Tootfinder

Augmenting LLMs for General Time Series Understanding and Prediction
Felix Parker, Nimeesha Chan, Chi Zhang, Kimia Ghobadi
https://arxiv.org/abs/2510.01111 https://

Augmenting LLMs for General Time Series Understanding and Prediction
Time series data is fundamental to decision-making in many crucial domains including healthcare, finance, and environmental science. However, analyzing this data often requires incorporating unstructured contextual information, answering domain-specific questions, and generating natural language explanations -- capabilities that traditional time series models lack due to their inability to process text. While Large Language Models (LLMs) excel at contextual reasoning and knowledge integration, …

MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinement
Youpeng Li, Kartik Joshi, Xinda Wang, Eric Wong
https://arxiv.org/abs/2510.00317 h…

MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinement
The widespread adoption of open-source software (OSS) necessitates the mitigation of vulnerability risks. Most vulnerability detection (VD) methods are limited by inadequate contextual understanding, restrictive single-round interactions, and coarse-grained evaluations, resulting in undesired model performance and biased evaluation results. To address these challenges, we propose MAVUL, a novel multi-agent VD system that integrates contextual reasoning and interactive refinement. Specifically, …

Time-marching multi-level variational multiscale tensor decomposition algorithm for heat conduction with moving heat source
Xinyi Guan, Jiayi Hu, Lei Zhang, Shaoqiang Tang, Wing Kam Liu
https://arxiv.org/abs/2510.00516

Time-marching multi-level variational multiscale tensor decomposition algorithm for heat conduction with moving heat source
In this paper, we propose a time-marching multi-level Variational Multiscale-Tensor Decomposition (VMS-TD) algorithm to solve the heat equation with a moving heat source model that arises from additive manufacturing. First, we take a second-order centered difference for time semi-discretization. The temperature field is decomposed according to multiple space resolution levels, each represented by the TD method. Then we adopt the VMS formulation [T.J.R. Hughes, G.R. Feijoo, L. Mazzei, J.B. Quinc…

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
Miha O\v{z}bot, Igor \v{S}krjanc, Vitomir \v{S}truc
https://arxiv.org/abs/2510.00960 https://

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Transformer (Fuzzformer), a novel recurrent neural network architecture combined with multi-head self-attention and fuzzy inference systems to analyze multivariate stock market data and conduct long-term time series forecasting. The method leverages LSTM networks and temporal attention to condense multivariate data into i…

Given the remarkable #GreensSurge in members and polling, and the ongoing chaotic infighting at 'Your Party', it's maybe worth reposting my questioning of both leaderships on the eco-planetary crisis and overshoot.
As UK politics turns both right and left, how do we get degrowth onto the agenda? – degrowthUK

As UK politics turns both right and left, how do we get degrowth onto the agenda?
By Mark H Burton In the series, Prospects for Degrowth The last few weeks have seen two opposing developments in British politics, both in the context of the Starmer Labour government and its failu…

#Blakes7 Series D, Episode 08 - Games
AVON: Spare us the technical details, just give us the message.
ORAC: It offers visual contact. Do we accept?
TARRANT: Well since they obviously know us and we don't know them...
https://

Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning
Felix Parker, Nimeesha Chan, Chi Zhang, Kimia Ghobadi
https://arxiv.org/abs/2510.01116

Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning
Complex numerical time series analysis often demands multi-step reasoning capabilities beyond current models' reach. Tasks like medical diagnosis and weather forecasting require sequential reasoning processes -- including counterfactual analysis, logical deduction, knowledge application, and multi-modal contextual integration -- that existing time series models cannot explicitly perform. While recent research has shown large language models (LLMs) can achieve sophisticated Chain-of-Thought (CoT…

On the Birch and Swinnerton-Dyer formula modulo squares for certain quadratic twists of elliptic curves
Alexander J. Barrios, Chung Pang Mok
https://arxiv.org/abs/2510.00926 htt…

On the Birch and Swinnerton-Dyer formula modulo squares for certain quadratic twists of elliptic curves
Let $E/\mathbb{Q}$ be an elliptic curve with conductor $N=N_+N_-$, where $N_+$ and $N_-$ are coprime and $N_-$ is squarefree. Let $D$ be a positive fundamental discriminant satisfying the modified Heegner hypothesis with respect to $(N_+,N_-)$: primes dividing $N_+$ (resp. $N_-$) split (resp. are inert) in $\mathbb{Q}(\sqrt{D})$; we denote by $E^D/\mathbb{Q}$ the quadratic twist of $E/\mathbb{Q}$ by $D$. In the first half of the paper we consider the situation where $N_-$ is a squarefree produc…

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COM-BOM: Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier
Gaoxiang Luo, Aryan Deshwal
https://arxiv.org/abs/2510.01178 https://…

COM-BOM: Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier
Selecting an optimal set of exemplars is critical for good performance of in-context learning. However, prior exemplar search methods narrowly optimize for predictive accuracy, critically neglecting model calibration--a key determinant of trustworthiness and safe deployment. In this paper, we formulate exemplar selection as a multi-objective optimization problem, explicitly targeting both the maximization of predictive accuracy and the minimization of expected calibration error. We solve this p…