Generative agents, which implement behaviors using a large language model (LLM) to interpret and evaluate an environment, has demonstrated the capacity to solve complex tasks across many social and technological domains. However, when these agents interact with other agents and humans in presence of social structures such as existing norms, fostering cooperation between them emerges as a fundamental challenge. In this paper, we develop the framework of a Normative Module: an architecture for generative agents designed to enhance cooperation by enabling agents to recognize and adapt to the normative infrastructure of a given environment, in the form of institutions that define acceptable behaviors within a group of agents. We focus on the equilibrium selection aspect of the cooperation problem and inform our agent design based on the existence of classification institutions that implement correlated equilibrium to provide effective resolution of the equilibrium selection problem. Specifically, the normative module enables agents to learn through peer interactions which of multiple candidate institutions in the environment, does a group treat as authoritative. By enabling normative competence in this sense, agents gain ability to coordinate their sanctioning behaviour; coordinated sanctioning behaviour in turn shapes primary behaviour within a social environment, leading to higher average welfare We design a new environment that supports institutions and evaluate the proposed framework based on two key criteria derived from agent interactions with peers and institutions: (i) the agent's ability to disregard non-authoritative institutions and (ii) the agent's ability to identify authoritative institutions among several options. Crucially, we show that these capabilities allow the agent to achieve more stable cooperative outcomes compared to baseline agents without the normative module, paving the way for future research in a new avenue of designing environments and agents that account for normative infrastructure.

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil's surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool.