On abstract intelligence and its denotational mathematics foundations
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
Recent researches reveal that various paradigms of intelligence, such as natural, artificial, machinable, and computational intelligence, can be unified at the logical and functional levels known as abstract intelligence. This paper introduces abstract intelligence as a form of driving force that transfers information into knowledge and behaviors. An architectural framework of abstract intelligence and the Generic Abstract Intelligence Mode (GAIM) are formally developed that provide a unified theory for explaining the mechanisms of advanced intelligence. In order to deal with the highly complex and abstract objects in abstract intelligence, denotational mathematics is introduced as a category of expressive mathematical structures for modeling and manipulating high-level mathematical entities beyond numbers and sets, such as abstract objects, complex relations, behavioral information, abstract concepts, knowledge, processes, and systems. Applications of denotational mathematics in abstract intelligence, cognitive informatics, and computational intelligence are elaborated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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