Results of simulations of a system with the recommendation architecture
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
Functionally complex electronic systems are organized into functional components exchanging unambiguous information. The requirement to exchange unambiguous information results in difficulties in implementing parallel processing and extreme difficulty in implementing any capability to heuristically change functionality based on experience. The recommendation architecture allows the exchange of ambiguous information between functional components and therefore offers a way to reduce these difficulties. A system with the recommendation architecture uses a device imprinting mechanism to heuristically organize its inputs into a portfolio of ambiguous information repetition conditions on a range of levels of detail. The presence or absence of these conditions contains enough information to be used by a separate subsystem to determine appropriate behavior. Simulations of a simple system with the recommendation architecture demonstrate that sequences of inputs of wide range of different types can be heuristically organized into a functionally usable set of repetition conditions. Organization is successful even though there are no exact repetitions of input conditions. Learning effectiveness measures which make no use of information on the consequences of system actions can be used to adjust architectural parameters to organize even wider ranges of input types. These results demonstrate the feasibility of developing functionally complex systems with the recommendation architecture.
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