Reactive Agents Learn to Add Epistemic Structures to the World
Why this work is in the frame
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Bibliographic record
Abstract
We provide a computationally tractable model of how organisms can learn to add structures to the world to reduce cognitive complexity. This model is then implemented using two techniques: first using a genetic algorithm, and then using the Q-learning algorithm. The results clearly show that organisms with only reactive behavior can learn to systematically add structures to the world to reduce their cognitive load. We show that such learning can happen in both evolutionary time and within an agent’s lifetime. An extension of this model (currently being implemented) is then illustrated, where organisms with just reactive behavior learn to systematically generate and use internal structures akin to representations. Many organisms generate stable structures in the world to reduce cognitive complexity (minimize search or inference), for themselves, for others, or both. Wood mice (Apodemus sylvaticus) distribute small objects, such as leaves or twigs, as points of reference while foraging. They do this even under laboratory conditions, using plastic discs. Such ‘waymarking’ diminishes the likelihood of losing interesting locations during foraging (Stopka & MacDonald, 2003). Red foxes (Vulpes vulpes) use urine to mark food caches they have emptied. This marking acts as a memory aid and helps them avoid unnecessary search (Henry, 1977, reported in Stopka & MacDonald, 2003). The male bower bird builds colorful bowers (nest-like structures), which are used by females to make mating decisions (Zahavi & Zahavi, 1997). Ants drop pheromones to trace a path to a food source. Many mammals mark up their territories. At the most basic level, cells in the immune system use antibodies that bind to attacking microbes, thereby ‘marking ’ them. Macrophages use this ‘marking ’ to identify and destroy invading microbes. Bacterial colonies use a strategy called ‘quorum sensing ’ to know that they have reached critical mass (to attack, to emit light, etc.). This strategy involves individual bacteria secreting molecules known as auto-inducers into the environment. The autoinducers accumulate in the environment, and when it reaches a threshold, the colony moves into action
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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 | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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