Accumulating a life history is a valuable resource for understanding self and reflecting on personal historical experience, which could be developed through diary writing. To facilitate the recording of past events in a diary, we designed and implemented Rebulb, a system that enables users to engage with reflective questions about proud moments and document memories evoked for accumulating one's life history. Our four-month field study with three participants showed that users intentionally and spontaneously recalled vague and wide memories during their daily activity and then concretized these memories by writing them down in a journal. The study also revealed that regardless of whether the memories were positive or negative, the current state of the user played a crucial role in how these memories were processed and reflected upon. Our finding imply consideration in designing a tool for supporting the recall and documentation of past experiences.

Reinforcement learning (RL) agents have demonstrated strong performance in structured environments, yet they continue to struggle in real-world settings where goals are ambiguous, conditions change dynamically, and external supervision is limited. These challenges stem not primarily from the algorithmic limitations but from the characteristics of conventional training environments, which are usually static, task-specific, and externally defined. In contrast, biological agents develop autonomy and adaptivity by interacting with complex, dynamic environments, where most behaviors are ultimately driven by internal physiological needs. Inspired by these biological constraints, we introduce EVAAA (Essential Variables in Autonomous and Adaptive Agents), a 3D virtual environment for training and evaluating egocentric RL agents endowed with internal physiological state variables. In EVAAA, agents must maintain essential variables (EVs)—e.g., satiation, hydration, body temperature, and tissue integrity (the level of damage)—within viable bounds by interacting with environments that increase in difficulty at each stage. The reward system is derived from internal state dynamics, enabling agents to generate goals autonomously without manually engineered, task-specific reward functions. Built on Unity ML-Agents, EVAAA supports multimodal sensory inputs, including vision, olfaction, thermoception, collision, as well as egocentric embodiment. It features naturalistic survival environments for curricular training and a suite of unseen experimental testbeds, allowing for the evaluation of autonomous and adaptive behaviors that emerge from the interplay between internal state dynamics and environmental constraints. By integrating physiological regulation, embodiment, continual learning, and generalization, EVAAA offers a biologically inspired benchmark for studying autonomy, adaptivity, and internally driven control in RL agents. Our code is publicly available at https://github.com/cocoanlab/evaaa

Capturing people’s spontaneous thoughts or daily emotions in their everyday living space can be difficult and can pose a psychological burden from a clinical standpoint. To address this issue, we have developed EmotionFrame, which allows users to archive their daily experiences by recording videos of their current thoughts and emotions at home. EmotionFrame serves as a journaling device, and the parameters for journaling have been determined based on a depression assessment model. This study explores the benefits of users’ recording feelings about their daily events, which can also help with assessing depressive mood with the journaling data. Our three-week in-field study with eight participants showed that EmotionFrame could be integrated into participants’ daily routines based on their previous diary-writing habits. In addition, its harmonious frame shape fits in with the everyday environment. Being able to engage in spontaneous journaling in private spaces – and in a way that allowed them to see themselves – prompted the study participants to effectively record their sincere thoughts and emotions during the archiving process.