Logical generative models for probabilistic reasoning about existence, roles and identity
Bibliographic record
Abstract
In probabilistic reasoning, the problems of existence and identity are important to many different queries; for example, the probability that something that fits some description exists, the probability that some description refers to an object you know about or to a new object, or the probability that an object fulfils some role. Many interesting queries reduce to reasoning about the role of objects. Being able to talk about the existence of parts and sub-parts and the relationships between these parts, allows for probability distributions over complex descriptions. Rather than trying to define a new language, this paper shows how the integration of multiple objects, ontologies and roles can be achieved cleanly. This solves two main problems: reasoning about existence and identity while preserving the clarity principle that specifies that probabilities must be over well defined propositions, and the correspondence problem that means that we don’t need to search over all possible correspondences between objects said to exist and things in the world.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".