惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

V
V2EX
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
WordPress大学
WordPress大学
罗磊的独立博客
小众软件
小众软件
I
InfoQ
Y
Y Combinator Blog
宝玉的分享
宝玉的分享
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Hugging Face - Blog
Hugging Face - Blog
MyScale Blog
MyScale Blog
博客园 - 聂微东
Microsoft Security Blog
Microsoft Security Blog
H
Help Net Security
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园_首页
S
SegmentFault 最新的问题
博客园 - 三生石上(FineUI控件)
P
Proofpoint News Feed
博客园 - 司徒正美
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Azure Blog
Microsoft Azure Blog
Jina AI
Jina AI
N
Netflix TechBlog - Medium

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
Twisted homology jump loci, twisted Alexander polynomials...
[Submitted on 27 May 2026 (v1), last revised 11 Aug 2026 (this v · 2026-05-28 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:We introduce the twisted homology jump loci of a space $X$: the jump loci for homology with coefficients in rank-one local systems, twisted by a fixed finite-dimensional representation $\sigma$ of $\pi_1(X)$. These loci refine the classical characteristic varieties, and their defining equations in degree one are the twisted Alexander polynomials of knot theory. Our main theorem is that their tropicalizations bound from above the Bieri--Neumann--Strebel--Renz (BNSR) $\Sigma$-invariants of $X$.
Twisting gains real ground. The resulting bound is strictly stronger than the untwisted tropical bounds obtained from the usual characteristic varieties: for a one-relator group whose $\Sigma^1$ was computed by Brown, the untwisted bound excludes only two directions in $H^1(G;\mathbb{R})$, whereas the twisted bound determines $\Sigma^1(G)$ exactly. For a compact orientable $3$-manifold $M$ with toroidal or empty boundary, the twisted bound is sharp: the union of the twisted tropical varieties over all finite-image integral representations of $\pi_1(M)$ computes $\Sigma^1(\pi_1(M))$, and hence recovers the fibered faces of the Thurston norm ball. Sharpness genuinely requires twisting: a non-fibered class enters the tropical variety through the vanishing of a twisted Alexander polynomial along it, and the untwisted polynomial need not vanish. For a compact Kähler manifold $X$, we prove that the first twisted Alexander polynomial $\Delta^{\sigma}(X)$ is either $0$ or $1$, for every representation $\sigma$ over every field, and that $\Sigma^1(\pi_1(X))$ is controlled by the hyperbolic orbifold fibrations of $X$ for every $\sigma$. The obstruction to Kählerianity that comes out of this is strictly finer than its untwisted counterpart: we exhibit groups $G$ with $\Delta(G)=1$ but $\Delta^{\sigma}(G)\ne 1$, which the twisted test excludes from being Kähler and the classical one does not.

Submission history

From: Yongqiang Liu [view email]
[v1] Wed, 27 May 2026 15:14:40 UTC (36 KB)
[v2] Tue, 11 Aug 2026 03:14:47 UTC (38 KB)