Candidate Retention for Abductive Learning
Quick summary
arXiv:2609.39561v1 Announce Type: cross Abstract: Abductive learning combines neural perception with symbolic reasoning, using explanations generated by abduction to supervise the perception model. Multiple valid explanations of the same symbolic target can assign conflicting labels to the same inputs. Common policies select a single candidate as a pseudo-label, which may reinforce mistaken assignments, or weight all candidates, which may spread supervision across competing labels. These risks motivate selecting a retained subset to balance supervision sharpness and model-mass coverage. To gui
Key takeaways
- arXiv:2609.39561v1 Announce Type: cross Abstract: Abductive learning combines neural perception with symbolic reasoning, using explanations generated by abduction to supervise the perception model.
- Multiple valid explanations of the same symbolic target can assign conflicting labels to the same inputs.
- Common policies select a single candidate as a pseudo-label, which may reinforce mistaken assignments, or weight all candidates, which may spread supervision across competing labels.
Why it matters
“Candidate Retention for Abductive Learning” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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