Open-weight large language models create demand for weight protection schemes that let users run models locally while preserving developer control over valuable model weights. Existing schemes, however, are often justified by heuristic arguments and empirical checks rather than explicit adversarial goals, security definitions, or proofs. As a case study, we analyze a recent scheme previously published in *EMNLP 2024 Main Conference* and show that it is vulnerable to our efficient weight-extraction attack, recovering more than 99.95% of protected parameters and producing reconstructed models with near-identical performance. Motivated by this failure, we formulate LLM weight protection schemes through game-based definitions, and we discuss how these notions relate. We encourage that future weight protection schemes should be designed with formal security goals, proof-oriented analysis, and offensive evaluation rather than relying on informal intuition alone.

Text-driven indoor scene generation and editing require an intermediate representation that language models can both produce and revise. Existing LLM-based systems often rely on scene graphs or global constraint lists, which are compact but underspecify local geometry and make instruction-based edits difficult to localize. We frame the task as structured program generation and local program repair, and propose the Hierarchical Descriptive Scene Language (HDSL), an XML/CSS-style domain-specific language in which rooms, regions, objects, and support surfaces form a tree with parent-relative local coordinates. Our pipeline generates HDSL subtrees recursively with bounded verification, grounds non-virtual nodes through multimodal asset retrieval, and applies force-directed layout optimization to repair boundary and collision errors. For editing, Hierarchical Retrieval-Augmented Generation retrieves the relevant subtree, rewrites only that local context, and merges the result back through a deterministic three-way merge. In our reproduced benchmark, HDSL improves average object coverage, text-scene alignment, and generation time over full text-to-scene baselines while remaining competitive with layout-only reproductions on geometry metrics. On thirty paired editing instructions, HRAG reduces token use by 67.6% and runtime by 68.1%, and cuts non-target removals by 87.5% over full-scene rewriting; attribute ablations confirm that local coordinates and virtual/leaf flags are necessary for valid, locality-preserving edits.