A smart buffer for tracking using motion data
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
Responsive vision is vision responding to the environment. The characteristics of a responsive system are: active response in dynamic environment, real time computation, and using multiple modalities in a multi-purpose system. The vision engine is a general purpose for general vision tasks. Early vision processing, e.g., optical flow and stereo is implemented in near real-time using the Datacube, producing dense displacement fields at near video rates, which are then transferred to a transputer subsystem, where data dependent processing occurs in parallel on subimages. The authors use the vision engine for complex processing under real-time constraints, the differences between the processing rates in a robotic system require smart buffers, objects that can buffer data between perception, reasoning and action processes. Smart buffers offer a simple interface between asynchronous processing tasks and simplify the structure of multiprocessor vision systems. The authors describe a simple motion tracker that uses a smart buffer to mediate between early and middle vision processing. The smart buffer permits the system to sense during action by letting the sensing component accumulate visual data in the course of action.
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