Sequential Demand-Driven Evaluation of Eager TransLucid
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
We present the Eager TransLucid language, an inten- sional programming language in which the value of a variable is a function mapping multidimensional contexts - the "possible worlds" of intensional logic - to ground values or, equivalently, that variables define multidimensional arrays of arbitrary dimensionality. The Eager TransLucid language is a natural generalisation of Wadge and Ashcroft's Lucid dataflow language. Given a specific set of equations and a context, the operational semantics determines the value taken by a variable in that context, which may depend both on the values of dimensions within the context and the values of variables in other contexts. The contexts correspond to tags in tagged-token dataflow systems. The key contribution of the paper is to prove that it is possible to create a warehouse caching the values of already computed (identifier, context) pairs in such a way as to ensure that no reference is made to unnecessary dimensions. The method consists of storing demands for relevant dimensions in the current context as these are needed.
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 | 0.001 |
| 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 it