Revelation Control
Revelation Control introduces a decision-theoretic framework for selecting priced interventions that reveal hidden states only when distinctions can alter consequential decisions, while separately accounting for productive progress from the intervention itself The framework defines decision-sufficient revelation and revelation depth, separating pure information value from productive reuse, and embeds static Bayes refinement into state-dependent continuation value An exact cost-adjusted factoriza
Analysis
TL;DR
- Revelation Control introduces a decision-theoretic framework for selecting priced interventions that reveal hidden states only when distinctions can alter consequential decisions, while separately accounting for productive progress from the intervention itself
- The framework defines decision-sufficient revelation and revelation depth, separating pure information value from productive reuse, and embeds static Bayes refinement into state-dependent continuation value
- An exact cost-adjusted factorization criterion is established: an additional shallow coordinate is decision-nonredundant only when states sharing a scalar summary lie on opposite sides of the priced Stop/Continue boundary
- Bounded stop-flip risk alone cannot certify positive expected utility under unrestricted severity, proven as a formal result within the framework
- Empirical validation across Qwen2.5-7B and Mistral-7B-v0.3 shows deeper future-learning probes yield positive decision value and productive reuse provides strict equal-compute utility advantages, with structural (not numerical) transferability
Why It Matters
This framework addresses a fundamental gap in AI system design: how to intelligently allocate intervention budgets when probing model internals, ensuring that information gathering is justified by its decision-changing potential rather than treated as an end in itself. For AI practitioners building adaptive or continuously learning systems, it provides a rigorous cost-benefit calculus for when to invest in deeper introspection versus acting on current knowledge.
Technical Details
- The framework formalizes "decision-sufficient revelation" as the minimal hidden-state disclosure required to alter a consequential decision, with "revelation depth" quantifying how much state must be exposed
- It decomposes intervention value into two distinct components: pure information value (revelation that changes decisions) and productive reuse (progress that benefits future training independent of immediate decision impact)
- The cost-adjusted factorization criterion states that a shallow coordinate is decision-nonredundant if and only if states sharing a scalar summary fall on opposite sides of a priced Stop/Continue boundary
- A target-independent protocol is provided for model-specific instantiation, with formal proof that bounded stop-flip risk is insufficient to certify positive expected utility under unrestricted severity conditions
- Empirical evaluation on Qwen2.5-7B and Mistral-7B-v0.3 demonstrates that deeper future-learning probes have positive decision value; Qwen shows evidence of a decision-nonredundant shallow revealability regime, while Mistral exhibits scalar continuation architecture with positive familywise-adjusted lower bounds on disjoint target panels
Industry Insight
- Organizations investing in interpretability and introspection tools should adopt cost-bounded revelation criteria rather than maximizing information extraction, as unnecessary state disclosure wastes compute without decision impact
- The structural transferability finding suggests that while the theoretical framework generalizes across architectures, empirical thresholds and coefficients must be re-validated per system—teams should expect architecture-specific calibration rather than plug-and-play deployment
- The proof that bounded stop-flip risk is insufficient under unrestricted severity warns against over-reliance on safety-certification shortcuts; robust intervention protocols must account for severity distributions, not just flip probabilities
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