{"paper":{"title":"Cognitive Pivot Points and Visual Anchoring: Unveiling and Rectifying Hallucinations in Multimodal Reasoning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Multimodal reasoning models hallucinate when they stop querying visual evidence at high-entropy decision points.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fei Luo, Jungong Han, Xinyu Liu, Yanbiao Ma, Yike Guo, Zhe Qian, Zhonghua Wang, Zhongxing Xu, Zhuohan Ouyang, Zongyuan Ge","submitted_at":"2026-04-11T13:59:05Z","abstract_excerpt":"Multimodal Large Reasoning Models (MLRMs) have achieved remarkable strides in visual reasoning through test time compute scaling, yet long chain reasoning remains prone to hallucinations. We identify a concerning phenomenon termed the Reasoning Vision Truth Disconnect (RVTD): hallucinations are strongly correlated with cognitive bifurcation points that often exhibit high entropy states. We attribute this vulnerability to a breakdown in visual semantic anchoring, localized within the network's intermediate layers; specifically, during these high uncertainty transitions, the model fails to query"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We identify a concerning phenomenon termed the Reasoning Vision Truth Disconnect (RVTD): hallucinations are strongly correlated with cognitive bifurcation points that often exhibit high entropy states. We attribute this vulnerability to a breakdown in visual semantic anchoring, localized within the network's intermediate layers; specifically, during these high uncertainty transitions, the model fails to query visual evidence, reverting instead to language priors.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that dynamically incentivizing visual attention across critical intermediate layers upon detecting high entropy states will translate external debiasing interventions into an intrinsic capability for hallucination mitigation, and that this can be achieved via the proposed HVAR within GRPO and FRM without degrading overall reasoning performance.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Multimodal reasoning models hallucinate at high-entropy cognitive bifurcation points due to loss of visual semantic anchoring, and the V-STAR training paradigm with HVAR rewards and FRM reflection mitigates this by reinforcing visual attention.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Multimodal reasoning models hallucinate when they stop querying visual evidence at high-entropy decision points.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c33a11838a197cb52ed74862c18cbdb9a7bc8eaaa3e8c5abe345394eade85bf8"},"source":{"id":"2604.10219","kind":"arxiv","version":2},"verdict":{"id":"eb859ac6-13a1-428f-8293-f554d6b43d8d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T15:59:59.661589Z","strongest_claim":"We identify a concerning phenomenon termed the Reasoning Vision Truth Disconnect (RVTD): hallucinations are strongly correlated with cognitive bifurcation points that often exhibit high entropy states. We attribute this vulnerability to a breakdown in visual semantic anchoring, localized within the network's intermediate layers; specifically, during these high uncertainty transitions, the model fails to query visual evidence, reverting instead to language priors.","one_line_summary":"Multimodal reasoning models hallucinate at high-entropy cognitive bifurcation points due to loss of visual semantic anchoring, and the V-STAR training paradigm with HVAR rewards and FRM reflection mitigates this by reinforcing visual attention.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that dynamically incentivizing visual attention across critical intermediate layers upon detecting high entropy states will translate external debiasing interventions into an intrinsic capability for hallucination mitigation, and that this can be achieved via the proposed HVAR within GRPO and FRM without degrading overall reasoning performance.","pith_extraction_headline":"Multimodal reasoning models hallucinate when they stop querying visual evidence at high-entropy decision points."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.10219/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"}