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
FPGA prototypes have become an increasingly important part of the overall integrated circuit design and verification flow, providing the ability to test an integrated circuit running at (near) speed with realistic inputs and outputs. When unexpected behaviour is observed in the prototype, it is necessary to determine the source of this behaviour, this usually requires observing signals that are internal to one of the devices in the prototype. Tools currently exist to enable FPGAs to be instrumented, but these are normally used in a reactive manner, that is, instrumentation is only added after incorrect behaviour has been observed. In this paper, we propose speculative debug insertion, in which a tool automatically predicts what signals will be useful during debug, and instruments the design during the first compilation. If done correctly, this can significantly accelerate the debug process, especially for large prototypes containing many FPGAs. However, it is important that this does not negatively affect the performance, capacity, power, or compilation time. We show that speculative debug insertion is possible, and experimentally evaluate the limits to speculative insertion.
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) | 0.000 | 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