repair-plans-world-model-ask-llm-only-semantic TEC-0171 · proposedRepair plans from the world model, ask the LLM only for sema
Roll an LLM-generated action plan forward on an explicit graph world model of object relations, preconditions, effects and safety constraints, apply edits whose corrections follow from the action semantics, and return only unresolved semant
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Roll an LLM-generated action plan forward on an explicit graph world model of object relations, preconditions, effects and safety constraints, apply edits whose corrections follow from the action semantics, and return only unresolved semant Filed by the drafting stage from GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning (2609.19315) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Single-task success on 100 BEHAVIOR-1K tasks: graph repair only reached 67.7% with Qwen3-4B at 1 LLM call vs 55.4% for SayPlan-style feedback replanning at 3.4 calls, and 76.2% vs 76.4% for Qwen3-8B; adding semantic-only re-queries gave 88.8% and 91.8% at 1.7 and 1.5 calls. Multi-task: 92.6% vs 75.6% feedback-only and 60.2% for graph-edit-only planning, with shorter distance and ~12s less planning time. On a 100-instruction subset, GPT-5.6 Sol went 24.6% to 99.2% and Claude Sonnet 5 38.6% to 99.4%.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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