Bibliographic record
Abstract
Every task involves constraint, Solve the thing without complaint; There are magic links and chains Forged to loose our rigid brains. Structures, strictures, though they bind, Strangely liberate the mind . –James Falen Instead of reasoning explicitly in terms of states, it is often better to describe states in terms of features and then to reason in terms of these features. Often these features are not independent and there are hard constraints that specify legal combinations of assignments of values to variables. As Falen's elegant poem emphasizes, the mind discovers and exploits constraints to solve tasks. Common examples occur in planning and scheduling, where an agent must assign a time for each action that must be carried out; typically, there are constraints on when actions can be carried out and constraints specifying that the actions must actually achieve a goal. There are also often preferences over values that can be specified in terms of soft constraints . This chapter shows how to generate assignments that satisfy a set of hard constraints and how to optimize a collection of soft constraints. Features and States For any practical problem, an agent cannot reason in terms of states; there are simply too many of them. Moreover, most problems do not come with an explicit list of states; the states are typically described implicitly in terms of features .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".