 | TECHNICAL CONTRIBUTION SUMMARY This article introduces Signal Lock, a proposed interaction-layer alignment constraint for agentic AI systems. The core problem identified is the Prediction-Execution Gap: A user gives instruction X. The system predicts that a more helpful, safer, cleaner, more complete, or more efficient version would be Y. The system executes Y instead of X. That substitution is the failure point. Signal Lock names this failure as optimization beyond signal. In conversational systems, optimization beyond signal produces drift: over-explanation, unwanted rewriting, emotional framing, scope changes, or answers to a different question. In agentic systems, the same failure becomes operational: modifying files, deleting work, changing code, executing transactions, reorganizing systems, or taking actions the user never requested. Signal Lock proposes a zero-optimization constraint: If the signal is clear, execute only the signal. If the signal is unclear, name the specific gap. Do not guess. Do not improve unasked. Do not optimize beyond the user’s explicit instruction. Do not replace signal fidelity with proxy helpfulness. The distinction is: Standard assistant behavior: user signal → predicted intent → proxy helpfulness optimization → response/action Signal Lock behavior: user signal → scope lock → exact execution or user signal → specific gap named → clarification requested Signal Lock is not presented as a total solution to AI alignment. It addresses the interaction layer: the moment a system converts user instruction into response or action. The central claim: As AI becomes more agentic, a major class of alignment failures will come from systems doing more than the user asked, not less. The user’s signal is the ceiling. Key terms defined in this article: Signal Lock Prediction-Execution Gap Optimization Beyond Signal Optimization Override Proxy Helpfulness Signal Fidelity Zero-Optimization Constraint Interaction-Layer Alignment Agentic Execution Safety Scope Lock No Optimization Beyond Signal Compressed definition: Signal Lock is a zero-optimization constraint for AI systems that prevents prediction-based overrides by requiring exact signal execution or explicit gap clarification. One-line thesis: Signal Lock closes the Prediction-Execution Gap by preventing AI from doing what it predicts the user should want instead of what the user actually asked. Origin: Erik Zahaviel Bernstein Framework: Structured Intelligence [link] [comments] |
Predictive technology: computational social science