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Bo Fu

Publications and source records attributed to Bo Fu.

At least 19 recordsLinked to original sources
arXiv2026

Hidden topology and strong quantum metric bounds in trivial systems

The quantum metric integral (QMI) in two-dimensional (2D) systems is conventionally bounded from below by the Chern number. For systems with zero Chern number or identically vanishing Berry curvature, however, this bound becomes trivial and provides no useful geometric constraints. Here, we develop a dimension-reduction framework that decomposes the 2D QMI into lower-dimensional components in a nested-loop way. With this method, we establish a nonzero lower bound on the QMI arising from one-dimensional topological obstructions even when the conventional 2D topology is trivial. We explicitly demonstrate this mechanism in a tilted 2D Su-Schrieffer-Heeger model and an anisotropic Wilson-Dirac model with chiral symmetry. The resulting lower bounds of QMI are determined by the quantized Wannier bands along two different directions. We further investigate the quantum geometry in higher-order topological phases following the same strategy. By introducing Wannier-band basis obtained from the nested Wilson loop, we demonstrate that the Wannier-band QMI is bounded from below by the higher-order topological invariant, e.g. the quadrupole moment in Benalcazar-Bernevig-Hughes model. Our results establish nonzero lower bounds on QMI from a dimension-reduction framework, thereby generalizing the fundamental relation between quantum geometry and topology.

cond-mat.mes-hall↗
arXiv2026

First-Order Topological FFLO Transition and Superconducting Diode Sign Reversal in Altermagnetic Nanowires

Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state conventionally emerges via a second-order phase transition driven by finite magnetization. Here we show that a spin-orbit-coupled nanowire proximitized to $d$-wave altermagnets -- with zero net magnetization -- can realize topological FFLO states through a first-order transition, marked by a sharp sign-reversing superconducting diode effect. The altermagnetic field generates band-resolved competing pairing channels, giving rise to a double-valley free energy landscape whose global minimum switches discontinuously. It consequently leads to a first-order topological FFLO transition with simultaneous jumps in the Cooper pairing amplitude and finite center-of-mass momentum. Remarkably, this discontinuous topological reconfiguration substantially enhances the diode efficiency and drives a characteristic sharp sign reversal across the transition. The mechanism of such exotic phenomena is captured by Ginzburg--Landau theory. Our results provide a field-free altermagnetic route to topological FFLO states and identify their direct transport fingerprint.

cond-mat.supr-con↗
arXiv2026

3-VASS Reachability is in EXPSPACE

A VASS can be viewed as a finite-state automaton manipulating a fixed number (called its dimension) of counters holding non-negative values. The reachability problem, asking whether there is a run from one configuration, defined by a state and values of the counters, to another configuration, has been a long-standing algorithmic challenge in theoretical computer science. When the dimension is part of the input, the problem has been shown to be ACKERMANN-complete in 2021. For fixed dimension greater than 2, and in particular for dimension 3, the exact complexity of the reachability problem remains unclear. For a long time the known algorithms for the 3-dimensional VASS reachability problem had been non-elementary, while the best known lower bound is merely PSPACE hardness inherited from dimension 2. A recent breakthrough in (Czerwi\'nski, Jecker, Lasota, Orlikowski, ICALP 2025) gave the first elementary upper bound for the problem, namely 2-EXPSPACE. In this paper it is shown that the reachability problem in 3-VASS belongs to EXPSPACE. The proof is based on a hierarchical pumpability analysis, yielding a doubly-exponential length bound on the shortest runs between two configurations.

cs.FL↗
arXiv2026

Suppression of Extrinsic Anomalous Hall Conductivity in Disordered Parity Anomalous Semimetal

We present an analytical investigation of the extrinsic contributions to the anomalous Hall conductivity in the context of the half-quantized Hall effect observed in disordered parity anomalous semimetal emerged from semi-magnetic topological insulator thin films. The gapless Dirac cone surface state, which embodies the quintessence of the half-quantized Hall effect, exhibits remarkable robustness against disorder scattering. Two primary extrinsic mechanisms, the side-jump and skew-scattering, are deemed irrelevant and make no contributions. These results establish the parity anomalous semimetal as a disorder-resilient quantum phase, thereby providing insights into Dirac fermion physics.

cond-mat.dis-nn↗
arXiv2026

STEDiff: Strengthening Text Embedding for Text-to-Image Alignment in Diffusion Model

Although pretrained text-to-image (T2I) generation models can produce high-quality images, they often fail to faithfully reflect the semantic intent of complex prompts due to stochastic noise and inherent model limitations. This issue frequently manifests as the model overlooking specific objects or failing to correctly bind attributes to their corresponding entities, a challenge referred to as semantic alignment. Unlike existing approaches that rely on computationally expensive fine-tuning or labor-intensive layout priors, we propose STEDiff, a training-free method designed to enhance semantic representations directly within the text-embedding space. Specifically, we introduce a method that primarily leverages the [EOT] token to strengthen the relevant semantics of sub-sentences and then replaces the corresponding tokens in the original prompt. Furthermore, a novel semantic enhancement loss is incorporated to enforce spatial constraints, ensuring that the semantics of each entity are precisely mapped to their respective image regions. Extensive quantitative and qualitative evaluations on the T2I-CompBench demonstrate that our method notably improves semantic consistency and generation integrity in complex scenarios.

cs.CV↗
arXiv2026

MiRD: Reliable Set-Valued Prediction for Open-Ended Question Answering via Miscoverage Risk Decomposition

Reliable set-valued prediction provides a principled way to mitigate hallucinations in open-ended question answering (QA), yet existing conformal approaches typically rely on a fragile premise: finite sampling must already produce at least one admissible candidate, or calibration examples violating this condition are discarded. In this paper, we introduce MiRD, a two-stage framework that decomposes overall miscoverage into sampling failure and conditional selection failure. In Stage I, MiRD establishes an expectation-level marginal upper bound on the probability that finite sampling produces no admissible answer under a fixed budget. In Stage II, conditioned on sampling success, MiRD calibrates a conformal selection threshold using admission-correlated nonconformity scores defined over the full calibration set, thereby preserving calibration-set integrity. Across three open-ended QA datasets and eight models, MiRD controls sampling risk, conditional selection risk, and overall miscoverage, while yielding tighter first-stage bounds than PAC-style alternatives and more adaptive prediction sets than successful-only calibration.

cs.CL↗
arXiv2026

Set-Valued Prediction for Large Language Models with Feasibility-Aware Coverage Guarantees

Large language models (LLMs) inherently operate over a large generation space, yet conventional usage typically reports the most likely generation (MLG) as a point prediction, which underestimates the model's capability: although the top-ranked response can be incorrect, valid answers may still exist within the broader output space and can potentially be discovered through repeated sampling. This observation motivates moving from point prediction to set-valued prediction, where the model produces a set of candidate responses rather than a single MLG. In this paper, we propose a principled framework for set-valued prediction, which provides feasibility-aware coverage guarantees. We show that, given the finite-sampling nature of LLM generation, coverage is not always achievable: even with multiple samplings, LLMs may fail to yield an acceptable response for certain questions within the sampled candidate set. To address this, we establish a minimum achievable risk level (MRL), below which statistical coverage guarantees cannot be satisfied. Building on this insight, we then develop a data-driven calibration procedure that constructs prediction sets from sampled responses by estimating a rigorous threshold, ensuring that the resulting set contains a correct answer with a desired probability whenever the target risk level is feasible. Extensive experiments on six language generation tasks with five LLMs demonstrate both the statistical validity and the predictive efficiency of our framework.

cs.CL↗
arXiv2026

Invariant quantile regression for heterogeneous environments

In this paper, we propose an invariant quantile regression (IQR) framework specifically designed for multi-environment datasets, which captures the invariance across different environments. This framework is closely related to transfer learning, causal inference, and fair machine learning, and is motivated by scenarios in which the conditional probability of the response given covariates varies, while certain key variables remain invariant. This perspective differs notably from previous works that restrict attention to the conditional mean, which is often insufficient to capture the full causal relationships between covariates and the response in heterogeneous environments. In contrast, quantile-based invariance naturally accommodates heterogeneity, and aligns more closely with structural causal models, in which variables invariant across environments at one or multiple quantile levels directly indicate potential and stable causal variables. Moreover, we show that IQR may yield a larger set of endogenous variables compared to the conditional mean framework, which in turn promotes more effective exclusion of spurious (non-causal) variables. To achieve this, we introduce a Kernel-Smoothed Invariant Quantile Regression (KS-IQR) estimator, which leverages the underlying invariance structure and heterogeneity among environments, ensuring stable estimation across multiple environments. We establish the causal discovery properties of our method, demonstrate its ability to overcome the ``curse of endogeneity'', and derive an $\ell_2$ error bound for our estimator, all in a non-asymptotic framework. We apply our method to real data for causal discovery and obtain biologically meaningful relationships, recovering known signaling pathways and revealing additional quantile-specific effects.

stat.ME↗
arXiv2025

Altermagnetism Induced Bogoliubov Fermi Surfaces Form Topological Superconductivity

We propose a novel type of topological superconductivity based on Bogoliubov Fermi surfaces (BFSs) in an altermagnetic topological insulator proximitized by an s-wave superconductor. The 3D altermagnetic topological insulator is characterized by zero-energy surface states in bulk nodal-ring phases and anisotropically shifted surface Dirac cones in topological insulating phases. The altermagnetic order in combination with superconductivity gives rise to highly anisotropic superconducting gaps with crystal-facet-dependent BFSs at the physical boundaries. These particular BFSs provide distinct platforms to realize topological superconductivity. We propose a quasi-1D nanowire in which the anisotropic BFSs experience topological phase transitions due to quantum confinement leading to Majorana zero modes (MZMs) at its ends. We further consider vortex phase transitions in the superconducting altermagnetic topological insulators. Remarkably, we find that the altermagnetic order allows us to transit between two distinct type of MZMs, one type is located at the vortex line, while the other type is located at the physical boundaries. Our work paves a new avenue utilizing altermagnetism-induced BFSs to engineer topological superconductivity through crystal anisotropy and quantum confinement.

cond-mat.supr-con↗
arXiv2025

Transfer learning for high-dimensional Factor-augmented sparse linear model

In this paper, we study transfer learning for high-dimensional factor-augmented sparse linear models, motivated by applications in economics and finance where strongly correlated predictors and latent factor structures pose major challenges for reliable estimation. Our framework simultaneously mitigates the impact of high correlation and removes the additional contributions of latent factors, thereby reducing potential model misspecification in conventional linear modeling. In such settings, the target dataset is often limited, but multiple heterogeneous auxiliary sources may provide additional information. We develop transfer learning procedures that effectively leverage these auxiliary datasets to improve estimation accuracy, and establish non-asymptotic $\ell_1$- and $\ell_2$-error bounds for the proposed estimators. To prevent negative transfer, we introduce a data-driven source detection algorithm capable of identifying informative auxiliary datasets and prove its consistency. In addition, we provide a hypothesis testing framework for assessing the adequacy of the factor model, together with a procedure for constructing simultaneous confidence intervals for the regression coefficients of interest. Numerical studies demonstrate that our methods achieve substantial gains in estimation accuracy and remain robust under heterogeneity across datasets. Overall, our framework offers a theoretical foundation and a practically scalable solution for incorporating heterogeneous auxiliary information in settings with highly correlated features and latent factor structures.

stat.ME↗
arXiv2025

Nearly semi-elliptic relation between the minimal conductivity and Hall conductivity in unpaired Dirac fermions

Electric conductivities may reveal the topological and magnetic properties of band structures in solids, especially for two-dimensional unpaired Dirac fermions. In this work, we evaluate the longitudinal and Hall conductivity for unpaired Dirac fermions in the framework of the self-consistent Born approximation and find a nearly semi-elliptic relation between the minimal conductivity and Hall conductivities in the Dirac fermions. Near the charge neutrality point, disorder may drive a metal-insulator transition, and enhance the longitudinal conductivity. For the massless case, the minimal conductivity $\sigma_{xx}^*$ coexists with the half-quantized Hall conductivity $e^2/2h$, forming an indicator for the parity anomalous semimetal. The relation signals a disorder-induced metallic phase that bridges two topologically distinct insulating phases, and agrees with the recent experimental observation in magnetic topological insulators.

cond-mat.mes-hall↗
arXiv2025

Unifiedly Efficient Inference on All-Dimensional Targets for Large-Scale GLMs

The scalability of Generalized Linear Models (GLMs) for large-scale, high-dimensional data often forces a trade-off between computational feasibility and statistical accuracy, particularly for inference on pre-specified parameters. While subsampling methods mitigate computational costs, existing estimators are typically constrained by a suboptimal $r^{-1/2}$ convergence rate, where $r$ is the subsample size. This paper introduces a unified framework that systematically breaks this barrier, enabling efficient and precise inference regardless of the dimension of the target parameters. To overcome the accuracy loss and enhance computational efficiency, we propose three estimators tailored to different scenarios. For low-dimensional targets, we propose a de-variance subsampling (DVS) estimator that achieves a sharply improved convergence rate of $\max\{r^{-1}, n^{-1/2}\}$, permitting valid inference even with very small subsamples. As $r$ grows, a multi-step refinement of our estimator is proven to be asymptotically normal and semiparametric efficient when $r/\sqrt{n} \to \infty$, matching the performance of the full-sample estimator-a property confirmed by its Bahadur representation. Critically, we provide an improved principle to high-dimensional targets, developing a novel decorrelated score function that facilitates simultaneous inference for a diverging number of pre-specified parameters. Comprehensive numerical experiments demonstrate that our framework delivers a superior balance of computational efficiency and statistical accuracy across both low- and high-dimensional inferential tasks in large-scale GLM, thereby realizing the promise of unifiedly efficient inference for large-scale GLMs.

stat.ME↗
arXiv2025

Parity Anomalous Semimetal with Minimal Conductivity Induced by an In-Plane Magnetic Field

The interplay between topological materials and local symmetry breaking gives rise to diverse topological quantum phenomena. A notable example is the parity anomalous semimetal (PAS), which hosts a single unpaired gapless Dirac cone with a half-integer quantized Hall conductivity. Here, we realize this phase in a magnetic topological sandwich structure by applying an in-plane magnetic field. This configuration aligns the magnetization of one surface in-plane while preserving magnetization out-of-plane on the opposite surface, satisfying the condition for a gapless surface state near the Fermi level on only one surface. Our key evidence is a distinctive two-stage evolution of the conductivity tensor ($\sigma_{xy}$, $\sigma_{xx}$). The first stage culminates in the PAS at the fixed point ($\frac{e^2}{2h}$, $m \frac{e^2}{h}$), where $m \approx 0.6$ corresponds to the minimal longitudinal conductivity of a single gapless Dirac cone of fermions on a 2D lattice. This PAS state remains stabilized and is superposed with a gapped band flow in the second stage. This observation demonstrates that this state stabilized by the in-plane field resists localization--contrary to conventional expectations for 2D electron systems with broken time reversal symmetry. The dynamic transition from an integer quantized insulator to a half-integer quantized semimetal establishes this material system as a versatile platform for exploring parity anomaly physics.

cond-mat.mtrl-sci↗
arXiv2025

Reinterpretation of chiral anomaly on a lattice

The chiral anomaly is a quantum mechanical effect for massless Dirac fermions in both particle physics and condensed matter physics. Here we present a set of effective models for single massless Dirac fermions in one- and three-dimensions in the whole Brillouin zone from higher-dimensional Chern insulators, which uniquely capture both the chiral fermion behavior near the Dirac point and high-energy states at Brillouin zone boundaries. In the presence of electromagnetic fields, the chiral coefficient $C_{5}$ is found to be chemical-potential dependent in general, but quantized precisely at the Fermi surface where chiral symmetry is preserved. This result provides an alternative interpretation of chiral anomaly on a lattice: the anomaly is caused by the symmetry-broken states far below the Fermi surface, and protected by the local chiral symmetry. Our analysis here might provide a potential theoretical foundation for applying the concept of chiral anomaly in condensed matter physics.

cond-mat.mes-hall↗
arXiv2025

Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents

We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a goal state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveraging joint configuration space search, classical planning, multi-agent pathfinding, and expert heuristics. We evaluate the five approaches empirically for plan quality and scalability. Despite the exponential relation between search space size and block number, our methods demonstrate efficiency in creating rearrangement plans for deeply buried blocks in up to 80x80 grids.

cs.AI↗
arXiv2025

Half-Quantized Hall Metal and Marginal Metal in Disordered Magnetic Topological Insulators

A semimagnetic topological insulator -- a heterostructure combining a topological insulator with a ferromagnet -- exhibits a half-quantized Hall effect, characterized by a quantized Hall conductance of $\frac{1}{2}\frac{e^{2}}{h}$ (where $e$ is the elementary charge and $h$ is the Planck constant), which reinforces the established understanding of topological phenomena in condensed matter physics. However, its stability in realistic, disordered systems remains poorly understood. Here, we demonstrate the robustness of the half-quantized Hall effect in weakly disordered systems, stemming from a single gapless Dirac cone of fermions and coexisting with weak antilocalization due to the $\pi$ Berry phase that suppresses backscattering. Furthermore, we uncover a marginal metallic phase emerging between weak antilocalization and Anderson insulation -- a transition that defies conventional metal-insulator transitions by lacking an isolated critical point -- where both conductance and normalized localization length exhibit scale invariance, independent of system size. The half-quantized Hall metal and the marginal metallic phase challenge existing localization theories and provide insights into disorder-driven topological phase transitions in magnetic topological insulators, opening avenues for exploring quantum materials and next-generation electronic devices.

cond-mat.dis-nn↗
arXiv2025

Quantum Geometric Renormalization of the Hall Coefficient and Unconventional Hall Resistivity in ZrTe5

The anomalous Hall effect (AHE), conventionally associated with time-reversal symmetry breaking in ferromagnetic materials, has recently been observed in nonmagnetic topological materials, raising questions about its origin. We unravel the unconventional Hall response in the nonmagnetic Dirac material ZrTe5, known for its massive Dirac bands and unique electronic and transport properties. Using the Kubo-Streda formula within the Landau level framework, we explore the interplay of quantum effects induced by the magnetic field (B) and disorder across the semiclassical and quantum regimes. In the semiclassical regime, the Hall resistivity remains linear in the magnetic field, but the Hall coefficient will be renormalized by the quantum geometric effects and electron-hole coherence, especially at low carrier densities where the disorder scattering dominates. In quantum limit, the Hall conductivity exhibits an unsaturating 1/B scaling. As a result, the transverse conductivity dominates transport in the ultra-quantum limit, and the Hall resistivity crosses over from B to B^{-1} dependence as the system transitions from the semiclassical regime to the quantum limit. This work elucidates the mechanisms underlying the unconventional Hall effect in ZrTe5 and provides insights into the AHE in other nonmagnetic Dirac materials as well.

cond-mat.mes-hall↗
arXiv2025

Large Foundation Model for Ads Recommendation

Online advertising relies on accurate recommendation models, with recent advances using pre-trained large-scale foundation models (LFMs) to capture users' general interests across multiple scenarios and tasks. However, existing methods have critical limitations: they extract and transfer only user representations (URs), ignoring valuable item representations (IRs) and user-item cross representations (CRs); and they simply use a UR as a feature in downstream applications, which fails to bridge upstream-downstream gaps and overlooks more transfer granularities. In this paper, we propose LFM4Ads, an All-Representation Multi-Granularity transfer framework for ads recommendation. It first comprehensively transfers URs, IRs, and CRs, i.e., all available representations in the pre-trained foundation model. To effectively utilize the CRs, it identifies the optimal extraction layer and aggregates them into transferable coarse-grained forms. Furthermore, we enhance the transferability via multi-granularity mechanisms: non-linear adapters for feature-level transfer, an Isomorphic Interaction Module for module-level transfer, and Standalone Retrieval for model-level transfer. LFM4Ads has been successfully deployed in Tencent's industrial-scale advertising platform, processing tens of billions of daily samples while maintaining terabyte-scale model parameters with billions of sparse embedding keys across approximately two thousand features. Since its production deployment in Q4 2024, LFM4Ads has achieved 10+ successful production launches across various advertising scenarios, including primary ones like Weixin Moments and Channels. These launches achieve an overall GMV lift of 2.45% across the entire platform, translating to estimated annual revenue increases in the hundreds of millions of dollars.

cs.LG↗