A Framework for Leveraging LLMs for Scene Analysis and Cognitive 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
In everyday visual search tasks, humans rely on prior knowledge of object placements in scenes to efficiently locate target objects. This ability is evidenced by eye movement patterns, where individuals focus on areas that are more likely to contain the target, such as searching for a cup on a table or shoes on the floor. Building on this, we propose a new annotation pipeline that leverages these priors by extracting a knowledge graph from images based on automatically annotated objects. This knowledge graph is then used with large language models (LLMs) to predict the most likely locations of a specific target object in an image. Our approach is the first instance of using LLMs to identify relevant prior knowledge in images and to bridge the gap between human scene understanding and computational models.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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