Grounding Self-Consistency (Consensus Over Proposals)

A target can be grounded several ways at once — set-of-marks, OCR, template match, the a11y tree, or N samples from a model — and they don’t always agree. ab_locator / element_scoring rank locator strategies by historical reliability but never fuse simultaneous proposals into one consensus point with a dispersion metric, and action_grounding.snap_to_element snaps a single coordinate to the nearest element with no notion of disagreement. grounding_consensus clusters the candidate points (or votes candidate elements), returns the agreed target plus an agreement fraction and spread, and flags low-agreement targets so the caller can zoom / ask a human instead of clicking blind.

Pure-stdlib geometry; deterministic and unit-testable with no device. Imports no PySide6.

Headless API

from je_auto_control import consensus_point, consensus_element, is_confident

proposals = [[480, 260], [484, 258], [477, 263], [900, 100]]  # one outlier
result = consensus_point(proposals, cluster_radius=24)
if is_confident(result, min_agreement=0.6):
    click(*result.point)
else:
    print("proposals disagree:", result.agreement, result.spread)

# or vote proposals onto a known element list
element, agreement = consensus_element(proposals, elements)

consensus_point clusters [x, y] or [x, y, weight] candidates and returns a ConsensusResult (point / agreement — winning cluster weight over total / spread / n_clusters), or None when empty. consensus_element votes each proposal to its nearest element and returns (element, agreement). is_confident thresholds the agreement.

Executor commands

AC_consensus_point (candidates / cluster_radius → {found, result}) and AC_consensus_element (candidates / elements → {found, element, agreement}). They are exposed as the MCP tools ac_consensus_point / ac_consensus_element (read-only) and as the Script Builder commands Grounding Consensus Point / Grounding Consensus Element under Native UI.