Research at DreamLabs
Our research advances the frontier of interactive intelligence through foundational work in AI systems, model architectures, and human-AI interaction. We publish our findings, open-source key components, and collaborate with the broader AI research community.
Publications
→ Turn-Level Metadata Extraction for Conversational Agents
A paired-LLM approach for real-time metadata extraction to enable embodied, context-aware conversational experiences.
Active Research Areas
→ Foundation Models
Developing next-generation large language models with enhanced reasoning, contextual understanding, and task generalization. Our models are designed to be more capable, efficient, and aligned with human intentions.
→ Multimodal Intelligence
Building systems that seamlessly integrate multiple modalities: text, vision, audio, and beyond. We're exploring how AI can understand and generate across different forms of information to enable richer interactions.
→ Interactive Learning
Research into how AI systems can learn from interaction, adapt to user preferences, and improve through feedback. We're developing techniques for more natural, dynamic human-AI collaboration.
→ AI Safety & Alignment
Ensuring our models are reliable, truthful, and aligned with human values. This includes work on interpretability, robustness, reducing hallucinations, and developing better evaluation frameworks.
→ Efficient Inference
Optimizing model architectures and inference algorithms to make powerful AI accessible on a wide range of devices and environments. Democratizing access requires efficiency at every level.
Open Research
We believe in transparency and collaboration. Select research outputs, model weights, and tools are made available to the community to accelerate progress in AI research and enable broader experimentation.
→ Research papers and preprints coming soon
→ Model releases planned for Q2 2026
→ Open-source tooling in active development