Bibliographic record
Abstract
The Logic Virtual Machine (LVM) is an efficient Prolog execution model consisting of a set of high-level instructions and a memory architecture for handling control and unification. Different from the well-known Warren's Abstract Machine which uses Structure Copying method, the LVM adopts a hybrid of Program Sharing and Structure Copying to represent first-order terms. In addition, the LVM employs a single stack paradigm for dynamical memory allocation and embeds an efficient garbage collection algorithm to reclaim useless memory cells. This paper will present the design of the LVM compiler-LVMC. It is developed to translate Prolog programs into LVM bytecode instructions. The compiler heavily depends on the input mode to generate optimized LVM code. It will extract the necessary properties, such as determinacy and garbage estimation, from source programs. At the implementation level, the LVMC carries out determinism transformation, garbage collection assistance, last argument dispatching, and some special optimizations. The first version of the LVMC (about 8000 lines of C code) has been developed. Some compiled programs have been tested under the LVM emulator. Benchmarks show that the LVM system is highly promising in memory utilization and performance.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".