KASER: a qualitatively fuzzy object-oriented inference engine
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
This paper describes a shell that has been developed for the purpose of fuzzy qualitative reasoning. The relation among object predicates is defined by object trees that are fully capable of dynamic growth and maintenance. The qualitatively fuzzy inference engine and the developed expert system can then acquire a virtual-rule space that is exponentially (subject to machine implementation constants) larger than the actual, declared-rule space and with a decreasing non-zero likelihood of error. This capability is called knowledge amplification, and the methodology is named KASER. KASER is an acronym for Knowledge Amplification by Structured Expert Randomization. It can handle the knowledge-acquisition bottleneck in expert systems. KASER represents an intelligent, creative system that fails softly, learns over a network, and has enormous potential for automated decision making. KASERs compute with words and phrases and possess capabilities for metaphorical explanations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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