{"paper":{"title":"OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A tool-augmented agent overcomes the limits of self-contained vision-language models in open-world forgery detection by integrating external tools and specialized training.","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jinjie Shen, Lechao Cheng, Nan Pu, Shengeng Tang, Tianrui Hui, Yaxiong Wang, Yuchen Zhang, Yujiao Wu, Zheng Huang, Zhun Zhong","submitted_at":"2026-05-16T12:26:04Z","abstract_excerpt":"Existing vision-language forgery detection and grounding methods operate under a closed-world paradigm, assuming verification can be completed by the model alone. However, self-contained MLLMs are constrained by finite parametric knowledge, static training corpora, and limited perceptual resolution, creating a practical ceiling in dynamic open-world forensics -- particularly for real-time event verification requiring external clues and forgery segmentation demanding fine-grained scrutiny of local manipulations. To address these limitations, we shift from scaling up the self-contained model tow"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"OmniVL-Guard Pro achieves state-of-the-art performance across various tasks, and exhibits strong zero-shot generalization by extending unified forensics from closed-world prediction to open-world clues-driven reasoning via tool integration, Tree-Structured Self-Evolving Tool Trajectory Generation, and Checker-Guided Agentic Reinforcement Learning.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the proposed tool environment (real-time event search, local cropping and zooming, edge-anomaly screening, face detection, video frame extraction, and SAM3-based segmentation) combined with the self-evolving trajectories and process-level RL supervision will overcome the practical ceiling of self-contained MLLMs in dynamic open-world forensics.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"OmniVL-Guard Pro is a tool-augmented agent for open-world vision-language forgery detection that integrates multiple external tools with self-evolving trajectory generation and checker-guided reinforcement learning to achieve claimed SOTA performance and zero-shot generalization.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A tool-augmented agent overcomes the limits of self-contained vision-language models in open-world forgery detection by integrating external tools and specialized training.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c95d6be815c2ee6e0d225658500f77dce867e8e33621ee5153353a1b46bc3e9e"},"source":{"id":"2605.16962","kind":"arxiv","version":1},"verdict":{"id":"b704a281-3143-43c8-b5de-4c47acc2df67","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:31:46.382566Z","strongest_claim":"OmniVL-Guard Pro achieves state-of-the-art performance across various tasks, and exhibits strong zero-shot generalization by extending unified forensics from closed-world prediction to open-world clues-driven reasoning via tool integration, Tree-Structured Self-Evolving Tool Trajectory Generation, and Checker-Guided Agentic Reinforcement Learning.","one_line_summary":"OmniVL-Guard Pro is a tool-augmented agent for open-world vision-language forgery detection that integrates multiple external tools with self-evolving trajectory generation and checker-guided reinforcement learning to achieve claimed SOTA performance and zero-shot generalization.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the proposed tool environment (real-time event search, local cropping and zooming, edge-anomaly screening, face detection, video frame extraction, and SAM3-based segmentation) combined with the self-evolving trajectories and process-level RL supervision will overcome the practical ceiling of self-contained MLLMs in dynamic open-world forensics.","pith_extraction_headline":"A tool-augmented agent overcomes the limits of self-contained vision-language models in open-world forgery detection by integrating external tools and specialized training."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16962/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.074325Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:40:51.214783Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T19:51:58.041086Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T19:50:15.272972Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.229719Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.314953Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"604583a90a580fda50963906dd9e2a37caa71bc310b85e611acc79324225c484"},"references":{"count":35,"sample":[{"doi":"","year":null,"title":"Is the event time correctly extracted?","work_id":"de314768-e092-4bb0-b655-4ac3bb36e0e1","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Is the subject correctly identified?","work_id":"7fc14d00-7a20-4050-8944-65a6d2a80fcf","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Is the location correctly identified?","work_id":"354f0945-418f-42ac-80fc-f48433c39cec","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Is the key action correctly described?","work_id":"df50d697-5cd9-47da-aa66-c4f76ff3d563","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Correct” or “Incorrect","work_id":"208ab5ca-e95a-41ea-b7a9-b0ec46a8c36e","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":35,"snapshot_sha256":"f0dbcba748c766aa8033ad44e03bc1d03f0342835fd093e1f97e4deefc2a7c1b","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"12613d48b186b93bd25f8c04b9024f5a4bab68da4e3dba814a43482ff4ee2f60"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}