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
What's New? <strong>Python frontend improvements</strong>: More Python features are supported, such as return values, tuples, and numpy broadcasting. <code>@dace.program</code>s can now call other programs or SDFGs. <strong>AMD GPU (HIP) Support</strong>: AMD GPUs are now fully supported with HIP code generation. <strong>Easy-to-use transformation APIs</strong>: Apply transformation compositions with one call, enumerate subgraph matches manually, and many more functions now available as part of the dace API. See the new tutorial for examples. <strong>Faster code generation</strong>: Backends now generate lower-level code that is more compiler-friendly. <strong>Instrumentation interface</strong>: Setting the <code>instrument</code> property for SDFG nodes and states enables easy-to-use, localized performance reporting with timers, GPU events, and PAPI performance counters. <strong>DaCe VSCode plugin</strong>: Interactive SDFG viewer and optimizer as part of Visual Studio Code. Download the plugin here. <strong>Type inference and connector types</strong>: In addition to automatic type inference, connectors on nodes can now be defined with explicit types, giving more fine-grained control over type reinterpreting and vector types. <strong>Subgraph transformations</strong>: New transformation type that can work on arbitrary subgraphs. For example, fuse any computation within a state with <code>SubgraphFusion</code>. <strong>Persistent GPU kernel schedule</strong>: Launch persistent kernels with a change of a property! Proportion used of GPU multiprocessors is configurable. <strong>More transformations</strong>: Loop manipulation and other new transformations now available with DaCe. Some transformations (such as <code>Vectorization</code>) made more robust to corner cases. <strong>More tools</strong>: Use <code>sdfgcc</code> to quickly compile and optimize <code>.sdfg</code> files from the command line, generating header and library files. Great for interoperability and Makefiles. <strong>Short DaCe annotation</strong>: Data-centric functions can now be annotated with <code>@dace</code>. <strong>Many minor fixes and additions</strong>: More library nodes (such as <code>einsum</code>) and new properties added, enabling faster performance and more productive high-performance coding than ever.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.233 | 0.199 |
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