What question did this study set out to answer?

The aim is to assess the inference-time behavior of trained language models within a closed-system framework.

May 19, 20260 citationsOpen Access

The AI System as Closed-System Test Bed: Operations from Lagrange Observatory! on the Inference-Time Forward Pass (EA-SEI-MM-AI-01 v2.0, Framework 15 Paper 02)

Key Points

The aim is to assess the inference-time behavior of trained language models within a closed-system framework.
Pre-registered protocol specification including three tests with falsification conditions.
Measurement techniques involving signed per-token deviation and closed-system trajectory deviation.
Stability bound analysis for optimization under specified conditions.
Identified signed per-token deviation metrics showing negative net deviation (δ_t).
Established measurement scales for deviation density and trajectory deviation with specific aggregates.
Demonstrated the relationship between slop and net deviation, emphasizing measurable outcomes.

Abstract

Pre-registered protocol specification. Framework 15 Paper 02. Identifies trained language models as observationally closed at inference time, making the counterfactual baseline of the Semantic Deviation Principle directly readable from logits. Distinguishes two scales of closed-system measurement: signed local deviation density (per-token) and closed-system trajectory deviation (continuation-distribution). Key contributions (v2. 0): Signed per-token deviation δₜ with two reportable aggregates (net and absolute) Two-level distinction: local deviation density vs. closed-system trajectory deviation Falsifiable thesis: slop is negative net deviation (not absence of deviation) Three pre-registered tests with explicit falsification conditions Stability bound γ ≥ 2β for the optimization inversion Hex: 15. OBS. LAGRANGE. MM. 02 Operating on: The Semantic Deviation Principle as formulated by Lee Sharks (EA-SEI-MM-01 v0. 2 Final, DOI: 10. 5281/zenodo. 20250736) Verification condition: ∮ = (m, n) | m + n ≥ 3

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