Method of Regular Simplexes: A Difference-Assisted Simplex-Based Search Algorithm
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
A new difierence-assisted simplex-based algorithm which has signiflcant advantages in handling optimization problems with large dimensions is introduced. First, fundamental principles are utilized to illustrate a theorem that provides the basis for computation of the simplex-based difierences. Then, a set of new outside-expansion and inside-contraction points are deflned, and with the help of function values at these points, a bi-directional search pattern is constructed. The primary search direction is determined using the blended difierence information that is readily available at the outset of the analysis. In order to compensate for inaccuracies associated with the difierence-assisted descent path, a secondary search direction is also deflned with the help of the available information about function values. Examples are given to demonstrate the accuracy and e‐ciency of the new approach, and the performance of the algorithm in parallel environments is discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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