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-## Epistemic Implications of the Failure Boundary
-
-Across the MarCognity cross-domain benchmark, claim-level verification exhibits a **persistent and domain-invariant failure rate of ~8–15%**.  
-This residual uncertainty remains even when the system is equipped with:
-
-- a metacognitive control loop  
-- a skeptical verification agent  
-- structured memory and reflection  
-- domain-specific retrieval mechanisms  
-
-### Failure Rate by Domain
-
-| Domain            | Failure Rate |
-|-------------------|--------------|
-| Medicine          | 15%          |
-| Linguistics       | 13%          |
-| Law               | 10.5%        |
-| Neuroscience      | 9%           |
-| Statistics        | 9%           |
-| Computer Science  | 9%           |
-| Physics           | 8.5%         |
-| Biology           | 6.5%         |
-
-The consistency of this pattern suggests that the source of uncertainty does not lie in the verification agent or in the architecture supervising the reasoning process.  
-Instead, it reflects a **structural constraint** inherent to autoregressive language models.
-
----
-
-## A Structural Limit of Probabilistic Language Models
-
-Autoregressive LLMs optimize **next-token likelihood**, not epistemic truth.  
-They generate coherent linguistic continuations without possessing:
-
-- internal truth states  
-- stable epistemic representations  
-- grounding mechanisms independent of text  
-
-As a consequence:
-
-- some claims remain unverifiable even under optimal verification  
-- the residual error is not reducible to noise  
-- the failure boundary appears as an **emergent property** of the generative process  
-
-This reframes the question from *“Where did the system fail?”* to the more fundamental:
-
-**“What structural limits of LLMs does this failure boundary reveal?”**
-
----
-
-## The Epistemic Boundary
-
-We define the **Epistemic Boundary** as the **irreducible region of uncertainty** in which textual evidence is insufficient to reduce epistemic risk below a threshold, **regardless of the verification architecture**.
-
-This boundary emerges from the mismatch between:
-
-- **linguistic coherence**, which LLMs optimize  
-- **epistemic justification**, which verification requires  
-
-It is reinforced by factors such as:
-
-- implicit or unstated reasoning  
-- incomplete or non-exhaustive corpora  
-- semantic underdetermination  
-- ambiguity inherent to natural language  
-
-Crucially, the boundary does **not** shrink through:
-
-- improved prompting  
-- stricter evaluation protocols  
-- more sophisticated verification agents  
-
-It reflects a deeper limitation:  
-**the epistemic space accessible to a probabilistic language model is narrower than the space of claims requiring justification.**
-
----
-
-## Scientific Significance
-
-The MarCognity framework does not attempt to eliminate this uncertainty.  
-Instead, it **makes it observable** through structured claim-level verification and metacognitive analysis.
-
-Within this perspective, the residual failure rate is not merely a performance metric but a **scientific signal**:
-
-**The rationality of LLM-based systems may be bounded not by the evaluating agent, but by the probabilistic engine that generates their outputs.**
-
-This insight outlines a direction for future research on the epistemic limits of language-based AI systems and on architectures designed to expose—rather than obscure—epistemic uncertainty.