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
Vision and manipulation are inextricably intertwined in the primate brain. Tantalizing results from neuroscience are shedding light on the mixed motor and sensory representations used by the brain during reaching, grasping, and object recognition. We now know a great deal about what happens in the brain during these activities, but not necessarily why. Is the integration we see functionally important, or just a reflection of evolution's lack of enthusiasm for sharp modularity? We wish to instantiate these results in robotic form to probe the technical advantages and to find any lacunae in existing models. We believe it would be missing the point to investigate this on a platform where dextrous manipulation and sophisticated machine vision are already implemented in their mature form, and instead follow a developmental approach from simpler primitives. We begin with a precursor to manipulation, simple poking and prodding, and show how it facilitates object segmentation, a long-standing problem in machine vision. The robot can familiarize itself with the objects in its environment by acting upon them. It can then recognize other actors (such as humans) in the environment through their effect on the objects it has learned about. We argue that following causal chains of events out from the robot's body into the environment allows for a very natural developmental progression of visual competence, and we relate this idea to results in neuroscience.
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) | 0.008 | 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