Sael is a research-focused project that explores how conversational AI systems can move beyond purely informational responses toward emotionally aware, personality-consistent, and memory-driven interactions.
The goal of this project is to investigate how emotion modeling, long-term memory, and personality constraints can be integrated into a large language model based system to create more human-like, coherent, and emotionally appropriate conversations over long time spans.
Most modern conversational AI systems are optimized for:
- Answering questions
- Following instructions
- Producing correct information
However, they are not designed to maintain emotional continuity, personality consistency, or long-term relational context.
Human conversation is not only about information exchange — it is also about:
- Emotion
- Tone
- Memory
- Relationship continuity
Sael is an attempt to study and prototype systems that operate in this space.
This project aims to explore:
- How can an AI system model and respond to user emotions in a consistent way?
- How can long-term and short-term memory be structured for conversational continuity?
- How can a stable personality layer be imposed on top of a generative model?
- How can responses be shaped not only by what is said, but by how it should be said?
- Emotion-Aware Response Generation
- Hybrid Memory System (Short-term + Long-term)
- Personality-Constrained Decoding Layer
- Paralinguistic Expression (tone, pacing, softness, etc.)
- Long-Horizon Conversational Consistency
User Input
↓
Emotion & Intent Analyzer
↓
Context + Memory Retrieval
↓
LLM Core
↓
Personality & Tone Shaping Layer
↓
Response (Text / Voice)