Psychologically Enhanced AI Agents

We introduce MBTI-in-Thoughts, a framework for enhancing the effectiveness of Large Language Model (LLM) agents through psychologically grounded personality conditioning. Drawing on the Myers-Briggs Type Indicator (MBTI), our method primes agents with distinct personality archetypes via prompt engineering, enabling control over behavior along two foundational axes of human psychology, cognition and affect. We show that such personality priming yields consistent, interpretable behavioral biases across diverse tasks: emotionally expressive agents excel in narrative generation, while analytically primed agents adopt more stable strategies in game-theoretic settings. Our framework supports experimenting with structured multi-agent communication protocols and reveals that self-reflection prior to interaction improves cooperation and reasoning quality. To ensure trait persistence, we integrate the official 16Personalities test for automated verification. While our focus is on MBTI, we show that our approach generalizes seamlessly to other psychological frameworks such as Big Five, HEXACO, or Enneagram. By bridging psychological theory and LLM behavior design, we establish a foundation for psychologically enhanced AI agents without any fine-tuning.

Deep Learning Approaches on Image Captioning: A Review

Automatic image captioning, which involves describing the contents of an image, is a challenging problem with many applications in various research fields. One notable example is designing assistants for the visually impaired. Recently, there have been significant advances in image captioning methods owing to the breakthroughs in deep learning. This survey paper aims to provide a structured review of recent image captioning techniques, and their performance, focusing mainly on deep learning methods. We also review widely-used datasets and performance metrics, in addition to the discussions on open problems and unsolved challenges in image captioning.