Tootfinder

On decomposability and subdifferential of the tensor nuclear norm
Jiewen Guan, Bo Jiang, Zhening Li
https://arxiv.org/abs/2510.04647 https://arxiv.org/pdf/…

On decomposability and subdifferential of the tensor nuclear norm
We study the decomposability and the subdifferential of the tensor nuclear norm. Both concepts are well understood and widely applied in matrices but remain unclear for higher-order tensors. We show that the tensor nuclear norm admits a full decomposability over specific subspaces and determine the largest possible subspaces that allow the full decomposability. We derive novel inclusions of the subdifferential of the tensor nuclear norm and study its subgradients in a variety of subspaces of in…

Quantum approximate optimization of bosonic finite-state systems
Shakib Daryanoosh
https://arxiv.org/abs/2510.05576 https://arxiv.org/pdf/2510.05576…

Quantum approximate optimization of bosonic finite-state systems
There exist numerous problems in nature inherently described by finite $D$-dimensional states. Formulating these problems for execution on qubit-based quantum hardware requires mapping the qudit Hilbert space to that of multiqubit which may be exponentially larger. To exclude the infeasible subspace, one common approach relies on penalizing the objective function. However, this strategy can be inefficient as the size of the illegitimate subspace grows. Here we propose to employ the Hamiltonian-…

An Empirical Study of Security-Policy Related Issues in Open Source Projects
Rintaro Kanaji, Brittany Reid, Yutaro Kashiwa, Raula Gaikovina Kula, Hajimu Iida
https://arxiv.org/abs/2510.05604

An Empirical Study of Security-Policy Related Issues in Open Source Projects
GitHub recommends that projects adopt a SECURITY.md file that outlines vulnerability reporting procedures. However, the effectiveness and operational challenges of such files are not yet fully understood. This study aims to clarify the challenges that SECURITY.md files face in the vulnerability reporting process within open-source communities. Specifically, we classified and analyzed the content of 711 randomly sampled issues related to SECURITY.md. We also conducted a quantitative comparative …

Prior-Aligned Meta-RL: Thompson Sampling with Learned Priors and Guarantees in Finite-Horizon MDPs
Runlin Zhou, Chixiang Chen, Elynn Chen
https://arxiv.org/abs/2510.05446 https:…

Prior-Aligned Meta-RL: Thompson Sampling with Learned Priors and Guarantees in Finite-Horizon MDPs
We study meta-reinforcement learning in finite-horizon MDPs where related tasks share similar structures in their optimal action-value functions. Specifically, we posit a linear representation $Q^*_h(s,a)=Φ_h(s,a)\,θ^{(k)}_h$ and place a Gaussian meta-prior $ \mathcal{N}(θ^*_h,Σ^*_h)$ over the task-specific parameters $θ^{(k)}_h$. Building on randomized value functions, we propose two Thompson-style algorithms: (i) MTSRL, which learns only the prior mean and performs posterior sampling wit…

Spectral scheme for an energetic Fokker-Planck equation with $\kappa$-distribution steady states
Hugo Parada (IMT), Claudia Negulescu
https://arxiv.org/abs/2509.04911 https://…

Spectral scheme for an energetic Fokker-Planck equation with $κ$-distribution steady states
The concern of the present paper is the design of efficient numerical schemes for a specific Fokker-Planck equation describing the dynamics of energetic particles occurring in thermonuclear fusion plasmas (runaway electrons for example). In the long-time limit, the velocity distribution function of these particles tends towards a thermal non-equilibrium $κ$-distribution function which is a steady-state of the considered Fokker-Planck equation. These $κ$-distribution functions have the particu…

Steady-State Spread Bounds for Graph Diffusion via Laplacian Regularisation
Ardavan Rahimian
https://arxiv.org/abs/2510.04924 https://arxiv.org/pdf/2510.04…

Steady-State Spread Bounds for Graph Diffusion via Laplacian Regularisation
We study how far a diffusion process on a graph can drift from a designed starting pattern when that pattern is produced using Laplacian regularisation. Under standard stability conditions for undirected, entrywise nonnegative graphs, we give a closed-form, instance-specific upper bound on the steady-state spread, measured as the relative change between the final and initial profiles. The bound separates two effects: (i) an irreducible term determined by the graph's maximum node degree, and (ii…

Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactions
Yixiang Zhang, Xinhao Deng, Zhongyi Gu, Yihao Chen, Ke Xu, Qi Li, Jianping Wu
https://arxiv.org/abs/2510.07176

Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactions
Large Language Models (LLMs) are increasingly deployed as agents that orchestrate tasks and integrate external tools to execute complex workflows. We demonstrate that these interactive behaviors leave distinctive fingerprints in encrypted traffic exchanged between users and LLM agents. By analyzing traffic patterns associated with agent workflows and tool invocations, adversaries can infer agent activities, distinguish specific agents, and even profile sensitive user attributes. To highlight th…

Comparative study of Bayesian and Frequentist methods for epidemic forecasting: Insights from simulated and historical data
Hamed Karami, Ruiyan Luo, Pejman Sanaei, Gerardo Chowell
https://arxiv.org/abs/2509.05846

Comparative study of Bayesian and Frequentist methods for epidemic forecasting: Insights from simulated and historical data
Accurate epidemic forecasting is critical for effective public health interventions. This study compares Bayesian and Frequentist estimation frameworks within deterministic compartmental epidemic models, focusing on nonlinear least squares optimization versus Bayesian inference using MCMC sampling via Stan. We compare forecasting performance under shared modeling structure and error assumptions for specific implementations of both approaches. We assess performance on simulated datasets (with …

Embezzlement as a "Self-Test" for Infinite Copies of Entangled States
Li Liu
https://arxiv.org/abs/2509.05036 https://arxiv.org/pdf/2509.05036

Embezzlement as a "Self-Test" for Infinite Copies of Entangled States
We investigate the operator-algebraic structure underlying entanglement embezzlement, a phenomenon where a fixed entangled state (the catalyst) can be used to generate arbitrary target entangled states without being consumed. We show that the ability to embezzle a target state $g$ imposes strong internal constraints on the catalyst state $f$: specifically, $f$ must contain infinitely many mutually commuting, locally structured copies of $g$. This property is formalized using C*-algebraic tools …

Combining TSL and LLM to Automate REST API Testing: A Comparative Study
Thiago Barradas, Aline Paes, V\^ania de Oliveira Neves
https://arxiv.org/abs/2509.05540 https://

Combining TSL and LLM to Automate REST API Testing: A Comparative Study
The effective execution of tests for REST APIs remains a considerable challenge for development teams, driven by the inherent complexity of distributed systems, the multitude of possible scenarios, and the limited time available for test design. Exhaustive testing of all input combinations is impractical, often resulting in undetected failures, high manual effort, and limited test coverage. To address these issues, we introduce RestTSLLM, an approach that uses Test Specification Language (TSL) …