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
Modern processors use branch target buffers (BTBs) to predict the target address of branches such that they can fetch ahead in the instruction stream increasing concurrency and performance. Ideally, BTBs would be sufficiently large to capture the entire working set of the application and sufficiently small for fast access and practical on-chip dedicated storage. Depending on the application, these requirements are at odds. This work introduces a BTB design that accommodates large instruction footprints without dedicating expensive onchip resources. In the proposed Phantom-BTB (PBTB) design, a conventional BTB is augmented with a virtual table that collects branch target information as the application runs. The virtual table does not have fixed dedicated storage. Instead, it is transparently allocated, on demand, in the on-chip caches, at cache line granularity. The entries in the virtual table are proactively prefetched and installed in the dedicated conventional BTB, thus, increasing its perceived capacity. Experimental results with commercial workloads under full-system simulation demonstrate that PBTB improves IPC performance over a 1K-entry BTB by 6.9% on average and up to 12.7%, with a storage overhead of only 8%. Overall, the virtualized design performs within 1% of a conventional 4K-entry, single-cycle access BTB, while the dedicated storage is 3.6 times smaller.
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 | 0.002 | 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