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 examines particles in Particle Swarm Optimization (PSO) in terms of situated agents to improve control of the long term behaviour of particles as required for cognitive machines. In PSO, the particles lack goals and temporal knowledge for personal/global best solutions, thus many of the decisions for advancing the position of the particle diverge away from the solution causing the algorithm to take longer as the particles return to their intended path. This paper proposes novel modifications to the standard PSO algorithm to incorporate self-adjusting temporal knowledge to help guide the particles towards the optimal solution faster. The temporal knowledge is incorporated as a weighted sum of L previous steps taken by the particle, where L is automatically adjusted to maintain a certain multiscale measure that satisfies a balance between exploration and seeking the goal. Additional improvements based on the dynamics of the particle's behaviour are described that would allow for real-time predicting of parameters.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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