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Qi Bi

Publications and source records attributed to Qi Bi.

At least 19 recordsLinked to original sources
arXiv2026

Fluid-Dynamic Interference Modeling for LEO Mega-Constellations: A Spatiotemporal Kinetic Field Approach

Low Earth orbit (LEO) mega-constellations create a highly non-stationary interference environment that cannot be accurately captured by static stochastic-geometry snapshots. This paper proposes a kinetic interference field framework that models the constellation as a compressible fluid shell evolving under orbital kinematics. By mapping satellite motion into a continuum flux field, we derive a hydrodynamic conservation law for the aggregate interference and obtain a closed-form expression for the time-varying outage probability via moment matching. The analysis reveals that high-latitude ``interference surges'' are a direct consequence of orbital compression and boundary flux, rather than random anomalies. Numerical validation against ephemeris-driven Monte Carlo simulations confirms the accuracy of the framework across time evolution, latitude, and design parameters. Leveraging the closed-form model, we further show that the conventional $90^{\circ}$ polar-orbit design is not universally outage-optimal. Instead, an inclination angle near $79^{\circ}$ at low altitude achieves a favorable trade-off between coverage continuity and geometric interference isolation. The proposed framework provides a tractable analytical tool for interference-aware 6G non-terrestrial network (NTN) design.

cs.NI↗
arXiv2026

Proper Sea Surface Roughness Enhances the Performance of Near-Shore Maritime Networks

Accurate performance analysis for near-shore maritime wireless communication is essential for ensuring robust and reliable operations. However, existing analytical models often rely on oversimplified propagation assumptions, such as a perfectly smooth sea surface, which fail to capture the full dynamics of the maritime channel. In this paper, we develop a physically grounded analytical framework using stochastic geometry that bridges this gap. The spatial distribution of vessels is modeled as a non-homogeneous Poisson point process to reflect realistic near-port densities. We replace the idealized smooth-sea assumption by deriving a novel reflection coefficient from the classical Rayleigh criterion, which explicitly links the path loss to the significant wave height. Integrating this roughness-aware channel model into the stochastic geometry framework, we derive new analytical expressions for the uplink coverage probability and average ergodic rate, providing the first tractable characterization of aggregate interference under such dynamic conditions. The analysis reveals a sea-state-dependent reliability--capacity trade-off: roughness-induced attenuation of the coherent specular reflection can suppress destructive-interference nulls and improve reliability-oriented coverage, while reducing high-SINR and average-rate performance. Available measurements support the underlying roughness-sensitive reflection mechanism, but direct VHF validation under rough sea conditions remains unavailable; the corresponding rough-sea results are therefore interpreted as model-based predictions. A cross-frequency ablation further confirms the wavelength dependence of the roughness effect and shows that the reflection coefficient must be evaluated for the operating frequency.

cs.NI↗
arXiv2026

Digital Tides: A Fluid-Dynamic Framework for Flux-Aware Infrastructure Provisioning in UAV Logistics Networks

The emergence of high-frequency pulsating logistics unmanned aerial vehicle (UAV) swarms gives rise to ``Digital Tides'', i.e., complex traffic dynamics that challenge sustainable resource provisioning in mobile computing networks. Conventional infrastructure provisioning strategies, which typically rely on static snapshot-based analysis and localized density estimation, fail to capture the macroscopic advection of computational workloads. As a result, reactive resource activation suffers from inherent hysteresis, yielding nominal efficiency gains at the cost of mission-critical service loss at the advancing wavefront. To address this issue, we develop a fluid-based spatiotemporal framework by explicitly solving the continuity equation to characterize the macroscopic velocity field of the workload flow. Building on this framework, we propose a flux-aware asymmetric activation strategy that leverages the derived information flux vector as a kinematic precursor of demand propagation. Unlike symmetric thresholding, the proposed control logic decouples activation and deactivation dynamics. Theoretical analysis confirms the intrinsic spatial phase-lead of the flux signal and shows that the proposed strategy generates a proactive guard ring to compensate for service setup latency, including delays caused by mobile edge computing container cold-starts. We further derive closed-form expressions for instantaneous service availability and period-average energy efficiency. In addition, we formulate a quality-of-service-penalized metric to evaluate effective energy efficiency under strict outage constraints. Numerical results show that the proposed flux-driven strategy enables zero-latency tracking of the mobile wavefront and achieves a Pareto-optimal trade-off between service reliability and energy consumption, outperforming reactive baselines in dynamic logistics corridors.

cs.NI↗
arXiv2026

Vorticity Dissipation Based Routing: A Fluid-Kinetic Framework for Loop-Free Transport in Ultra-Dense Networks

Discrete routing protocols in ultra-dense wireless networks are constrained by signaling overhead and transient routing loops that degrade radio-resource efficiency. While continuum modeling provides a scalable alternative, existing scalar density approaches lack the vector geometric structure required to characterize these topological anomalies. This paper introduces a fluid-kinetic framework, vorticity dissipation-based routing (VDR), utilizing the Helmholtz-Hodge decomposition. We demonstrate that the macroscopic traffic flux can be orthogonally decoupled into a demand-driven irrotational component and a loop-induced solenoidal component representing routing vorticity. Building on this insight, we define network vorticity as a macroscopic metric to quantify topological inefficiency. Routing optimization is formulated as a gradient flow on an enstrophy functional, yielding a vorticity dissipation equation as the governing dynamic law. Lyapunov stability analysis proves that this mechanism ensures the monotonic decay of global enstrophy toward an asymptotically loop-free equilibrium. Numerical results validate that VDR suppresses realized forwarding loops, reduces end-to-end delay, maintains robust packet delivery, and exhibits near-linear scaling under fixed-area densification while explicitly accounting for the grid-dependent Poisson-solver cost.

cs.NI↗
arXiv2026

Collider Spin Tomography with Missing Neutrinos

Missing neutrinos need not destroy collider spin tomography. We formulate the visible measurement under kinematic ambiguities arising from invisible particles as a coarse-grained positive-operator-valued measure on the production spin density matrix. We show that information loss is governed by the null space of the resulting visible-data map, not by the number of kinematic solutions. In $e^+e^-\to\tau^+\tau^-\to\pi^+\pi^-+\nu\bar\nu$, the twofold ambiguity leaves only the antisymmetric spin-correlation combination $C_{nr}-C_{rn}$ unidentifiable, while the differential production rate and the remaining fourteen spin coefficients are identifiable. For practical reconstruction under kinematic ambiguities, we develop a self-consistent fixed-point unfolding method using only visible data, without assuming a theoretical production template. Closure tests in Standard Model and anomalous tau-dipole benchmarks show that the method reproduces the truth-level differential production rate and all identifiable spin coefficients, whereas the usual flat average over kinematic folds gives significantly biased reconstructions. When a nontrivial null space is present, the reconstructed identifiable subspace together with positivity yields controlled ranges for concurrence and the CHSH parameter.

hep-ph↗
arXiv2026

Fluid-Spatiotemporal Stochastic Geometry: Information Flow in Non-Stationary Fields

The fundamental limits of information flow in spatial networks are usually characterized under stationary spatial point processes, but this assumption cannot capture non-stationary regimes where the node intensity field evolves continuously in space and time. This paper develops Fluid-Spatiotemporal Stochastic Geometry (F-STSG), treating dynamic network topology as a hydrodynamic limit of the discrete node constellation. We formulate the identification of latent network dynamics as an inverse boundary value problem and, using the minimum kinetic energy principle from optimal transport, establish the existence and uniqueness of a scalar potential field governing the compressive evolution of network load. The resulting field-theoretic formulation couples continuous Lagrangian transport with discrete Eulerian interference geometry. Based on this model, we derive the information flux vector as a sufficient statistic for macroscopic advection and the material derivative as a kinematic predictor of topological divergence. We further characterize non-stationary network limits through energy-density scaling and source-channel interpretation, showing how coordination overhead, topology deformation, and control signaling requirements are linked to the kinematic entropy of the evolving network topology.

cs.NI↗
arXiv2026

Spectral-Progressive Thought Flow for Lightweight Multimodal Reasoning

Multimodal spatial reasoning often relies on long chains of intermediate textual and visual thoughts, where accumulating visual tokens and dense cross-modal attention incur substantial computation and memory overhead. To address this challenge, we propose Spectral-Progressive Thought Flow (SpecFlow), a novel lightweight multimodal spatial reasoning framework that represents intermediate visual thoughts in a fixed-size discrete cosine space. By exploiting strong energy compaction, SpecFlow preserves global layout and relational structure while introducing high-frequency details only when increased spatial precision is required. To align visual state evolution with linguistic intent, classifier-free guidance enables autoregressive textual thoughts to steer flow-based updates of the visual workspace/state without expanding the context. As a result, SpecFlow maintains a bounded visual workspace whose updates depend only on the current visual state and accumulated textual trace, enabling long-horizon inference with stable latency and memory usage independent of reasoning depth. Empirical results show that SpecFlow achieves competitive or superior reasoning performance while reducing computation and KV cache costs by up to 2.1 times.

cs.LG↗
arXiv2025

SAM3-I: Segment Anything with Instructions

Segment Anything Model 3 (SAM3) advances open-vocabulary segmentation through promptable concept segmentation, enabling users to segment all instances associated with a given concept using short noun-phrase (NP) prompts. While effective for concept-level grounding, real-world interactions often involve far richer natural-language instructions that combine attributes, relations, actions, states, or implicit reasoning. Currently, SAM3 relies on external multi-modal agents to convert complex instructions into NPs and conducts iterative mask filtering, leading to coarse representations and limited instance specificity. In this work, we present SAM3-I, an instruction-following extension of the SAM family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. Built upon SAM3, SAM3-I introduces an instruction-aware cascaded adaptation mechanism with dedicated alignment losses that progressively aligns expressive instruction semantics with SAM3's vision-language representations, enabling direct interpretation of natural-language instructions while preserving its strong concept recall ability. To enable instruction-following learning, we introduce HMPL-Instruct, a large-scale instruction-centric dataset that systematically covers hierarchical instruction semantics and diverse target granularities. Experiments demonstrate that SAM3-I achieves appealing performance across referring and reasoning-based segmentation, showing that SAM3 can be effectively extended to follow complex natural-language instructions without sacrificing its original concept-driven strengths. Code and dataset are available at https://github.com/debby-0527/SAM3-I.

cs.CV↗
arXiv2025

Probing Scalar-Mediated Sterile Neutrinos with Gravitational Wave and Colliders Signals

We propose a UV-complete extension of the Standard Model in which a gauge-singlet scalar $S$ acquires a vacuum expectation value, generates a Majorana mass for a sterile neutrino $N$, and mixes with the Higgs field. This framework addresses neutrino masses via a seesaw mechanism and, for sufficiently large scalar mixing, can also drive a strong first-order electroweak phase transition, producing gravitational-wave (GW) signals potentially detectable by GW observatories. The Higgs-$S$ mixing also enhances sterile-neutrino pair production at colliders through $s$-channel exchange of the Higgs and $S$. Owing to the small active-sterile mixing angle, $N$ is generically long-lived, yielding characteristic displaced-vertex signatures. The combination of GW observations and displaced-vertex searches at colliders provides complementary cross-checks of the model parameter space.

hep-ph↗
arXiv2025

Single Domain Generalization for Multimodal Cross-Cancer Prognosis via Dirac Rebalancer and Distribution Entanglement

Deep learning has shown remarkable performance in integrating multimodal data for survival prediction. However, existing multimodal methods mainly focus on single cancer types and overlook the challenge of generalization across cancers. In this work, we are the first to reveal that multimodal prognosis models often generalize worse than unimodal ones in cross-cancer scenarios, despite the critical need for such robustness in clinical practice. To address this, we propose a new task: Cross-Cancer Single Domain Generalization for Multimodal Prognosis, which evaluates whether models trained on a single cancer type can generalize to unseen cancers. We identify two key challenges: degraded features from weaker modalities and ineffective multimodal integration. To tackle these, we introduce two plug-and-play modules: Sparse Dirac Information Rebalancer (SDIR) and Cancer-aware Distribution Entanglement (CADE). SDIR mitigates the dominance of strong features by applying Bernoulli-based sparsification and Dirac-inspired stabilization to enhance weaker modality signals. CADE, designed to synthesize the target domain distribution, fuses local morphological cues and global gene expression in latent space. Experiments on a four-cancer-type benchmark demonstrate superior generalization, laying the foundation for practical, robust cross-cancer multimodal prognosis. Code is available at https://github.com/HopkinsKwong/MCCSDG

cs.CV↗
arXiv2025

Ultimate Quantum Precision Limit at Colliders: Conditions and Case Studies

We investigate whether collider experiments can reach the quantum limit of precision, defined by the quantum Fisher information (QFI), using only classical observables such as particle momenta. As a case study, we focus on the $\tau^+\tau^-$ system and the decay channel $\tau \to \pi \nu$, which offers maximal spin-analyzing power and renders the decay a projective measurement. We develop a general framework to determine when collider measurements can, in principle, saturate the QFI in an entangled biparticle system, and this framework extends naturally to other such systems. Within this framework, QFI saturation occurs if and only if the symmetric logarithmic derivative (SLD) commutes with a complete set of orthonormal separable projectors associated with collider-accessible measurements. This separability condition, reflecting the independence of decay amplitudes, is highly nontrivial. To meet this condition, a key requirement is that the spin density matrix be rank-deficient, allowing the SLD sufficient freedom. We show that the classical Fisher information asymptotically saturates the QFI for magnetic dipole moments and CP-violating Higgs interactions in selected phase-space regions, but not for electric dipole moments. These results bridge quantum metrology and collider physics, providing a systematic method to identify quantum-optimal sensitivity in collider experiments.

hep-ph↗
arXiv2025

MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection

We present a new adaptation method MaCP, Minimal yet Mighty adaptive Cosine Projection, that achieves exceptional performance while requiring minimal parameters and memory for fine-tuning large foundation models. Its general idea is to exploit the superior energy compaction and decorrelation properties of cosine projection to improve both model efficiency and accuracy. Specifically, it projects the weight change from the low-rank adaptation into the discrete cosine space. Then, the weight change is partitioned over different levels of the discrete cosine spectrum, and each partition's most critical frequency components are selected. Extensive experiments demonstrate the effectiveness of MaCP across a wide range of single-modality tasks, including natural language understanding, natural language generation, text summarization, as well as multi-modality tasks such as image classification and video understanding. MaCP consistently delivers superior accuracy, significantly reduced computational complexity, and lower memory requirements compared to existing alternatives.

cs.LG↗
arXiv2025

DGFamba: Learning Flow Factorized State Space for Visual Domain Generalization

Domain generalization aims to learn a representation from the source domain, which can be generalized to arbitrary unseen target domains. A fundamental challenge for visual domain generalization is the domain gap caused by the dramatic style variation whereas the image content is stable. The realm of selective state space, exemplified by VMamba, demonstrates its global receptive field in representing the content. However, the way exploiting the domain-invariant property for selective state space is rarely explored. In this paper, we propose a novel Flow Factorized State Space model, dubbed as DG-Famba, for visual domain generalization. To maintain domain consistency, we innovatively map the style-augmented and the original state embeddings by flow factorization. In this latent flow space, each state embedding from a certain style is specified by a latent probability path. By aligning these probability paths in the latent space, the state embeddings are able to represent the same content distribution regardless of the style differences. Extensive experiments conducted on various visual domain generalization settings show its state-of-the-art performance.

cs.CV↗
arXiv2025

Learning Fine-grained Domain Generalization via Hyperbolic State Space Hallucination

Fine-grained domain generalization (FGDG) aims to learn a fine-grained representation that can be well generalized to unseen target domains when only trained on the source domain data. Compared with generic domain generalization, FGDG is particularly challenging in that the fine-grained category can be only discerned by some subtle and tiny patterns. Such patterns are particularly fragile under the cross-domain style shifts caused by illumination, color and etc. To push this frontier, this paper presents a novel Hyperbolic State Space Hallucination (HSSH) method. It consists of two key components, namely, state space hallucination (SSH) and hyperbolic manifold consistency (HMC). SSH enriches the style diversity for the state embeddings by firstly extrapolating and then hallucinating the source images. Then, the pre- and post- style hallucinate state embeddings are projected into the hyperbolic manifold. The hyperbolic state space models the high-order statistics, and allows a better discernment of the fine-grained patterns. Finally, the hyperbolic distance is minimized, so that the impact of style variation on fine-grained patterns can be eliminated. Experiments on three FGDG benchmarks demonstrate its state-of-the-art performance.

cs.CV↗
arXiv2025

SSH: Sparse Spectrum Adaptation via Discrete Hartley Transformation

Low-rank adaptation (LoRA) has been demonstrated effective in reducing the trainable parameter number when fine-tuning a large foundation model (LLM). However, it still encounters computational and memory challenges when scaling to larger models or addressing more complex task adaptation. In this work, we introduce Sparse Spectrum Adaptation via Discrete Hartley Transformation (SSH), a novel approach that significantly reduces the number of trainable parameters while enhancing model performance. It selects the most informative spectral components across all layers, under the guidance of the initial weights after a discrete Hartley transformation (DHT). The lightweight inverse DHT then projects the spectrum back into the spatial domain for updates. Extensive experiments across both single-modality tasks such as language understanding and generation and multi-modality tasks such as video-text understanding demonstrate that SSH outperforms existing parameter-efficient fine-tuning (PEFT) methods while achieving substantial reductions in computational cost and memory requirements.

cs.CV↗
arXiv2024

MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection

We present a new adaptation method MaCP, Minimal yet Mighty adaptive Cosine Projection, that achieves exceptional performance while requiring minimal parameters and memory for fine-tuning large foundation models. Its general idea is to exploit the superior energy compaction and decorrelation properties of cosine projection to improve both model efficiency and accuracy. Specifically, it projects the weight change from the low-rank adaptation into the discrete cosine space. Then, the weight change is partitioned over different levels of the discrete cosine spectrum, and each partition's most critical frequency components are selected. Extensive experiments demonstrate the effectiveness of MaCP across a wide range of single-modality tasks, including natural language understanding, natural language generation, text summarization, as well as multi-modality tasks such as image classification and video understanding. MaCP consistently delivers superior accuracy, significantly reduced computational complexity, and lower memory requirements compared to existing alternatives.

cs.LG↗
arXiv2024

Long-lived Sterile Neutrino Searches at Future Muon Colliders

We explore the potential of studying sterile neutrinos at a future high-energy muon collider, where these particles can generate small active neutrino masses via the seesaw mechanism and exhibit long-lived particle signatures. A Dirac sterile neutrino model with ${\rm U(1)}_{L_\mu-L_\tau}$ symmetry is introduced, where the heavy right-handed neutrino ($N_R$) produces tiny active neutrino masses, and the light left-handed neutrino ($N_L$) naturally behaves as a long-lived particle. The ${\rm U(1)}_{L_\mu-L_\tau}$ gauge symmetry also enhances sterile neutrino pair production at a future high-energy muon collider. Using the displaced vertex method, the muon collider can search for heavy sterile neutrino, especially for $m_L> m_W$. We find that a muon collider with $\sqrt{s} = 3~ (10)$ TeV and luminosity $L=1~(10)$ ab$^{-1}$ can probe $N_L$ masses of $m_L \in [100,~1500~(5000)]$ GeV and mixing angles $\theta_{\nu L} \in [10^{-13},~10^{-6}]$.

hep-ph↗
arXiv2024

Learning Spectral-Decomposed Tokens for Domain Generalized Semantic Segmentation

The rapid development of Vision Foundation Model (VFM) brings inherent out-domain generalization for a variety of down-stream tasks. Among them, domain generalized semantic segmentation (DGSS) holds unique challenges as the cross-domain images share common pixel-wise content information but vary greatly in terms of the style. In this paper, we present a novel Spectral-dEcomposed Token (SET) learning framework to advance the frontier. Delving into further than existing fine-tuning token & frozen backbone paradigm, the proposed SET especially focuses on the way learning style-invariant features from these learnable tokens. Particularly, the frozen VFM features are first decomposed into the phase and amplitude components in the frequency space, which mainly contain the information of content and style, respectively, and then separately processed by learnable tokens for task-specific information extraction. After the decomposition, style variation primarily impacts the token-based feature enhancement within the amplitude branch. To address this issue, we further develop an attention optimization method to bridge the gap between style-affected representation and static tokens during inference. Extensive cross-domain experiments show its state-of-the-art performance.

cs.CV↗