Building at the frontier of agentic AI

Anthropic's massive valuation and revenue growth signal a shift toward enterprise dominance. Builders should expect aggressive expansion of the Claude ecosystem and API capabilities.

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.

A Survey of Large Language Models

This survey establishes a comprehensive taxonomy of large language model development, systematizing technical advancements across pre-training, adaptation, and utilization to create a foundational reference framework for the field. Garnering 1394 citations, it provides researchers and practitioners with structured guidance for navigating rapid architectural and methodological evolution while standardizing terminology and evaluation approaches. The work serves as a definitive roadmap that has shaped subsequent research by clarifying capabilities, limitations, and critical technical trade-offs in modern language AI systems.