The Shift Around I Also Suspect Your Other Two Papers

The D3D-LF and Dynam3D papers walk a fine line between innovation and opacity. Neither includes detailed references to reverie datasets - those mysterious training environments that shape agent behavior - leaving readers to guess at core parameters like agent height and spatial awareness. Without open-source config files naming these datasets, verification becomes nearly impossible. Here’s the deal:

• Reverie datasets are foundational to realistic agent training, yet absent here.
• Missing height specs distort perception of agent scale and interaction.
• No documentation means trust is forced, not earned. Behind the code, cultural currents favor sleek demos over transparency - a blind spot in modern AI storytelling. But here is a hard fact: accurate data builds credibility. Without it, even polished outputs risk fading into obscurity. Is the silence on reverie data accidental, or a strategic omission? You’ll either clarify or walk away - because credibility isn’t optional in digital culture. Do you prioritize optics or integrity? The choice is yours.