Update LLM-BEM-Engineer_Benchmark/README.md

LLM-BEM-Engineer_Benchmark/README.md

@@ -101,14 +101,14 @@ Each file name ends with a numeric suffix indicating a **distinct robustness tes
 
 ### Ambiguity, User Intent, and Modeling Precision
 
-When users aim to obtain more specific or accurate building energy models, they are expected to explicitly provide key modeling information, such as: number of thermal zones per story, space type definitions, and system details. Providing such information improves modeling precision and helps reduce model hallucination. This mirrors human communication: even in expert-to-expert interactions, clear and explicit specifications are required to produce accurate technical outputs.
+When users aim to obtain more specific or accurate building energy models, they are expected to explicitly provide key modeling information, such as: building geometry, number of thermal zones per story, space type definitions, and system details. Providing such information improves modeling precision and helps reduce model hallucination. This mirrors human communication: even in expert-to-expert interactions, clear and explicit specifications are required to produce accurate technical outputs.
 
 In cases where user intent is ambiguous or underspecified, the generated building model inevitably reflects the LLM’s own interpretation of the input. As a result, the output represents the closest plausible model inferred from the provided intent, rather than a uniquely determined solution.
 
-### Multi-Round Robust Inference Mechanism
+<!-- ### Multi-Round Robust Inference Mechanism
 
 Because ambiguous user intent must be interpreted by LLMs themselves, this benchmark incorporates a multi-round try to improve robustness as LLMs progressively better understand user intent, ensuring that the generated models converge toward the intended user requirements.
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+ -->
 
 ## 🎯 Benchmark Objectives