finding
active
finding:agent-achieves-approximately-70-sticker-removal-success-rate-by-end-of-500k-training-stepsAgent achieves approximately 70% sticker-removal success rate by end of 500k training steps
Main behavioral result demonstrating the model's efficacy in the mirror-mark task
Source paper
extracted_from(2026) · Dongmin Kim · Hoshinori Kanazawa · Yasuo Kuniyoshi
Neighborhood — ranked by edge-count
Claims (3)
claim
- A sensory anomaly (sticker mismatch) can itself become an intrinsic drive for action under active inference, even without external rewardassociated_withsupportsCore mechanism claim linking mismatch detection to behavior through EFE minimization
- Central interpretive claim of the paper, supported by EFE decrease after sticker removal
- Explains the ceiling on removal success as due to perceptual and kinematic constraints, not principled failures
Questions (1)
question
- The central research question motivating the paper
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Shows learning progression from chance-level to functional behavior
- Suggests the agent learned to recognize and approach the sticker before achieving reliable removal
- Operational definition: hand stays within 2 cm of sticker for 50 consecutive steps (0.5 seconds)
- Control showing that the EFE signal is learned, not inherent to the architecture
- Qualitative confirmation of EFE drop in trained model vs. untrained model (Δ = +1.70)
- Connects the model's behavior to Zaadnoordijk and Bayne's taxonomy of intentional agency
- Confirms that EFE systematically decreases after sticker removal, validating the self-prior as internal criterion
- Baseline EFE when sticker is present, used for comparison