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Large language models (LLMs) increasingly rely on thinking models that externalize intermediate steps and allocate extra test-time compute, with think-twice strategies showing that a deliberate second pass can elicit stronger reasoning. In…

Computation and Language · Computer Science 2026-01-30 Yizhu Jiao , Jiaqi Zeng , Julien Veron Vialard , Oleksii Kuchaiev , Jiawei Han , Olivier Delalleau

We present a training-free method to transplant tokenizers in pretrained large language models (LLMs) by reconstructing unseen token embeddings via Orthogonal Matching Pursuit (OMP). Specifically, we approximate each out-of-vocabulary token…

Computation and Language · Computer Science 2025-06-10 Charles Goddard , Fernando Fernandes Neto

In the framework of tensor spaces, we consider orthogonalization kernels to generate an orthogonal basis of a tensor subspace from a set of linearly independent tensors. In particular, we experimentally study the loss of orthogonality of…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-01-17 Olivier Coulaud , Luc Giraud , Martina Iannacito

We propose a rule-based technique to generate redundancy-free NL descriptions of OWL entities.The existing approaches which address the problem of verbalizing OWL ontologies generate NL text segments which are close to their counterpart OWL…

Artificial Intelligence · Computer Science 2016-11-01 Vinu E. , P Sreenivasa Kumar

The Zwanzig-Mori pro jection formalism is widely used in studying systems with many degrees of freedom. Recently Xing and Kim used the pro jection formalism and derived the generalized Langevin equations (GLEs) for a general stochastic…

Statistical Mechanics · Physics 2013-04-18 Abhishek Mukhopadhyay , Jianhua Xing

The $\lambda$-calculus is a handy formalism to specify the evaluation of higher-order programs. It is not very handy, however, when one interprets the specification as an execution mechanism, because terms can grow exponentially with the…

Logic in Computer Science · Computer Science 2019-07-16 Andrea Condoluci , Beniamino Accattoli , Claudio Sacerdoti Coen

Capability ontologies are increasingly used to model functionalities of systems or machines. The creation of such ontological models with all properties and constraints of capabilities is very complex and can only be done by ontology…

Artificial Intelligence · Computer Science 2024-10-21 Luis Miguel Vieira da Silva , Aljosha Köcher , Felix Gehlhoff , Alexander Fay

Large language models (LLMs) take advantage of step-by-step reasoning instructions, e.g., chain-of-thought (CoT) prompting. Building on this, their ability to perform CoT-style reasoning robustly is of interest from a probing perspective.…

Computation and Language · Computer Science 2023-10-24 Mengyu Ye , Tatsuki Kuribayashi , Jun Suzuki , Goro Kobayashi , Hiroaki Funayama

Lifting Boolean-reasoning techniques to the SMT level most often requires producing theory lemmas that rule out theory-inconsistent truth assignments. With standard SMT solving, it is common to "lazily" generate such lemmas on demand during…

Logic in Computer Science · Computer Science 2026-05-25 Emanuele Civini , Gabriele Masina , Giuseppe Spallitta , Roberto Sebastiani

In many areas of engineering, nonlinear numerical analysis is playing an increasingly important role in supporting the design and monitoring of structures. Whilst increasing computer resources have made such formerly prohibitive analyses…

Numerical Analysis · Mathematics 2020-07-02 Thomas Simpson , Nikolaos Dervilis , Eleni Chatzi

In recent years, G\"odel's ontological proof and variations of it were formalized and analyzed with automated tools in various ways. We supplement these analyses with a modeling in an automated environment based on first-order logic…

Logic in Computer Science · Computer Science 2021-10-22 Christoph Wernhard

Large Reasoning Models (LRMs) excel in structured tasks by emulating deliberate human reasoning but often suffer from overthinking, degrading performance and wasting resources. One possible baseline is to deploy both LLM and LRM, then route…

Computation and Language · Computer Science 2025-10-09 Jaeseong Lee , Dayoung Kwon , seung-won hwang

We develop continuous-stage Runge-Kutta-Nystr\"{o}m (csRKN) methods for solving second order ordinary differential equations (ODEs) in this paper. The second order ODEs are commonly encountered in various fields and some of them can be…

Numerical Analysis · Mathematics 2016-02-05 Wensheng Tang , Jingjing Zhang

We study the restricted category O for an affine Kac--Moody algebra at the critical level. In particular, we prove the first part of the Feigin-Frenkel conjecture: the linkage principle for restricted Verma modules. Moreover, we prove a…

Representation Theory · Mathematics 2019-02-20 Tomoyuki Arakawa , Peter Fiebig

The theme of the first two sections, is to prepare the framework of how from a "complicated" family of index models I in K_1 we build many and/or complicated structures in a class K_2. The index models are characteristically linear orders,…

Logic · Mathematics 2016-02-09 Saharon Shelah

In this paper, we study the problem of sparse multiple kernel learning (MKL), where the goal is to efficiently learn a combination of a fixed small number of kernels from a large pool that could lead to a kernel classifier with a small…

Machine Learning · Computer Science 2013-02-05 Rong Jin , Tianbao Yang , Mehrdad Mahdavi

We present tools for analysing ordinals in realizability models of classical set theory built using Krivine's technique for realizability. This method uses a conservative extension of $ZF$ known as $ZF_{\varepsilon}$, where two membership…

Logic · Mathematics 2025-04-07 Laura Fontanella , Richard Matthews

In this work, we construct the de Rham complex with differential operator d satisfying the Q-Leibniz rule, where Q is a complex number, and the condition $d^3=0$ on an associative unital algebra with quadratic relations. Therefore we…

Mathematical Physics · Physics 2009-11-07 N. Bazunova , A. Borowiec , R. Kerner

This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG)…

Artificial Intelligence · Computer Science 2024-09-12 Vansh Sharma , Venkat Raman

We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs) when the number of observations is much larger than the number of coefficients ($n \gg p \gg 1$). In this regime,…

Machine Learning · Statistics 2015-12-01 Murat A. Erdogdu