A Comparison of Case Acquisition Strategies for Learning from Observations of State-Based Experts.
Why this work is in the frame
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Bibliographic record
Abstract
This paper focuses on case acquisition strategies in the context of Case-based Learning from Observation (CBLfO). In Learning from Observation (LfO), a system learns behaviors by observing an expert rather than being explicitly programmed. Specifically, we focus on the problem of learning behaviors from experts that reason using internal state information, that is, information that can not be directly observed. The unobservability of this state information means that the behaviors can not be represented by a simple perception-to-action mapping. We propose a new case acquisition strategy called Similarity-based Chunking, and compare it with existing strategies to address this problem. Additionally, since standard classification accuracy in predicting the expert’s actions is known to be a poor measure for evaluating LfO systems, we propose a new evaluation procedure based on two complementary metrics: behavior performance and similarity with the expert.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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