arXiv subjects

Yi Liang

Publications and source records attributed to Yi Liang.

At least 19 recordsLinked to original sources

arXiv2026

UG-UMRE: Uncertainty-Guided Modality Augmentation and Distributional Calibration for Unified Multimodal Relation Extraction

Unified Multimodal Relation Extraction (UMRE) aims to identify intra-modal and cross-modal relations between textual entities and visual objects. However, existing UMRE studies still encounter two critical issues: ignoring inherent aleatoric uncertainty causes noise propagation, and deep-seated heterogeneity between distinct modal distributions hinders alignment. To address these issues, we propose the Uncertainty-Guided UMRE Network (UG-UMRE). Specifically, we design an Uncertainty-Driven Unimodal Augmentation (UDUA) module, which models features as Gaussian distributions based on the Variational Information Bottleneck. By incorporating an uncertainty-aware self-supervised contrastive learning mechanism, UDUA effectively filters out noise while maintaining semantic consistency. Furthermore, we introduce the Joint Aleatoric Uncertainty Alignment (JAUA) module as a global semantic pre-calibration mechanism. JAUA leverages probabilistic distribution consistency to construct a shared latent space, eliminating the distributional gap by synchronizing cross-modal statistical properties, thereby laying a robust foundation for fine-grained interaction. Experiments on three benchmark datasets (UMRE, MORE, and MNRE) demonstrate that UG-UMRE achieves state-of-the-art performance. Further analysis validates the pluggable and effective performance of the proposed UDUA and JAUA modules.

cs.CV↗

arXiv2026

Physical reduced states and continuum characters of the lattice Kramers-Wannier defect

A non-invertible topological line fixes global defect data but does not select a reduced density matrix in a spatially twisted sector. For the Kramers-Wannier defect of the critical Ising chain, we choose the equal-weight incoherent mixture of the two charge-sector ground states and reduce it to the ordinary full spin algebra of a complete non-wrapping prefix anchored at the defect endpoint. The finite-size RDM is the convex average of two charge-sector RDMs rather than a sign-zero Gaussian proxy, and an exact descended antiunitary enforces many-body Kramers pairing without determining the entropy. In the strict nested limit, with the circumference taken to infinity at fixed prefix and the prefix enlarged only afterward, full-RDM convergence together with the odd-even Toeplitz/Fisher-Hartwig endpoint yields an excess entropy $\tfrac12\log2$ over the homogeneous chain, whereas the matched invertible $\eta$ defect yields zero. The exact character of the same twisted Hamiltonian, jointly resolved by energy and modified translation, records the finite-size roots and their multiplicities, retains chirality in the marked scaling limit, and, using the standard Ising character identities, resolves the four Virasoro towers of the Ising duality-twisted sector.

quant-ph↗

arXiv2026

One cut, two parities: exact Fibonacci charge responses in the golden chain

We determine the exact cut-charge Born law in the ground state of every periodic antiferromagnetic golden chain with at least three sites. Its two parity branches retain different information. At even length, the ratio of nontrivial to vacuum probabilities equals the squared Fibonacci quantum dimension. The odd vacuum probability equals the even-branch nontrivial probability times one ground-state Born coordinate, while normalization fixes the complement. Parity selects the positive hoop sector at even length and the complementary sector at odd length. At each length, the corresponding cut projector compresses to a scalar in the even sector or to a non-scalar element of a two-character algebra in the odd sector. The unchanged measurement rule yields categorical rigidity at even length and one-coordinate state sensitivity at odd length.

quant-ph↗

arXiv2026

Interplay of Flat-band and Anderson localizations in disordered moire superlattices

Disorder in moire superlattices simultaneously degrades flat-band localization and induces Anderson localization, yet how these two regimes interact has remained unclear. Here, we introduce a combined framework linking localization-length scaling with differential probability density analysis to map localization transitions in partially disordered one-dimensional silicon moire lattices. It is found that flat bands confined within the interband gap keep their strong localization even as disorder grows. In contrast, flat bands intersecting dispersive bands exhibit rich behaviors: the low-frequency branch undergoes an inverse Anderson transition, while the high-frequency branch supports coexisting flat-band and Anderson localization at strong disorder. Our results deliver the direct evidence of competing localization mechanisms in disordered moire systems and offer guiding principles for engineering robust, nonideal moire photonic devices.

cond-mat.dis-nn↗

arXiv2026

Supercell-size scaling of moir\'e band flatness

In moir\'e superlattices, the band flatness governs the degree of wave localization, which is central to harnessing emergent phenomena and designing functional meta-devices. While research has focused on the magic conditions such as magic angle and magic distance for optimal flatness, a fundamental understanding of how flatness changes with the supercell size has remained elusive. Here, we establish a universal scaling between band flatness and supercell size. Theoretically, by recognizing the statistical equivalence between structural perturbations in moir\'e superlattices and disordered systems, we introduce the Thouless number to evaluate the strength of moir\'e localization. This approach allows us to establish a scaling theory for the evolution of band flatness with the supercell size, from which an analytical expression is derived. Our full-wave simulations with one-dimensional and two-dimensional moir\'e superlattices show excellent agreement with the theoretical prediction. Our work reveals a general scaling law for moir\'e band flatness, offering a new perspective for understanding and designing moir\'e-based resonant systems.

physics.optics↗

arXiv2026

Historical Foundation and Practical Guideline for Ferroelectric Switching Kinetic Studies

Electrical measurements of ferroelectric switching kinetics are widely used to probe the dynamics of polarization reversal, yet the influence of the measurement circuit is often underappreciated. In this paper, we show that the interplay between ferroelectric capacitors and circuit elements produces distorted, time-dependent voltage waveforms across the device, particularly in the sub-ns regime. We examine how these circuit contributions affect polarization transients extracted from PUND measurements. The resulting distortions scale with supply voltage, capacitor dimensions, and lumped circuit elements, but are not accounted for in conventional experimental analyses or analytical model fitting. We then critically assess existing nucleation and growth models and show that neglecting the time-varying voltage profile can lead to unphysical interpretations of switching kinetics, most notably in the extracted growth dimensionality represented by the Avrami exponent. Finally, we outline guidelines for future studies, emphasizing the need for direct voltage monitoring and circuit-aware de-embedding, as well as modeling frameworks that incorporate voltage-dependent nucleation and growth rates based on intrinsic material parameters.

cond-mat.mtrl-sci↗

arXiv2026

R3DP: Real-Time 3D-Aware Policy for Embodied Manipulation

Embodied manipulation requires accurate 3D understanding of objects and their spatial relations to plan and execute contact-rich actions. While large-scale 3D vision models provide strong priors, their computational cost incurs prohibitive latency for real-time control. We propose Real-time 3D-aware Policy (R3DP), which integrates powerful 3D priors into manipulation policies without sacrificing real-time performance. A core innovation of R3DP is the asynchronous fast-slow collaboration module, which seamlessly integrates large-scale 3D priors into the policy without compromising real-time performance. The system maintains real-time efficiency by querying the pre-trained slow system (VGGT) only on sparse key frames, while simultaneously employing a lightweight Temporal Feature Prediction Network (TFPNet) to predict features for all intermediate frames. By leveraging historical data to exploit temporal correlations, TFPNet explicitly improves task success rates through consistent feature estimation. Additionally, to enable more effective multi-view fusion, we introduce a Multi-View Feature Fuser (MVFF) that aggregates features across views by explicitly incorporating camera intrinsics and extrinsics. R3DP offers a plug-and-play solution for integrating large models into real-time inference systems. We evaluate R3DP against multiple baselines across different visual configurations. R3DP effectively harnesses large-scale 3D priors to achieve superior results, outperforming single-view and multi-view DP by 32.9% and 51.4% in average success rate, respectively. Furthermore, by decoupling heavy 3D reasoning from policy execution, R3DP achieves a 44.8% reduction in inference time compared to a naive DP+VGGT integration.

cs.RO↗

arXiv2026

LTRAS: A Linkable Threshold Ring Adaptor Signature Scheme for Efficient and Private Cross-Chain Transactions

Despite the advantages of decentralization and immutability, blockchain technology faces significant scalability and throughput limitations, which has prompted the exploration of off-chain solutions like payment channels. Adaptor signatures have been considered a promising primitive for constructing such channels due to their support for atomicity, offering an alternative to traditional hash-timelock contracts. However, standard adaptor signatures may reveal signer identity, raising potential privacy concerns. While ring signatures can mitigate this issue by providing anonymity, they often introduce high communication overhead, particularly in multi-account payment settings commonly used in UTXO-based blockchains like Monero. To address these limitations, we propose a Linkable Threshold Ring Adaptor Signature (LTRAS) scheme, which integrates the conditional binding of adaptor signatures, the multi-account payment of threshold ring signatures, and the linkability for preventing double-spending. The formal definition, security model and concrete construction of LTRAS are provided. We also analyze its security and evaluate its performance through theoretical analysis and experimental implementation. Experimental results demonstrate that our scheme achieve significantly lower computation and communication overhead compared to existing schemes in large ring sizes and multi-account payment scenarios. Finally, we discuss its application in cross-chain atomic swaps, demonstrating its potential for enhancing privacy and efficiency in blockchain transactions.

cs.CR↗

arXiv2026

Ferroelectric dynamic-field-driven nucleation and growth model for predictive materials-to-circuit co-design

Real ferroelectric devices operate under mixed and distorted time-varying voltages, yet the standard nucleation-growth frameworks used to interpret ferroelectric switching - most notably the Kolmogorov-Avrami-Ishibashi (KAI) and nucleation-limited switching models (NLS) - are derived under the critically limiting assumption of a constant electric field. Thus, the prevailing interpretation of ferroelectric switching dynamics fails under real operating conditions. Here we introduce a compact dynamic-field-driven nucleation and growth (DFNG) model that enables quantitative fits to switching transients across multiple ferroelectric materials to extract time-varying domain wall velocity and growth dimensionality, even under arbitrary voltage waveform. This capability then motivates its use in device modeling under complex signals spanning disparate time and frequency scales. Coupling the compact model to application-related waveforms and circuit-level simulation platform facilitates a predictive materials-circuit co-design framework by linking nucleation and growth parameters to memory window, disturb error, speed, and energy dissipation for next-generation ferroelectric technologies.

cond-mat.mtrl-sci↗

arXiv2026

Optical Self-Trapping and Nonlinear Light-Matter Interactions in Biological Soft Matter

Low-scattering, deep-penetration light transport in biological media remains a pivotal challenge for biophotonic technologies, including biomedical imaging, optical diagnostics, and photodynamic therapy. This review builds upon and extends our earlier studies of nonlinear optical self-trapping and optically induced waveguiding in biological suspensions, such as human erythrocytes and cyanobacteria, where light-matter coupling is governed by optical-force-mediated particle redistribution. Recent progress has revealed increasingly rich and complex regimes, including the propagation and nonlinear self-action of structured (vortex) beams in biological environments, as well as nonlinear responses dominated by thermally driven mechanisms in absorptive biomolecular solutions (e.g., heme and chlorophyll). We place particular emphasis on distinctive nonlinear phenomena observed in these systems, including spatial self-phase modulation, optical-force-induced sculpturing of effective energy landscapes, and quasi-waveguide formation in soft, heterogeneous biological media. We conclude by highlighting emerging opportunities to harness these nonlinear behaviors for deep-tissue imaging, label-free biosensing, and the realization of biocompatible photonic structures and devices assembled directly from living or hybrid biological matter.

physics.optics↗

arXiv2025

Budget-Aware Tool Use Enables Effective Agent Scaling

Scaling test-time computation has been extended from language model reasoning to tool-augmented agents, where scaling involves not only thinking in tokens but also acting via tool calls that directly constrain environmental interaction. However, we found that simply increasing the tool-call budget fails to improve performance, as agents lack "budget awareness" and quickly hit a performance ceiling. We study how to scale such agents effectively under explicit tool-call budgets, focusing on web search agents. We first introduce the Budget Tracker, a lightweight plug-in that provides the agent with continuous budget awareness, enabling simple yet effective scaling. We further develop BATS (Budget-Aware Test-time Scaling), an advanced framework that leverages this awareness to dynamically adapt its planning and verification strategy. To analyze cost-performance scaling in a controlled manner, we formalize a unified cost metric that jointly accounts for token and tool consumption. We provide the first systematic study on budget-constrained agents, showing that budget-aware methods produce more favorable scaling curves and push the cost-performance Pareto frontier. Our work offers empirical insights toward a more transparent and principled understanding of scaling in tool-augmented agents. Our code is available at https://github.com/google-research/budget-aware-agent.

cs.AI↗

arXiv2024

Integrating Planning into Single-Turn Long-Form Text Generation

Generating high-quality, in-depth textual documents, such as academic papers, news articles, Wikipedia entries, and books, remains a significant challenge for Large Language Models (LLMs). In this paper, we propose to use planning to generate long form content. To achieve our goal, we generate intermediate steps via an auxiliary task that teaches the LLM to plan, reason and structure before generating the final text. Our main novelty lies in a single auxiliary task that does not require multiple rounds of prompting or planning. To overcome the scarcity of training data for these intermediate steps, we leverage LLMs to generate synthetic intermediate writing data such as outlines, key information and summaries from existing full articles. Our experiments demonstrate on two datasets from different domains, namely the scientific news dataset SciNews and Wikipedia datasets in KILT-Wiki and FreshWiki, that LLMs fine-tuned with the auxiliary task generate higher quality documents. We observed +2.5% improvement in ROUGE-Lsum, and a strong 3.60 overall win/loss ratio via human SxS evaluation, with clear wins in organization, relevance, and verifiability.

cs.CL↗

arXiv2024

$M^3EL$: A Multi-task Multi-topic Dataset for Multi-modal Entity Linking

Multi-modal Entity Linking (MEL) is a fundamental component for various downstream tasks. However, existing MEL datasets suffer from small scale, scarcity of topic types and limited coverage of tasks, making them incapable of effectively enhancing the entity linking capabilities of multi-modal models. To address these obstacles, we propose a dataset construction pipeline and publish $M^3EL$, a large-scale dataset for MEL. $M^3EL$ includes 79,625 instances, covering 9 diverse multi-modal tasks, and 5 different topics. In addition, to further improve the model's adaptability to multi-modal tasks, We propose a modality-augmented training strategy. Utilizing $M^3EL$ as a corpus, train the $\textit{CLIP}_{\textit{ND}}$ model based on $\textit{CLIP} (\textit{ViT}-\textit{B}-\textit{32})$, and conduct a comparative analysis with an existing multi-modal baselines. Experimental results show that the existing models perform far below expectations (ACC of 49.4%-75.8%), After analysis, it was obtained that small dataset sizes, insufficient modality task coverage, and limited topic diversity resulted in poor generalisation of multi-modal models. Our dataset effectively addresses these issues, and the $\textit{CLIP}_{\textit{ND}}$ model fine-tuned with $M^3EL$ shows a significant improvement in accuracy, with an average improvement of 9.3% to 25% across various tasks. Our dataset is available at https://anonymous.4open.science/r/M3EL.

cs.IR↗

arXiv2024

Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild

As Large Language Models (LLMs) excel across tasks and specialized domains, scaling LLMs based on existing models has garnered significant attention, which faces the challenge of decreasing performance when combining disparate models. Various techniques have been proposed for the aggregation of pre-trained LLMs, including model merging, Mixture-of-Experts, and stacking. Despite their merits, a comprehensive comparison and synergistic application of them to a diverse model zoo is yet to be adequately addressed. In light of this research gap, this paper introduces Model-GLUE, a holistic LLM scaling guideline. First, our work starts with a benchmarking of existing LLM scaling techniques, especially selective merging, and variants of mixture. Utilizing the insights from the benchmark results, we formulate an optimal strategy for the selection and aggregation of a heterogeneous model zoo characterizing different architectures and initialization.Our methodology involves the clustering of mergeable models and optimal merging strategy selection, and the integration of clusters through a model mixture. Finally, evidenced by our experiments on a diverse Llama-2-based model zoo, Model-GLUE shows an average performance enhancement of 5.61%, achieved without additional training. Codes are available at: https://github.com/Model-GLUE/Model-GLUE.

cs.LG↗

arXiv2024

Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation

Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. They are trained using preference datasets where each example consists of one input prompt, two responses, and a preference label. As curating a high-quality human labeled preference dataset is both time-consuming and expensive, people often rely on existing powerful LLMs for preference label generation. This can potentially introduce noise and impede RM training. In this work, we present RMBoost, a novel synthetic preference data generation paradigm to boost reward model quality. Unlike traditional methods, which generate two responses before obtaining the preference label, RMBoost first generates one response and selects a preference label, followed by generating the second more (or less) preferred response conditioned on the pre-selected preference label and the first response. This approach offers two main advantages. First, RMBoost reduces labeling noise since preference pairs are constructed intentionally. Second, RMBoost facilitates the creation of more diverse responses by incorporating various quality aspects (e.g., helpfulness, relevance, completeness) into the prompts. We conduct extensive experiments across three diverse datasets and demonstrate that RMBoost outperforms other synthetic preference data generation techniques and significantly boosts the performance of four distinct reward models.

cs.CL↗

arXiv2024

Online Drift Detection with Maximum Concept Discrepancy

Continuous learning from an immense volume of data streams becomes exceptionally critical in the internet era. However, data streams often do not conform to the same distribution over time, leading to a phenomenon called concept drift. Since a fixed static model is unreliable for inferring concept-drifted data streams, establishing an adaptive mechanism for detecting concept drift is crucial. Current methods for concept drift detection primarily assume that the labels or error rates of downstream models are given and/or underlying statistical properties exist in data streams. These approaches, however, struggle to address high-dimensional data streams with intricate irregular distribution shifts, which are more prevalent in real-world scenarios. In this paper, we propose MCD-DD, a novel concept drift detection method based on maximum concept discrepancy, inspired by the maximum mean discrepancy. Our method can adaptively identify varying forms of concept drift by contrastive learning of concept embeddings without relying on labels or statistical properties. With thorough experiments under synthetic and real-world scenarios, we demonstrate that the proposed method outperforms existing baselines in identifying concept drifts and enables qualitative analysis with high explainability.

cs.LG↗

arXiv2024

Realizing a spatially correlated lattice interferometer

Atom interferometers provide a powerful tool for measuring physical constants and testifying fundamental physics with unprecedented precision. Conventional atom interferometry focuses on the phase difference between two paths and utilizes matter waves with fixed coherence. Here, we report on realizing a Ramsey-Bord\'e interferometer of coherent matter waves dressed by a moving optical lattice in the gravity direction, and explore the resulting interference along multiple paths with tunable coherence. We investigate spatial correlations of atoms both within the lattice and between two arms by interferometry, and observe the emerging multiple interference peaks owing to the long-range coherence nature of the Bose-Einstein condensate. Our findings agree well with theoretical simulations, paving the way for high-precision interferometry with ultracold atoms.

cond-mat.quant-gas↗

arXiv2024

Magnetic-field oscillation model of pulsar radio emission: prediction of an observable effect

Since the discovery of pulsars, the rotating-lighthouse model has been the choice of model to explain the radiation pulsation of pulsars. After discovering that some main sequence stars (e.g., CU Virginis) and ultracool dwarfs (e.g., TVLM 513-46546) also behave like pulsars, the lighthouse model was again adopted to explain their pulse signals. Our research found that if we use the magnetic-field oscillation (MO) model, we can explain the pulse radiation results better regardless of whether its source is a neutron star, a main-sequence star or an ultracool dwarf. We propose a verifiable prediction that can be used to evaluate the MO model. Our prediction is that there is a 90 degree phase lag between the magnetic field and radio signal of TVLM 513-46546, and the zero-crossing point of the magnetic field is the moment when the direction of the light's circular polarization is reversed. No new observations are needed to check this prediction, but certain existing data needs to be re-mined.

astro-ph.HE↗