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
Decision-making in practical domains is usually complex, as a coordinated sequence of actions is needed to reach a satisfactory state, and responsive, as no fixed sequence works for all cases – instead we need to select actions after sensing the environment. At each step, a lookahead control policy chooses among feasible actions by envisioning their effects into the future and selecting the action leading to the most promising state. There are several challenges to producing the appropriate policy. First, when each individual state description is large, the policy may instead use a low-dimensional abstraction of the states. Second, in some situations the quality of the final state is not given, but can only be learned from data. Deeper lookahead typically selects actions that lead to higher-quality outcomes. Of course, as deep forecasts are computationally expensive, it is problematic when computational time is a factor. This paper makes this accuracy/efficiency tradeoff explicit, defining a system’s effectiveness in terms of both the quality of the returned response, and the computational cost. We then investigate how deeply a system should search, to optimize this “type II ” performance criterion.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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