Learning to reason in a Probably Approximately Correct manner
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Bibliographic record
Abstract
This paper investigates the development of a knowledge base (KB) of logical functions, that can be used to do reasoning, from the consolidation of training examples of those logical functions. The work is based on the L2R (Learning to Reason) framework. A L2R agent only needs to answer knowledge queries that are relevant to its environment in a Probably Approximately Correct sense. We develop an L2R KB of Boolean functions by training a context-sensitive Multiple Task Learning network on examples of the truth tables of those functions. Reasoning is abstracted as a deduction task of determining whether a query Q is entailed by the KB. This is done by testing the neural network model on the truth table examples of Q to determine if the L2R KB agrees. Experimental results show that for different logical KBs and deduction rules the L2R approach shows promise.
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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 | 0.001 | 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