ORBIT: EFFICIENT VERIFICATION OF EDITED LLM OUTPUTS WITH COMPACT ROUTING HINTS AND LOCAL SEMANTIC ASSESSMENT

Verifying a locally edited LLM output against a registry of retained originals requires both discovering its source and assessing whether that source supports the observed claims. We present ORBIT, an authenticated verification pipeline that reduces the cost of both stages. Compact routing hints direct discovery toward indexed identifier buckets, while bounded hint recovery and fallback retrieval share a candidate budget. Every visited record must pass the same signature, digest, and identifier checks before assessment. After authentication, observed differences select the edited query sentences and their neighbors for semantic scoring, while preserving the complete original as source evidence. We evaluate ORBIT on 233 queries at registry sizes of 270, 2,000, and 19,207 records. At 19,207 records, combining edit-localized assessment with optimized hint decoding reduces mean end-to-end latency by 38.22% relative to full-query assessment without hints. Within matched assessment rules, hints provide additional reductions of 5.29% for Full and 8.30% for Local. At a shared minimum-aggregation threshold, sentence selection preserves all 233 decisions while reducing scoring pairs from 2,994 to 1,292, or 56.85%. Human review gives Local 98.48% Supported acceptance and 90.28% precision, with no accepted Contradicted queries. Backend and budget experiments quantify routing costs and candidate capacity. Long document studies isolate the effect of evidence aggregation. Encoder experiments characterize hint recovery under bounded generation.