Solving the Sensory Information Bottleneck to Central Processing in Adaptive Systems
Bibliographic record
Abstract
Information bottlenecks are an inevitable consequence when a complex system adapts by increasing its information input. Input and output bottlenecks are due to geometrical limits that arise because the area available for connections from an external surface always exceeds the area available for connections to an internal surface. Processing of the additional information faces an internal bottleneck As more elements increase the size of a processor, its interface surface increases more slowly than its volume. These bottlenecks had to be overcome before more complex life forms could evolve. Based on mapping studies, it is generally agreed that sensory inputs to the brain are organized as convergent-divergent networks. However, no one has previously explained how such networks can conserve the location and magnitude of any particular stimulus. The solution to a convergent-divergent network that overcomes bottleneck problems turns out to be surprisingly simple and yet restricted.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".