AIBearisharXiv – CS AI · May 117/10
🧠Researchers introduce the Adversarial Empathy Benchmark (AEB) to test whether RL-trained empathetic language models remain robust against adversarial user tactics like gaslighting and emotional manipulation. While RLVER-trained models significantly outperform baselines in empathetic responsiveness, a new metric (ECS) reveals they excel at behavioral responsiveness without demonstrating genuine emotional state tracking, raising questions about the depth of empathetic AI capabilities.
AIBullisharXiv – CS AI · Mar 177/10
🧠Researchers propose Emotional Cost Functions, a new AI safety framework that teaches agents to develop qualitative suffering states rather than numerical penalties to learn from mistakes. The system uses narrative representations of irreversible consequences that reshape agent character, showing 90-100% accuracy in decision-making compared to 90% over-refusal rates in numerical baselines.
AINeutralarXiv – CS AI · Jun 236/10
🧠EmoInstruct-TTS introduces a dual-path framework for emotional speech synthesis that enables fine-grained emotional control through natural language instructions. The system uses Emotion2embed, covering 48 emotional states, and an Instruction-Conditioned Emotion Flow Model to convert free-form text instructions into acoustically grounded emotion representations integrated with LLM-based synthesis pipelines.
AINeutralarXiv – CS AI · Jun 106/10
🧠Researchers propose Self-EmoQ, an emotion-planning framework that determines emotional context before text generation to improve streaming emotional text-to-speech synthesis. The system uses reinforcement learning with Plutchik's emotion theory and demonstrates superior performance on multiple dialogue datasets, with a functional real-time deployment pipeline.
AIBullisharXiv – CS AI · Mar 27/1012
🧠Researchers have introduced Hello-Chat, an end-to-end audio language model designed to create more realistic and emotionally resonant AI conversations. The model addresses the robotic nature of existing Large Audio Language Models by using real-life conversation data and achieving breakthrough performance in prosodic naturalness and emotional alignment.
AINeutralOpenAI News · Mar 215/105
🧠OpenAI and MIT Media Lab have announced a research collaboration focused on developing early methods for studying affective use and emotional well-being in ChatGPT interactions. This partnership aims to better understand how users emotionally engage with AI systems and the psychological impacts of AI conversations.
AINeutralThe Verge – AI · Mar 155/10
🧠AI companies are recruiting improv actors through companies like Handshake AI to train AI models on human emotion and authentic character portrayal. This represents a growing trend of AI labs seeking increasingly specialized training data to improve their models' emotional intelligence and human-like responses.
🏢 OpenAI
