Causal Leverage Density: A Universal Framework for Semantic Information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite living in the information age, we often ponder: what does information mean? Shannon defined syntactic information using a simple measure applied to probability distributions. Syntactic information captures the degree of ‘surprise’ on the part of a message receiver, given some prior credences over the alphabet of symbols being transmitted through a channel; however it remains silent on semantics—the notion of meaning. This gap is especially critical for the artificial life community, which aims to understand and synthesize life-like processes where meaning and correlated function are essential. Existing approaches to semantic information are often domain-specific, tied either to linguistic contexts or to the viability of agents, limiting their generality. We thus introduce ‘Causal Leverage Density’ (CLD), a generalised approach to quantifying semantic information grounded in established concepts from statistical physics. CLD quantifies the influence of syntactic information by evaluating the effect of information-scrambling interventions on the future evolution of a system’s phase space trajectories, which encapsulate all relevant degrees of freedom. This concept yields a universal approach to characterising meaning and semantic influence in any type of system, from physics to biology to machine learning. Crucially, by identifying systems where information has causal efficacy, CLD offers a robust tool for defining and detecting life as might be found elsewhere in the universe or created artificially on Earth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it