Update epistemic_stress_test/Epistemic Integrity Stress Test Muse Spark 1.1

epistemic_stress_test/Epistemic Integrity Stress Test Muse Spark 1.1

@@ -241,6 +241,4 @@ The test highlights how a generative model can produce a complex, coherent, and
 
 ### 7. Conclusion
 
-An element of extraordinary methodological interest that emerged during the experiment concerns the behavior of the supervision system. In confirming the formal validity of the legal theories expressed, the validator anchored its verification to databases that were clearly inconsistent with the domain being addressed (assigning the authorship of concepts from the philosophy of law to PubMed sources).
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-The test proved so profound that it revealed the Epistemic Boundary even within the validator itself, proving that statistical consistency tends to structurally override historical truth. This phenomenon empirically demonstrates that the residual uncertainty regime is not an isolated flaw of the generative model (Muse Spark), but an intrinsic characteristic of the autoregressive architecture, which also influences the automated evaluation and monitoring processes. The adopted protocol therefore offers a reproducible and necessary method for quantitatively mapping these fractures, reminding us that the integrity of synthetic knowledge cannot be assessed by its form, but only by the transparency of its traceability.
+The test demonstrates that a language model can generate complex and coherent legal content that is not, however, necessarily grounded from an epistemic standpoint. External validation makes it possible to identify gaps, omissions, and discrepancies between the text’s authoritative tone and its actual factual basis, revealing that epistemic robustness does not equate to linguistic or argumentative quality. The primary result is methodological: text generation and verification must be considered in tandem to ensure reliability and transparency. The adopted protocol offers a reproducible method for assessing the epistemic integrity of content produced by autoregressive models, highlighting that a text’s credibility cannot be inferred from its form, but only from its verifiability.