{"paper":{"title":"SVSR: A Self-Verification and Self-Rectification Paradigm for Multimodal Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Multimodal models learn to verify and correct their own reasoning steps through a three-stage training process.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fei Luo, Hebei Li, Nianbing Su, Yanbiao Ma, Yueying Li, Zhe Qian, Zhonghua Wang, Zhongxing Xu, Zhuohan Ouyang","submitted_at":"2026-04-11T14:25:17Z","abstract_excerpt":"Current multimodal models often suffer from shallow reasoning, leading to errors caused by incomplete or inconsistent thought processes. To address this limitation, we propose Self-Verification and Self-Rectification (SVSR), a unified framework that explicitly integrates self-verification and self-rectification into the model's reasoning pipeline, substantially improving robustness and reliability in complex visual understanding and multimodal reasoning tasks. SVSR is built on a novel three-stage training paradigm. First, we construct a high-quality unified preference dataset by refining reaso"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"SVSR improves reasoning accuracy and enables stronger generalization to unseen tasks and question types. Notably, once trained with explicit self-reflective reasoning, the model also exhibits improved implicit reasoning ability, outperforming strong baselines even when no explicit reasoning traces are provided.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That refining reasoning traces from pre-trained VLMs and filtering model-generated traces with a teacher VLM produces data that genuinely teaches robust self-verification rather than just memorizing patterns or teacher biases.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SVSR trains multimodal models to verify and correct their own reasoning using a preference dataset, supervised fine-tuning, and semi-online DPO with a teacher model.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Multimodal models learn to verify and correct their own reasoning steps through a three-stage training process.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"00ed9c2a8d90def2a0611deb51c38c4e98c253c36fec4b2ef6cc6779a4e602dc"},"source":{"id":"2604.10228","kind":"arxiv","version":2},"verdict":{"id":"ed47cf41-1263-4469-a357-90a9ac790a1a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T15:55:35.578583Z","strongest_claim":"SVSR improves reasoning accuracy and enables stronger generalization to unseen tasks and question types. Notably, once trained with explicit self-reflective reasoning, the model also exhibits improved implicit reasoning ability, outperforming strong baselines even when no explicit reasoning traces are provided.","one_line_summary":"SVSR trains multimodal models to verify and correct their own reasoning using a preference dataset, supervised fine-tuning, and semi-online DPO with a teacher model.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That refining reasoning traces from pre-trained VLMs and filtering model-generated traces with a teacher VLM produces data that genuinely teaches robust self-verification rather than just memorizing patterns or teacher biases.","pith_extraction_headline":"Multimodal models learn to verify and correct their own reasoning steps through a three-stage training process."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.10228/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"}