Intelligence in architectures: reconfigurable learning techniques in autonomous agents
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
In this paper, we introduce a computing technique based on a genetic algorithm (GA) built to first evolve, then learn the logic circuits of defined functions. An input model represents the problem being resolved, and the system evolves a solution using a hardware-based GA. The presented work is part of the ongoing research in the concept of intelligent architectures, first presented by Rami Abielmona (2002), based on the merge of evolvable hardware methodologies and reconfigurable computing approaches. The system, named Genetic algorithm Synthesis, has been realized and analyzed, with the results presented in this paper. The major finding is that the system is able to find a minimized representation of the circuit, based on the ideas of functional decomposition of boolean literals and technology mapping on a field programmable gate array. The system not only finds a minimized structure,, but multiple minimal structures, thus allowing the environment to control which structure is reconfigured on the device, based on external factors such as temperature, available area and/or memory and required speed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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