Phase Transition of Tractability in Constraint Satisfaction and Bayesian\n Network Inference
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
There has been great interest in identifying tractable subclasses of NP\ncomplete problems and designing efficient algorithms for these tractable\nclasses. Constraint satisfaction and Bayesian network inference are two\nexamples of such problems that are of great importance in AI and algorithms. In\nthis paper we study, under the frameworks of random constraint satisfaction\nproblems and random Bayesian networks, a typical tractable subclass\ncharacterized by the treewidth of the problems. We show that the property of\nhaving a bounded treewidth for CSPs and Bayesian network inference problem has\na phase transition that occurs while the underlying structures of problems are\nstill sparse. This implies that algorithms making use of treewidth based\nstructural knowledge only work efficiently in a limited range of random\ninstance.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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