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
Recent research has proposed the use of trusted execution environments (TEEs), such as SGX, in serverless computing to safeguard against threats from insecure system software, malicious co-located tenants, or suspicious cloud operators. However, integrating SGX, one of the most mature TEE, with serverless computing results in significant performance degradation due to the function startup latency caused by enclave creation. This performance degradation arises because SGX is not designed with serverless function startup procedures in mind, where numerous application codes, libraries, and data are re-initialized upon each function invocation. The inherent limitations of SGX contribute to significant performance degradation, whether through the addition of every page into the enclave, or the restriction of page permissions, which ultimately cause TLB flushes, context switches, and re-entering the enclave. In this paper, we first take key observations resident in the intrinsic features of the server-less function and propose Cryonics, a method of serving snapshot-based enclave that accelerates the startup time of the function instance by creating a future-proof working set of that. We consider the page locality and obsolete pages of the enclaved function instance to create a lightweight working set used for serving requests. Our evaluation shows that Cryonics achieves up to 100x outperformed startup time compared to existing cold-start-based methods and reveals the stability of the startup time.
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 | 0.001 |
| 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 | 0.001 |
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