Is there a plan to reduce hallucinations?

One of the critical challenges in using LLMs for scientific research is hallucination (e.g., fabricating citations, misinterpreting experimental results, or generating non-functional code). In a research context, accuracy and reproducibility are paramount, so this is likely a primary bottleneck for adoption. I was wondering if there is a current roadmap or specific strategies planned to address this? For example: Verification Steps: Implementing self-correction loops or external tool-based verification (e.g., running code to check outputs). RAG & Grounding: Enhancing retrieval mechanisms to ensure claims are strictly grounded in provided papers/data. Human-in-the-loop: Designing workflows that explicitly flag low-confidence steps for human review. I'd love to hear your thoughts on how the project plans to tackle reliability. If this is a priority area, I (and likely others) would be happy to help explore solutions or contribute to related modules.