University of Michigan - Ann Arbor

Selective filtering is the design principle stating that execution experience must be critically abstracted and filtered before being committed to persistent procedural memory. Unfiltered failure-driven adaptation risks three primary failure modes: overfitting to ambiguous task specifications by creating arbitrary default conventions, memorizing external evaluator quirks rather than genuine task contracts, and over-abstracting heavy, restrictive procedures that harm general performance across varied workflows. Harness scaling is fundamentally a problem of abstracting and protecting consequential execution boundaries rather than accumulating rules from every observed failure trace.

Selective Filtering in Harness Learning

According to the principles of selective filtering, what is the primary objective of harness scaling?

Unfiltered failure-driven adaptation risks creating heavy, restrictive procedures that harm general performance across varied workflows.

According to the principles of selective filtering, what specific failure mode arises when an autonomous harness adapts to ambiguous task specifications without proper filtering?

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