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Yidi Wang

Publications and source records attributed to Yidi Wang.

At least 19 recordsLinked to original sources
arXiv2026

TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models

Ambiguity is a central feature of natural communication: a sentence, image, or sound may remain underspecified until evidence from the other channels is considered. Yet current multimodal benchmarks mostly test perception, recognition, or reasoning over already determinate inputs, leaving unclear whether omni-modal large language models can resolve ambiguity distributed across text, vision, and audio. We introduce TRIAD, a benchmark for omni-modal ambiguity resolution. Each item is a text--image--audio triplet paired with a question and a set of answer options, and is constructed so that the full triplet determines a unique answer while removing any one modality makes the set of consistent options non-unique. TRIAD contains $327$ hand-written queries in $86$ scene groups and covers $18$ ambiguity categories across the three modalities. Evaluating $14$ omni-modal systems reveals that strong item-level performance does not translate into robust scene-level disambiguation: the best model still trails humans by over $20$ points in group accuracy. Removing answer options widens the gap further, driving model group accuracy to the single digits while humans remain robust. Leave-one-out and transcript/description counterfactuals further show that current systems often underuse audio cues when text and image are present. TRIAD reframes omni-modal evaluation around disambiguation rather than recognition, and provides a taxonomy, benchmark, and diagnostic protocol for measuring this capability.

cs.CL↗
arXiv2026

Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning

Omni-modal large language models are increasingly evaluated on clean text--vision--audio inputs, where every channel is present, synchronized, and readily interpretable. Such scores are often taken as evidence of robust cross-modal fusion, but clean evaluation cannot tell whether success depends on stable cross-modal structure or on cues sufficient only in intact inputs. To address this gap, we define a modality fault line: a boundary at which model behavior becomes unstable when a modality remains present and human-interpretable, but its internal evidence structure is perturbed. We introduce SCEval (Structure-Corruption Evaluation) a diagnostic evaluation protocol that keeps the question, answer space, and modality channels fixed while applying controlled structural corruptions to text, vision, and audio individually and jointly. Built from $273$ human-verified tri-modal examples from Social-IQ, OmniBench, and VALOR, SCEval evaluates $15$ proprietary and open-source omni-modal systems. The results show that structural corruption lowers clean accuracy, text--vision damage forms the most stable shared fault line, and multi-modal degradation is non-additive rather than a simple function of the number of corrupted modalities. Clean omni-modal accuracy therefore does not establish that a model will remain reliable when cross-modal evidence becomes structurally unreliable.

cs.CL↗
arXiv2026

Shaping the Wind: Nested Potentials for Kinematically Admissible Urban Wind Prediction

Predicting transient urban winds is fundamental to understanding urban microclimates and designing climate-resilient cities. Building-resolving large-eddy simulation produces detailed incompressible urban wind fields at substantial computational cost for each layout. Neural surrogates offer a faster alternative by learning to predict the evolution of velocity fields. However, minimizing velocity prediction error does not guarantee local mass conservation and wall impermeability, which together define kinematic admissibility. This limitation stems from an unconstrained output representation: geometry conditioning guides predictions but does not restrict them to admissible velocity fields. Correcting boundary violations in these outputs changes the flux balance in adjacent fluid cells and may consequently compromise local mass conservation. To address the challenge, we propose Sculpt, a nested potential framework that builds the coupled, geometry-dependent constraints directly into its parameterization. This nested parameterization generates divergence-free velocity updates through the discrete curl of a volume vector potential on the native three-dimensional staggered grid. A shared scalar potential constrains the vector potential's boundary values so that the same operator also enforces impermeability, without a per-step pressure projection. Because backpropagation through this curl attenuates large-scale gradient signals, we parameterize the volume potential at multiple resolutions to better capture large-scale flow structures. We introduce UrbanWindFlow, an LES dataset spanning urban morphologies and inflow conditions, to evaluate accuracy and kinematic admissibility together.

cs.LG↗
arXiv2026

Relative Transitions, Not Absolute Destinations: A Transfer-and-Ground Framework for Target-Trajectory-Free Human Mobility Generation

Individual mobility trajectories support urban analysis and location-based services, yet most trajectory generators require observations from their deployment city. This assumption excludes precisely the cities where trajectories are unavailable even though points of interest (POIs) and their attributes can be obtained from public maps. We study target-trajectory-free generation: learning from POIs and trajectories in source cities while utilizing only POI coordinates and categories in a target city, with no target trajectory or trajectory-derived statistic available for training, model selection, or generation. Existing trajectory generators typically predict absolute destinations, entangling reusable movement behavior with city-specific POI identities and spatial layouts. Our core insight is to replace this city-bound output with context-conditioned relative transitions. We propose Nomad, a transfer-and-ground framework that separates learning how people move from determining where those movements are realized. Specifically, a history-conditioned flow-matching model learns from source trajectories a transition prior over semantic displacement between POI contexts, geographic displacement, and elapsed time; at inference, a behavior graph and an exploration--return walk ground sampled transitions onto the target POI map. This factorization enables a direct test of representation level transferability without assuming invariance of the full mobility distribution. Extensive experiments across ten cities and 14 transfers show that Nomad outperforms adaptation baselines in trajectory fidelity and downstream utility, lowering the average error over the best baseline of each metric by about 15% in distributional fidelity and about 3% in downstream utility.

cs.LG↗
arXiv2026

Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Management

As optical networks continue to expand in scale, complexity, and service diversity, the implementation of automation has become essential for ensuring agility, efficiency, and reliability in lifecycle management (LCM) of optical networks. Large language model (LLM) Agent, distinguished by its progressively sophisticated capabilities in logical reasoning, adaptive decision-making, complex problem solving, and multi-task orchestration, presents great opportunities to advance network automation beyond traditional AI techniques. Nevertheless, the application of LLM Agent in optical networks remains in its early exploratory stage, challenged by the lack of multi-task coordination, high computational demands, data dependence, and reliability concerns. In this paper, we envision a conceptual roadmap toward Agentic Optical Networks (AONs) by integrating LLM Agents throughout the LCM with high-level autonomy. First, we trace the evolution from manual operations to AI-empowered frameworks and distill key technologies in Agent, providing actionable insights into leveraging its strengths for addressing practical network automation challenges. A core contribution of this paper is the proposal of a hierarchical multi-Agent framework, which is specifically developed to manage every phase in LCM of AONs, including planning, deployment, operation, maintenance, upgrade, and decommission, thereby enabling more cohesive and comprehensive automation throughout the entire lifecycle. In addition, future directions and underlying challenges are also discussed at the intersection of LLM and optical networks. By aligning the LLM Agent with the specialized requirements of AONs, this work aims to explore the potential for the evolution of optical networks moving from task-level semi-automatic execution toward lifecycle-level full autonomy.

cs.NI↗
arXiv2026

Staged emergence of anomalous Hall transport in a correlated uranium Weyl semimetal

Understanding how electronic correlations reshape topological states remains a central challenge in quantum materials. Here we investigate the uranium ferromagnet UPS using magnetotransport, angle-resolved photoemission spectroscopy, thermodynamic measurements, and first principles calculations. Resonant photoemission reveals narrow U-$5f$ spectral weight at the Fermi level coexisting with broad incoherent states, consistent with the itinerant/localized duality characteristic of uranium $5f$ electrons. The anomalous Hall conductivity reaches approximately $4.5\times10^{2}\,Ω^{-1}\mathrm{cm}^{-1}$, yet does not simply follow the ordered magnetic moment. Temperature dependent photoemission reveals relatively little change across $T_C=118$~K, followed by a pronounced redistribution of low-energy $5f$ spectral weight below approximately 90~K. First-principles calculations identify a symmetry protected Weyl crossing with pronounced Berry curvature and yield an intrinsic anomalous Hall conductivity of approximately $9.6\times10^{2}\,Ω^{-1}\mathrm{cm}^{-1}$. These results demonstrate that magnetic order, correlated electronic reconstruction, and anomalous Hall transport develop over distinct but overlapping temperature ranges, revealing how strong correlations reshape topological transport in a uranium Weyl semimetal.

cond-mat.str-el↗
arXiv2026

Iwasawa theory of (directed) Cayley graphs

In this article, we prove the Defect Conjecture for Cayley graphs of abelian groups, dihedral groups, and groups of the form $\mathbb{Z}/p\mathbb{Z} \rtimes \mathbb{Z}/(p-1)\mathbb{Z}$ where $p\geq 3$ is a prime. We also compute the Iwasawa invariants of the Bowen--Franks groups associated with these graphs in several cases.

math.NT↗
arXiv2026

R2S-Eval: Robot Evaluation with Real-to-Sim Calibration via Vision-Language Models

Evaluating robot manipulation policies is becoming increasingly important as generalist models, particularly vision-language-action (VLA) models, are deployed on physical robots. However, conventional real-world evaluation remains labor-intensive, unstable, and insufficiently informative. It requires repeated hardware trials, manual scene resets, and continuous operator monitoring, may produce different policy rankings across repeated evaluations, and primarily relies on success-rate metrics that provide limited information about execution quality. In contrast, humans assess robot performance by observing and comparing complete behaviors rather than relying solely on binary success outcomes. To this end, we propose R2S-Eval, an evaluation pipeline that combines real-to-sim calibration with vision-language model (VLM) preference evaluation. The real-to-sim component efficiently generates rollout videos in a simulator calibrated to the real-world evaluation setting, thereby reducing the need for repeated hardware trials. The VLM evaluator assesses the execution quality of rollout videos and produces pairwise preferences, which are subsequently aggregated into policy rankings. We further introduce a protocol to assess whether the proposed evaluation pipeline yields validated policy conclusions while mitigating the key challenges of conventional real-world evaluation. Experiments in both simulation and real-world settings demonstrate that R2S-Eval produces reliable and stable policy conclusions, achieves agreement with human preferences, substantially reduces repeated hardware-operation effort, and reveals behavior-quality differences that are not captured by binary success labels. In general, R2S-Eval advances robot evaluation from manual success counting toward automated, statistically stable, and quality-aware evaluation of robot behavior. Project page: https://r2s-eval.github.io.

cs.RO↗
arXiv2026

Physics-Informed CNN-LSTM for Street-Scale Urban Flood Prediction: Reconciling Aggregate Accuracy and Street-Level Plausibility

Deep learning surrogate models trained with mean-squared-error loss produce statistically accurate but physically unconstrained flood predictions: water may flow uphill, appear spontaneously, or smooth over street-level corridors. We develop a physics-informed training framework for CNN-LSTM models that predict urban flood depths at 15 min intervals over a 128x128 spatial grid. Three differentiable penalty terms are embedded into the loss: (i) a gravity loss penalizing depth increases against the water-surface-elevation gradient, (ii) a continuity loss enforcing local mass conservation with rainfall-adaptive thresholds, and (iii) a topography-aware false-alarm penalty modulated by the topographic wetness index (TWI). We evaluate on the Norfolk, Virginia flood dataset spanning two storm events (August 2017 and September 2022, 300 samples), with all variants trained on identical splits and robustness assessed over repeated random splits and leave-one-storm-out tests. A road-proximal evaluation restricted to a TWI-derived street mask quantifies street-level skill. The physics-constrained model achieves near-zero gravity violations (order 1e-6) and the highest street-channel recall (0.77 +/- 0.09 vs 0.44 +/- 0.10 for the unconstrained baseline), the capability most relevant to traffic routing, and its advantage more than doubles on a held-out storm; a uniform false-alarm variant attains 16% lower mean absolute error but suppresses street recall to 0.25. The TWI-modulated penalty reconciles this trade-off: it improves on the uniform variant on every metric, recovering 60% higher street recall at the lowest MAE among constrained variants and the best street-level F1. These results expose a fundamental tension between aggregate pixel-level error and application-specific physical plausibility, and show that terrain-aware loss modulation offers a principled resolution.

cs.LG↗
arXiv2026

PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation

Modern app store recommender systems struggle with multiple-category apps, as traditional taxonomies fail to capture overlapping semantics, leading to suboptimal personalization. We propose PCR-CA (Parallel Codebook Representations with Contrastive Alignment), an end-to-end framework for improved CTR prediction. PCR-CA first extracts compact multimodal embeddings from app text, then introduces a Parallel Codebook VQ-AE module that learns discrete semantic representations across multiple codebooks in parallel -- unlike hierarchical residual quantization (RQ-VAE). This design enables independent encoding of diverse aspects (e.g., gameplay, art style), better modeling multiple-category semantics. To bridge semantic and collaborative signals, we employ a contrastive alignment loss at both the user and item levels, enhancing representation learning for long-tail items. Additionally, a dual-attention fusion mechanism combines ID-based and semantic features to capture user interests, especially for long-tail apps. Experiments on a large-scale dataset show PCR-CA achieves a +0.76% AUC improvement over strong baselines, with +2.15% AUC gains for long-tail apps. Online A/B testing further validates our approach, showing a +10.52% lift in CTR and a +16.30% improvement in CVR, demonstrating PCR-CA's effectiveness in real-world deployment. The new framework has now been fully deployed on the Microsoft Store.

cs.IR↗
arXiv2026

Anticipation-VLA: Solving Long-Horizon Embodied Tasks via Anticipation-based Subgoal Generation

Vision-Language-Action (VLA) models have emerged as a powerful paradigm for embodied intelligence, enabling robots to perform tasks based on natural language instructions and current visual input. However, existing VLA models struggle with long-horizon tasks due to compounding errors. Prior methods decompose tasks into subtasks of fixed granularity, which cannot adapt to the varying complexity of execution states, limiting their robustness in long-horizon tasks. To overcome this, we introduce Anticipation Model, which adaptively and recursively generates future subgoals. This model continuously adapts as the task unfolds, adjusting future subgoals in response to evolving dynamics, facilitating more reliable planning paths. Building on this concept, we propose Anticipation-VLA, a hierarchical VLA model that leverages the anticipation model to generate actionable subgoals that guide VLA policy execution. We implement Anticipation-VLA with finetuning a Unified Multimodal Model (UMM) for high-level subgoal generation and a goal-conditioned VLA policy for low-level action execution. Experiments in both simulated and real-world robotic tasks demonstrate the effectiveness of Anticipation-VLA, highlighting the importance of adaptive and recursive subgoal generation for robust policy execution.

cs.RO↗
arXiv2026

The étale Brauer-Manin obstruction for classifying stacks

We study the strong approximation for classifying stacks $BG$, where $G$ is a linear algebraic group over a number field $k$. More specifically, we prove that the étale Brauer-Manin obstruction is the only obstruction to strong approximation for $BG$. To prove the result, we formulate the theory of torsors and Galois twists for algebraic stacks.

math.NT↗
arXiv2026

Out-of-Distribution Graph Models Merging

This paper studies a novel problem of out-of-distribution graph models merging, which aims to construct a generalized model from multiple graph models pre-trained on different domains with distribution discrepancy. This problem is challenging because of the difficulty in learning domain-invariant knowledge implicitly in model parameters and consolidating expertise from potentially heterogeneous GNN backbones. In this work, we propose a graph generation strategy that instantiates the mixture distribution of multiple domains. Then, we merge and fine-tune the pre-trained graph models via a MoE module and a masking mechanism for generalized adaptation. Our framework is architecture-agnostic and can operate without any source/target domain data. Both theoretical analysis and experimental results demonstrate the effectiveness of our approach in addressing the model generalization problem.

cs.LG↗
arXiv2026

Nanoscale electronic variations in altermagnetic $α$-MnTe

Altermagnets exhibit spin-split electronic bands like ferromagnets, yet they are magnetically compensated like collinear antiferromagnets. Altermagnets thus combine the benefits of ferromagnets and antiferromagnets, opening routes to tailored materials and applications. However, reported bulk signatures such as the anomalous Hall response in candidate altermagnets have been inconsistent across samples, suggesting that inhomogeneity may affect their functionality in electronic devices. Here, we use low-temperature scanning tunneling microscopy and spectroscopy to map the local electronic landscape of $α$-MnTe on atomically flat cleaved single crystals. We resolve two distinct electronic regions. In Region A, the chemical potential lies near the valence-band edge and varies by $\sim$100 meV on the nanometer length scale. In Region B, the chemical potential lies near the middle of a wider band gap. We further identify an incommensurate charge modulation with periodicity (2.5$\pm$0.1)$a$, observed exclusively in Region A. Our work establishes that $α$-MnTe can exhibit significant electronic non-uniformity, suggesting that nanoscale characterization is essential for its reliable use in electronic applications.

cond-mat.str-el↗
arXiv2026

Probing the Penetration Depth of Topological Surface States by Magnetic Impurity Scattering in V-doped Sb$_2$Te$_3$

Topological insulators host Dirac surface states (SS) protected by time-reversal symmetry. Inter-surface hybridization can gap the SS and give rise to the quantum spin Hall effect in films that are sufficiently thin compared to the SS penetration depth. However, quantifying the SS penetration depth typically requires painstaking synthesis of multiple films with varying thickness. Here we introduce a direct method to probe the SS penetration depth in bulk crystals, by studying the interplay between SS and magnetic impurities in \SVT. Using scanning tunneling microscopy and spectroscopy, we find that even sparse magnetic impurities ($\lesssim0.25\%$ vanadium) can gap the Dirac SS. However, a single V impurity induces only localized states, and does not form an impurity band, so the gapped Dirac dispersion is preserved away from the impurity. In high magnetic fields, we observe an energy shift of the $0^\text{th}$ Landau level and a suppression of quasiparticle lifetime at the Dirac point, indicating \newtext{magnetic} scattering of the SS. Crucially, by employing V impurities at different depths as precise scattering probes, we reveal the SS penetration depth on the sub-nanometer scale in a bulk crystal.

cond-mat.mes-hall↗
arXiv2026

Large language models for optical network O&M: Agent-embedded workflow for automation

With the continuous expansion of optical networks and the increasing diversity of services, existing operation and maintenance (O&M) approaches are increasingly challenged to meet the rising demands for intelligence and efficiency. Large language models (LLMs), endowed with advanced semantic understanding and contextual analysis capabilities, are emerging as a promising enabler for intelligent optical network O&M. Recent studies have demonstrated the feasibility of applying LLMs to optical network management, marking an important step toward intelligent automation. However, systematic investigations into how LLMs can be effectively integrated into existing O&M workflows remain limited. This paper addresses this gap by drawing inspiration from best practices in real-world O&M workflows and systematically identifying scenarios that are well suited for LLM integration. We highlight that agent-based design is key to improving the executability of tasks, and we propose a multi-Agent collaborative O&M architecture that integrates LLM capabilities with existing O&M tools. The proposed architecture leverages core LLM-related technologies including prompt engineering and tool invocation, to build Agent solutions targeting key tasks such as optical channel management, performance optimization, and fault management. This work presents a conceptual framework for embedding LLM-based Agents into optical network O&M workflows, forming agentized processes that demonstrate the feasibility of LLM-assisted task execution and lay the groundwork for future autonomous O&M systems featuring closed-loop perception, decision-making, and action.

physics.optics↗
arXiv2026

Kondo Reshapes Multiple Orders in a $5f$ van der Waals Material

Electron interactions can drive magnetism, superconductivity, and topology. However, the realization of these phases remains limited in van der Waals materials, and the full landscape of strong correlations remains uncharted in any context. While interactions between conduction electrons and localized spins yield a well-known competition between heavy fermions (Kondo hybridization) and magnetic order (RKKY exchange), such spin-driven competition represents only part of the correlated electron phase diagram. Here we demonstrate that a heavy-fermion state can also compete with charge order, such as the charge density wave (CDW) state typical in the van der Waals $4f$ rare-earth tritellurides (RTe$_3$). We exploit the spatially-extended $5f$ orbitals of $β$-UTe$_3$ to enhance Kondo hybridization compared to its isostructural RTe$_3$ cousins. Our scanning tunneling spectroscopy on $β$-UTe$_3$ shows Fano resonances characteristic of the heavy fermion state, while our quasiparticle interference imaging reveals the disappearance of Fermi-level nesting and the appearance of flat bands. We extend the tritelluride tight-binding model to include Kondo coupling and quantify the Fermi surface reconstruction. Consistent with the destruction of nesting, we observe no CDW in $β$-UTe$_3$. Our expansion of the Kondo phase diagram beyond spin-mediated competition opens new possibilities for proximity-induced phase engineering in correlated van der Waals heterostructures.

cond-mat.str-el↗
arXiv2025

CARTOS: A Charging-Aware Real-Time Operating System for Intermittent Batteryless Devices

This paper presents CARTOS, a charging-aware real-time operating system designed to enhance the functionality of intermittently-powered batteryless devices (IPDs) for various Internet of Things (IoT) applications. While IPDs offer significant advantages such as extended lifespan and operability in extreme environments, they pose unique challenges, including the need to ensure forward progress of program execution amidst variable energy availability and maintaining reliable real-time time behavior during power disruptions. To address these challenges, CARTOS introduces a mixed-preemption scheduling model that classifies tasks into computational and peripheral tasks, and ensures their efficient and timely execution by adopting just-in-time checkpointing for divisible computation tasks and uninterrupted execution for indivisible peripheral tasks. CARTOS also supports processing chains of tasks with precedence constraints and adapts its scheduling in response to environmental changes to offer continuous execution under diverse conditions. CARTOS is implemented with new APIs and components added to FreeRTOS but is designed for portability to other embedded RTOSs. Through real hardware experiments and simulations, CARTOS exhibits superior performance over state-of-the-art methods, demonstrating that it can serve as a practical platform for developing resilient, real-time sensing applications on IPDs.

cs.OS↗