Reinforcement learning has recently gained traction as a means to improve combinatorial optimization methods, yet its effectiveness within local search metaheuristics specifically remains comparatively underexamined. In this study, we evaluate a range of reinforcement learning-based neighborhood selection strategies — multi-armed bandits (upper confidence bound, $ε$-greedy) and deep reinforcement learning methods (proximal policy optimization, double deep $Q$-network) — and compare them against multiple baselines across three different problems: the traveling salesman problem, the pickup and delivery problem with time […]

Ver mais

arXiv:2602.15187v1 Announce Type: new Abstract: We propose GRAM-DIFF, a Gram-matrix-guided diffusion framework for semi-blind multiple input multiple output (MIMO) channel estimation. Recent diffusion-based estimators leverage learned generative priors to improve pilot-based channel estimation; but they do not exploit second-order structural information estimated from data symbols. In practical systems, the channel Gram matrix can be estimated from received symbols and it provides realization-level information about channel subspace structure. The proposed method integrates a pre-trained angular-domain diffusion prior with two […]

Ver mais

Over the last ten years, running has become a huge part of my life. I’m not sure how useful this write-up will be to others, but running preoccupies me enough that I feel the need to document the journey and externalize my thoughts. On 14.02.2026 (Valentine’s day) I ran my longest trail race: Trail du Mont Agel in Monaco. I expected a picturesque and exciting, yet challenging, adventure. I prepared for it by running 20–30 km every week […]

Ver mais

arXiv:2602.10147v1 Announce Type: new Abstract: The use of Large Language Models (LLMs) has drawn growing interest within the scientific community. LLMs can handle large volumes of textual data and support methods for evidence synthesis. Although recent studies highlight the potential of LLMs to accelerate screening and data extraction steps in systematic reviews, detailed reports of their practical application throughout the entire process remain scarce. This paper presents an experience report on the conduction of a systematic mapping study […]

Ver mais

arXiv:2601.09713v1 Announce Type: new Abstract: Proactively predicting a users next utterance in human-machine dialogue can streamline interaction and improve user experience. Existing commercial API-based solutions are subject to privacy concerns while deploying general-purpose LLMs locally remains computationally expensive. As such, training a compact, task-specific LLM provides a practical alternative. Although user simulator methods can predict a user’s next utterance, they mainly imitate their speaking style rather than advancing the dialogue. Preference data synthesis has been investigated to generate […]

Ver mais

Hey all. Just sharing a project I have been working on for the past two months. This one is about finetuning text-only language models to become vision language models (VLMs). Code is open source (repo below). Sharing a YouTube tutorial + results too, for those who are interested. Heres my full roadmap for future ML devs walking this path: – used 50k images from the conceptual captions dataset – VIT-base encoder for backbone, this remained frozen – Trained […]

Ver mais

arXiv:2602.21446v1 Announce Type: new Abstract: Hyperdimensional Computing (HDC) offers a computationally efficient paradigm for neuromorphic learning. Yet, it lacks rigorous uncertainty quantification, leading to open decision boundaries and, consequently, vulnerability to outliers, adversarial perturbations, and out-of-distribution inputs. To address these limitations, we introduce ConformalHDC, a unified framework that combines the statistical guarantees of conformal prediction with the computational efficiency of HDC. For this framework, we propose two complementary variations. First, the set-valued formulation provides finite-sample, distribution-free coverage guarantees. […]

Ver mais

arXiv:2407.11518v2 Announce Type: replace Abstract: In this paper, we present a new ensemble-based filter method by reconstructing the analysis step of the particle filter through a transport map, which directly transports prior particles to posterior particles. The transport map is constructed through an optimization problem described by the Maximum Mean Discrepancy loss function, which matches the expectation information of the approximated posterior and reference posterior. The proposed method inherits the accurate estimation of the posterior distribution from particle […]

Ver mais

arXiv:2602.03981v1 Announce Type: new Abstract: Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a shock to one token may result in significant and uncontrolled contagion effects. As the DeFi ecosystem becomes increasingly linked with traditional financial infrastructure through instruments, such as stablecoins, the risk posed by this dynamic demands more powerful quantification tools. We introduce DeXposure-FM, the first time-series, graph foundation model for measuring and forecasting inter-protocol […]

Ver mais