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 work in low-latency, high-bandwidth communication systems has resulted in building user--level Network Interface Controllers (NICs) and communication abstractions that support direct access from the NIC to applications virtual memory to avoid both data copies and operating system intervention. Such mechanisms require the ability to directly manipulate user--level communication buffers for delivering data and achieving protection. To provide such abilities, NICs must maintain appropriate translation data structures. Most user--level NICs manage these data structures statically, which results both in high memory requirements for the NIC and limitations on the total size and number of communication buffers that a NIC can handle.In this paper, we categorize the types of data structures used by NICs and propose dynamic handle lookup as a mechanism to manage such data structures dynamically. We implement our approach in a modern, user--level communication system and evaluate our system, miNL, with both micro-benchmarks and real applications. We also study the impact of various cache parameters on system performance. We find that, with appropriate cache tuning, our approach reduces the amount of NIC memory required in our system by a factor of two for the total NIC memory and by more than 80% for the lookup data structures. Moreover, by pinning physical memory automatically and on demand, our approach eliminates the limitations and complexities imposed by static memory pinning that is used in most user--level communication systems. Our approach increases execution time by at most 3% for all but one applications we examine.
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