{"paper":{"title":"VIG-RL: Learning to Search and Insert for Verified Image Grounding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chong Chen, Jun Guang, Qinhan Yu, Wentao Zhang","submitted_at":"2026-07-30T11:34:55Z","abstract_excerpt":"In knowledge-intensive scenarios, providing reliable interleaved text-image responses requires Verified Image Grounding (VIG): the precise integration of retrieved authentic visual evidence. Existing retrieval-augmented frameworks predominantly rely on decoupled, static pipelines, inherently failing to dynamically reason about when external knowledge is required and where visual assets should be contextually inserted. To bridge this gap, we propose VIG-RL, an autonomous agentic framework that formulates the search-selection-insertion workflow as an active decision-making process. Operating wit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28055","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.28055/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}