Hive: A distributed system for vision processing
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
We have built a novel vision processing system architecture called Hive. Hive fills a gap in the vision middleware by providing mechanisms for simple setup and configuration of distributed vision computation. Hive facilitates communication between independent cross-platform modules via an extensible protocol, allowing these distributed modules to form a vision processing pipeline. A plug-in interface allows general software to be represented as Hive modules: e.g. drivers for hardware devices such as cameras or implementations of particular vision algorithms. The modules are set up as a peer-to-peer network which allows for automated data transfer, callbacks and synchronization. We describe the architecture, communication protocol, plug-in interface and control system for the modules. A distributed face tracking system demonstrates the simplicity and flexibility for creating complex distributed vision applications using Hive.
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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