Exploiting Determinism to Scale Relational Inference
Why this work is in the frame
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Bibliographic record
Abstract
One key challenge in statistical relational learning (SRL) is scalable inference. Unfortunately, most real-world problems in SRL have expressive models that translate into large grounded networks, representing a bottleneck for any inference method and weakening its scalability. In this paper we introduce Preference Relaxation (PR), a two-stage strategy that uses the determinism present in the underlying model to improve the scalability of relational inference. The basic idea of PR is that if the underlying model involves mandatory (i.e. hard) constraints as well as preferences (i.e. soft constraints) then it is potentially wasteful to allocate memory for all constraints in advance when performing inference. To avoid this, PR starts by relaxing preferences and performing inference with hard constraints only. It then removes variables that violate hard constraints, thereby avoiding irrelevant computations involving preferences. In addition it uses the removed variables to enlarge the evidence database. This reduces the effective size of the grounded network. Our approach is general and can be applied to various inference methods in relational domains. Experiments on real-world applications show how PR substantially scales relational inference with a minor impact on accuracy.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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