Swarm Intelligence in Multiplexed Robots Problems Identified
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
Although kindling with the very consciousness of the human mind may have been banned, this did not stop scientists from tinkering and innovating with all the technology they were surrounded by. Stumbled by mathematical problems, mathematicians developed certain tactics we call algorithms. Algorithms are the layman's logic language for any device to function according to the desired task.Well, Mother Nature has already built us an unimaginable and incomprehensible algorithm propelled by personal choices, emotions, feelings, and sometimes rationality. For people who still didn't get it, we call it brain in day-to-day language. Our mind subconsciously designs such algorithms for us to efficiently function in our every day to day lives. Our project is an effort to reconstruct such rationality of thinking by artificial means through the use of reinforcement learning and robots to conclude the following 1) Is it possible to recreate human understanding? 2) If yes, then how do we channel it into something practical and physical? 3) How do we utilize this so-formed machine as a result of the conclusion of the second question? Asking this to ourselves we pursued to make our project P3 Hexa which is an endeavor to tinker with swarm intelligence and physically experience the functioning of a self-designed algorithm
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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