Update README.md

README.md

@@ -481,4 +481,16 @@ This model builds upon [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2
 
 Performance analysis across four ambiguity levels shows that QueryRefiner-0.5B-v0.1-GRPO sustains near-saturated accuracy for domain and intent identification (~99%) and achieves perfect structural and ambiguity assessment at the highest ambiguity tier. These results confirm that the model effectively internalized the XML schema and ambiguity-recognition criteria learned during GRPO pretraining.
 
-More nuanced metrics reveal moderate, expected declines in rephrasing quality (from 90.8 % to 87.5 %) and intent preservation (from ~96 % to 92.8 %) as ambiguity increases, indicating that the model’s semantic generation remains robust but not immune to underspecified inputs. The consistent yet non-flat degradation, coupled with realistic standard-deviation ranges, suggests healthy generalization rather than overfitting: the model adapts sensibly to rising query uncertainty while preserving adherence to output-format and guideline constraints.
+More nuanced metrics reveal moderate, expected declines in rephrasing quality (from 90.8 % to 87.5 %) and intent preservation (from ~96 % to 92.8 %) as ambiguity increases, indicating that the model’s semantic generation remains robust but not immune to underspecified inputs. The consistent yet non-flat degradation, coupled with realistic standard-deviation ranges, suggests healthy generalization rather than overfitting: the model adapts sensibly to rising query uncertainty while preserving adherence to output-format and guideline constraints.
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+### Discussion of Results
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+The evaluation results reveal a clear task difficulty hierarchy that aligns with the inherent complexity of each component. Structural and classification metrics (domain accuracy, intent accuracy, ambiguity assessment) achieve 98-99% performance, while the generative rephrasing task scores lower at ~90%. This gap reflects the fundamental difference in task complexity rather than a training deficiency.
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+Domain and intent classification are essentially pattern recognition tasks where the model must map queries to learned categories—a task well-suited to the model's 0.5B parameter capacity. Similarly, ambiguity assessment and guideline adherence involve rule-following and structural analysis, which the three-phase training procedure was explicitly designed to optimize.
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+Rephrasing quality, by contrast, requires the model to make nuanced judgments about when to intervene (versus preserving already-optimal queries), how to balance specificity against over-constraint, and whether to expand acronyms or add disambiguating context. These decisions demand deeper semantic understanding and generation capabilities that push the limits of a 0.5B model. The 90.33% score with 17.61% standard deviation represents strong performance on this challenging task, particularly given the diversity of the test set.
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+The per-ambiguity breakdown supports this interpretation: rephrasing quality remains relatively stable across ambiguity levels (87.5-90.8%), with the expected slight degradation on highly ambiguous queries. This consistency across query types suggests the model has learned generalizable rephrasing strategies rather than memorizing domain-specific patterns. The ~18% standard deviation across all metrics indicates natural variance in query difficulty rather than systematic failures on particular query types.
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+For production deployments, users should anticipate that the model will perform most reliably on structural conformance and classification tasks, while rephrasing decisions may occasionally require human review, particularly for edge cases or highly specialized domains underrepresented in the training data.