Achieving socially compatible human-AI interaction requires systems that can interpret and respond to human emotions appropriately in complex social environments. While traditional emotion recognition models rely heavily on facial or bodily expressions, a growing body of research demonstrates that such cues are insufficient without the dynamic, multimodal contextual cues. Positioned at the intersection of cognitive psychology and AI, this work identifies three essential qualities for context-sensitive emotion recognition (CSER): generalizability to unseen scenarios, data efficiency in adapting to new contexts, and reliability in predictive performance across contexts. We outline a research plan that systematically investigates the role of contextual factors, domain adaptation, and uncertainty quantification in building CSER models capable of robust performance across real-world settings. Our approach integrates computational rigour with ethical responsibility to lay the foundation for next-generation emotion-aware systems that are not only accurate but also trustworthy, transparent, and support human well-being in digital interactions. ... Read more

Aligning diffusion models to downstream tasks often requires finetuning new models or gradient-based guidance at inference time to enable sampling from the reward-tilted poste-rior. In this work, we explore a simple inference-time gradient-free guidance approach, called controlled denoising (CoDe), that circumvents the need for differentiable guidance functions and model finetuning. CoDe is a blockwise sampling method applied during intermediate denoising steps, allowing for alignment with downstream rewards. Our experiments demon-strate that, despite its simplicity, CoDe offers a favorable trade-off between reward alignment, prompt instruction following, and inference cost, achieving a competitive performance against the state-of-the-art baselines. Our code is available at: https://github.com/anujinho/code. ... Read more