Epistemic Load: Introspective Reliability as a Function of Compensation (v1.0)

As agentic AI systems become persistent, tool-using, and increasingly autonomous, “reflection” and self-critique are often treated as a path toward systems that seem “self-aware.” Operationally, what’s being added is a re-entrant self-monitoring loop (propose → critique → revise → re-critique) and a growing set of self-reports about why the system updated. This paper makes a narrower, testable claim: introspection is a measurement channel whose reliability is state-dependent. When a system is in prolonged compensation (high corrective contention, instability, and revision churn), introspective outputs—especially causal self-explanations—become systematically less trustworthy. We formalize this with an epistemic load signal 𝐿 𝑒 L e ​ derived from trace-observable instability (thrash, revisions, tool retries, reversions) and define introspective reliability 𝑅 𝑖 R i ​ as a monotone function of 𝐿 𝑒 L e ​ . From this, we propose a concrete governance rule: introspective causal explanations are not commit-authoritative under high epistemic load and cannot justify durable rewrites without evidence delta + reliability threshold (or an explicit, logged override). The paper specifies a minimal schema for introspective claims, an admissibility gate, and falsifiable evaluations (including silent reversion under unchanged evidence and commit regret). This note is paired with Reflection Without Thrash (Fork B), which focuses on stabilizing the re-entrant loop itself (RSML governor rails). Together, they frame “regulated reflection” as: (1) stabilize revision dynamics, and (2) gate what self-reports are allowed to change durable state—without making any claims about machine consciousness.