IntentTwin: Deconfounding Candidate Support and the Oracle-to-Deployment Gap in Offline Web Behavior Prediction

Offline web-behavior benchmarks frequently insert the demonstrated action into a finite candidate set and then measure reranking. We show that this protocol can create a candidate-support confound: when the target is outside training support while all distractors are sampled from that support, support membership identifies the gold action without modeling behavior. We formalize this effect with support-identifiability rate (SIR), construct support-balanced candidate sets, and evaluate a target-free dual-path proposer that combines support retrieval with open-vocabulary DOM/LM proposals. On filtered OPeRA (4,282/469/992 train/dev/test steps), the original protocol has SIR $1.0000$ on target-OOV rows and a support-only rule reaches $1.0000$ OOV Exact@1. Support balancing reduces SIR to $0.0000$; the strongest conditional reranker obtains $0.7214$ overall Exact@1, $0.7988$ on IV rows, and $0.5668$ on OOV rows. The target-free dual-path system reaches Proposal Recall@16 $0.6915$ and end-to-end Exact@1 $0.5212$, compared with oracle conditional Exact@1 $0.7450$. On Mind2Web, the same audit yields SIR $0.2380$ and an oracle-to-proposal gap of $0.2125$. A 200-row semantic audit labels $0.7150$ of exact-key OOV targets as semantically novel, $0.2450$ as locator/string variation, and $0.0400$ as ambiguous (Cohen's $\kappa =0.8173$). These measurements show why support composition must be controlled alongside candidate accuracy when offline results are used to reason about deployment.