall↗

arXiv2026

Modulation of the Nernst Thermoelectrics by Regulating the Anomalous Hall and Nernst Angles

The large anomalous Nernst effect in magnetic Weyl semimetals is one of the most intriguing transport phenomena, which draws significant attention for its potential applications in topological thermoelectrics. Despite frequent reports of substantial anomalous Nernst conductivity (ANC), methods to optimize Nernst thermoelectrics remain limited. Our research reveals that the magnitude of the ANC is directly related to the sum of the anomalous Nernst and Hall angles. While the sign of the anomalous Hall angle is relatively stable in a certain material, the sign of the anomalous Nernst angle can be intrinsically tuned. Therefore, the ANC can be effectively optimized by regulating these angles to work in concert. This finding is verified by experimental modulation from iron-doped magnetic topological material Co3Sn2S2. Additionally, we observed a robust TlnT scaling law of the ANC over the temperature range of 40 to 140 K in all studied samples, suggesting an intrinsic origin of the ANC. Considering the common opposite sign of the anomalous Nernst and Hall angles in many magnetic topological materials, our research offers an applicable scheme for optimizing the Nernst thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2026

TabClean: Reusable LLM-Synthesized Programs for Tabular Data Cleaning

Reliable analytics and machine-learning pipelines depend on clean tabular data, yet production tables often contain missing values, typographical errors, inconsistent formats, violated dependencies, unit mismatches, and ambiguous categorical values. Existing cleaning systems make different trade-offs. Constraint-based systems need experts to specify rules. Learning-based systems need labels or retraining. Recent LLM-based cleaners reduce setup effort, but many call an LLM on rows, cells, or repeated workflow steps, so their cost grows with table size and with every recurring batch. We present TabClean, a model-training-free system that compiles LLM reasoning into reusable guarded cleaning programs. Given a dirty table and a small annotated development set, TabClean profiles table evidence, diagnoses repair mechanisms, synthesizes executable Python transformations, validates candidates with cell-level feedback, and commits the best program for reuse on schema-compatible batches. The key abstraction is an evidence-backed guarded repair clause. A deterministic transformation may fire only when its dirty pattern, target-negative condition, evidence support, and scope constraints are satisfied. Across six benchmarks, TabClean achieves high precision, improves F1 over representative rule-based, learning-based, and LLM-based baselines on five datasets, and substantially reduces recurring runtime and API cost by replacing repeated LLM inference with deterministic program execution.

cs.DB↗

arXiv2026

Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents

Interactive text-to-SQL agents solve database tasks through multi-turn interactions involving schema exploration, query execution, feedback interpretation, and decision revision. Long-term memory helps agents reuse past experiences, but existing retrieval methods remain limited. Static methods rely on fixed similarity heuristics that do not optimize downstream utility, while dynamic methods often learn from sparse final outcomes and retrieve memories at a single decision horizon. This is insufficient when memory usefulness changes across interaction stages, since memories useful for initial planning may differ from those needed for local, state-conditioned execution. We propose MERIT, a dynamic multi-horizon memory retrieval framework. MERIT maintains episode-level memory for global strategic guidance and turn-level memory for local decision support. Both levels use learned retrieval policies optimized with reinforcement learning. To train turn-level retrieval despite limited intermediate supervision, MERIT uses a lightweight Process Reward Model to provide dense proxy rewards for local memory selection. Experiments on BIRD-Interact show that MERIT outperforms no-memory, static-retrieval, and dynamic-retrieval baselines in success rate while reducing average interaction turns. Transfer results on Spider2-Snow further show positive cross-benchmark transfer without benchmark-specific tuning. These results suggest that multi-horizon retrieval improves experience reuse in interactive text-to-SQL agents.

cs.CL↗

arXiv2026

An Obscured Tidal Disruption Event Uncovered by Its Mid- and Near-Infrared Dust Echo in a Star-Forming Galaxy

We present a comprehensive study of an infrared (IR) flare in the star-forming galaxy SDSS J010320.39+140152.5, which is selected from the sample of mid-IR (MIR) outbursts in nearby galaxies (MIRONG). Its MIR luminosity rose rapidly to a peak of $\sim5.4\times10^{43}$ \lum, maintained in the high state for about a year, and decreased continuously afterward. No optical variability was detected throughout the IR flare. Near-IR follow-up observations around the peak pinpointed the flare's location to spatially coincide with the galactic nucleus, with a $3\sigma$ upper limit of the offset of $\lesssim100$ pc. The IR spectral energy distribution (SED) of the flare is consistent with thermal emission of dust with temperatures of $\sim900$ K. Using a dust radiative transfer model, we inferred a peak UV luminosity of $\sim(4-10)\times10^{44}$ erg s$^{-1}$ and a total energy of $\sim(0.9-2)\times10^{52}$ ergs released. We ruled out the possibility of a supernova, and prefer that the IR flare originated from an obscured tidal disruption event (TDE) rather than a changing-look active galactic nucleus (AGN). This flare stands as one of the most compelling cases to date for the emerging class of dust-obscured TDEs in recent years. They are missed by optical surveys, partly accounting for the observed bias in TDE host galaxies, and represent a crucial, yet often overlooked, component for a complete understanding of the TDE population.

astro-ph.GA↗

arXiv2026

Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations

In many reasoning tasks, large language models (LLMs) rely on structured external knowledge, such as graphs and tables, which is typically linearized into sequential token representations. However, even when sufficient knowledge is available, LLMs can still produce hallucinated outputs, and the underlying mechanisms behind such failures remain poorly understood. We investigate these mechanisms and find that hallucinations arise from systematic internal dynamics rather than random noise. First, attention disproportionately concentrates toward shortcut-like structural cues rather than distributing across the full context. Second, feed-forward representations fail to ground the provided knowledge, causing the model to revert to parametric memory. Moreover, our results indicate that hallucination is consistently associated with failures in semantic grounding within feed-forward layers, while attention allocation exhibits greater task-dependent variability. Finally, we show that these mechanistic patterns generalize beyond single-hop graphs to multi-hop and tabular settings, enabling effective hallucination detection across structured knowledge formats.

cs.CL↗

arXiv2026

Proton-electron coupled catalyst for ionomer-free electrochemical energy conversion

Efficient electrochemical energy devices are vital to renewable energy technology, yet coordinating the effective flow of electrons, ions, and chemical species continues to be a major challenge. In conventional proton-exchange membrane fuel cell (PEMFC) catalyst layers, proton and electron transport are supplied separately through percolating carbon networks and ionomer binders, rendering the catalyst largely passive and imposing fundamental trade-offs between reactant accessibility, ionic conductivity, and catalyst activity. Here, we introduce a one-dimensional proton-electron coupled catalyst (PECC) design, a transport-integrated electrocatalyst architecture in which the catalyst itself simultaneously supplies electronic and protonic transport to catalyst active sites. Using this PECC, PEMFCs can have an ionomer-free cathode catalyst layer (CCL), resulting in a dramatic 95% reduction in non-Fickian oxygen transport and boosting power density by 34% and 85% compared to traditional CCLs, with cathode Pt loadings of approximately 0.090 mg/cm^2 and 0.037 mg/cm^2, respectively. Meanwhile, PECC retains 65% of its mass activity and exhibits 32% higher power density than its ionomer-based CCL counterpart after 30k accelerated stressed test. Similar mass transport improvements have been observed in the electrochemical hydrogen pump (EHP) using PECC in the catalyst layers. Molecular dynamics simulations show the PECC's proton conductivity is 249% higher than Nafion. This PECC catalyst structure addresses core transport problems in PEMFCs, leading to almost 20% improvement in fuel efficiency and opens up new possibilities for designing high-performance, cost-effective electrochemical devices.

cond-mat.mtrl-sci↗