Constraint handling in probability collectives using a modified feasibility-based rule
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
Almost all existing heuristic techniques are unconstrained optimisation methods and treat the system in centralised way. A distributed and decentralised optimisation technique in the framework of collective intelligence referred to as probability collectives (PCs) decomposes the entire system into subsystems and treats them as a multi-agent system. Similar to other contemporary heuristic techniques, its performance is significantly affected when constraints are involved. In order to handle constraints, a modified feasibility-based rule is incorporated into the PC algorithm. The approach is validated by solving a variety of constrained test problems. A tension/compression spring design problem, welded beam design problem and pressure vessel design problem are also solved. The approach is shown to be sufficiently robust and other strengths and weaknesses are also discussed. The solution to these problems proves that the constrained PC approach can be applied to a variety of practical/real world problems.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it