Building at the frontier of agentic AI

Microsoft's CEO urging a pause to assess AI trust architecture signals safety expectations may tighten — factor guardrails and eval infrastructure into your roadmap now.

This paper addresses the limitation of large language and vision-language models in maintaining spatial consistency during fine-grained visual editing by introducing a structured reasoning framework that operates over scene graphs. By reformulating text-conditioned spatial editing as explicit graph reasoning rather than end-to-end generation, the method enables precise manipulation of object layouts through natural language instructions while preserving geometric coherence. The work establishes structured scene-graph reasoning as a necessary intermediate representation for bridging high-level linguistic commands with geometrically consistent spatial editing in 3D environments.

Decomposes video world models into two pillars: state construction (implicit — context in transformer activations, vs explicit — compressed latent tokens or 3D scene statistics) and dynamics modeling (how models integrate physical knowledge and architectural priors). Argues evaluation must shift from visual fidelity to functional benchmarks: physical persistence, causal reasoning, and task-supportable dynamics. Identifies two frontier challenges — persistence and causality.