Some seemingly simple sequences of multiplication and addition grow so quickly that they question the very foundations of mathematics. Did you hear the one about the man who invented chess and got himself executed? Legend has it that a man called Sessa, who lived in India long ago, developed the rules for the game and presented them to a king. The king was delighted and offered the man his pick of reward. Sessa asked for a supposedly humble quantity of rice.

This limits the applicability and generalization of off-the-shelf models to diverse real-world applications.

A common framework consists in modeling the range of tasks the agent may encounter as a distribution over all possible tasks.

The emergence of Large Language Models (LLMs) with increasingly sophisticated natural language understanding and generative capabilities has sparked interest in the Agent-based Modelling (ABM) community. With their ability to summarize, generate, analyze, categorize, transcribe and translate text, answer questions, propose explanations, sustain dialogue, extract information from unstructured text, and perform logical reasoning and problem-solving tasks, LLMs have a good potential to contribute to the modelling process. After reviewing the current use of LLMs in ABM, this study reflects on the opportunities and challenges of the potential use of LLMs in ABM. It does so by following the modelling cycle, from problem formulation to documentation and communication of model results, and holding a critical stance.

Abstract--Multivariate time series forecasting (MTSF) is a critical task with broad applications in domains such as meteorology, transportation, and economics. Nevertheless, pervasive missing values caused by sensor failures or human errors significantly degrade forecasting accuracy . Prior efforts usually employ an impute-then-forecast paradigm, leading to suboptimal predictions due to error accumulation and misaligned objectives between the two stages. T o address this challenge, we propose the Collaborative Imputation-Forecasting Network (CoIFNet), a novel framework that unifies imputation and forecasting to achieve robust MTSF in the presence of missing values. Specifically, CoIFNet takes the observed values, mask matrix and timestamp embeddings as input, processing them sequentially through the Cross-Timestep Fusion (CTF) and Cross-Variate Fusion (CVF) modules to capture temporal dependencies that are robust to missing values. We provide theoretical justifications on how our CoIFNet learning objective improves the performance bound of MTSF with missing values. Through extensive experiments on challenging MSTF benchmarks, we demonstrate the effectiveness and computational efficiency of our proposed approach across diverse missing-data scenarios, e.g., CoIFNet outperforms the state-of-the-art method by 24.40 % ( 23.81 %) at a point (block) missing rate of 0.6, while improving memory and time efficiency by Our code is available at: https://github.com/KaiT By modeling complex temporal dependencies and inter-variate correlations, advanced MTSF methods [4], [5], [6] have demonstrated significant capabilities in predicting critical metrics and anticipating future trends.

This paper presents the in vivo validation of an inductive wireless power transfer (WPT) system integrated for the first time into a magnetically controlled robotic capsule endoscopy platform. The proposed system enables continuous power delivery to the capsule without the need for onboard batteries, thus extending operational time and reducing size constraints. The WPT system operates through a resonant inductive coupling mechanism, based on a transmitting coil mounted on the end effector of a robotic arm that also houses an external permanent magnet and a localization coil for precise capsule manipulation. To ensure robust and stable power transmission in the presence of coil misalignment and rotation, a 3D receiving coil is integrated within the capsule. Additionally, a closed-loop adaptive control system, based on load-shift keying (LSK) modulation, dynamically adjusts the transmitted power to optimize efficiency while maintaining compliance with specific absorption rate (SAR) safety limits. The system has been extensively characterized in laboratory settings and validated through in vivo experiments using a porcine model, demonstrating reliable power transfer and effective robotic navigation in realistic gastrointestinal conditions: the average received power was 110 mW at a distance of 9 cm between the coils, with variable capsule rotation angles. The results confirm the feasibility of the proposed WPT approach for autonomous, battery-free robotic capsule endoscopy, paving the way for enhanced diagnostic in gastrointestinal medicine.