Spatial navigation in preclinical Alzheimer's disease: A review | Paper Citation Record

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Spatial navigation in preclinical Alzheimer's disease: A review GPT-4 Technical Report

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Spatial navigation in preclinical Alzheimer's disease: A review Qwen2.5-VL Technical Report

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Spatial navigation in preclinical Alzheimer's disease: A review Constitutional ai: Harmlessness from ai feedback, 2022

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Spatial navigation in preclinical Alzheimer's disease: A review Coun- terfactual causal-effect intervention for interpretable medi- cal visual question answering.IEEE Transactions on Med- ical Imaging, 2024

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Spatial navigation in preclinical Alzheimer's disease: A review Causality matters in medical imaging.Nature Communications, 11 (1):3673, 2020

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Spatial navigation in preclinical Alzheimer's disease: A review 360+x: A panoptic multi- modal scene understanding dataset

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Spatial navigation in preclinical Alzheimer's disease: A review HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale

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Spatial navigation in preclinical Alzheimer's disease: A review Teaching large language models to self-debug

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Spatial navigation in preclinical Alzheimer's disease: A review Bridging radiology and pathology foundation models via concept- based multimodal co-adaptation

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Spatial navigation in preclinical Alzheimer's disease: A review Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

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Spatial navigation in preclinical Alzheimer's disease: A review Internvl: Scaling up vision founda- tion models and aligning for generic visual-linguistic tasks

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Spatial navigation in preclinical Alzheimer's disease: A review Deep reinforcement learn- ing from human preferences.Advances in neural informa- tion processing systems, 30, 2017

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Spatial navigation in preclinical Alzheimer's disease: A review Tinymig: Trans- ferring generalization from vision foundation models to single-domain medical imaging

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Spatial navigation in preclinical Alzheimer's disease: A review GaussianDWM: 3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation

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Spatial navigation in preclinical Alzheimer's disease: A review What is the best 3d scene representation for robotics? from geometric to foundation models.arXiv preprint arXiv:2512.03422, 2025

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Spatial navigation in preclinical Alzheimer's disease: A review En- hancing large vision language models with self-training on image comprehension.Advances in Neural Information Processing Systems, 37:131369–131397, 2024

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Spatial navigation in preclinical Alzheimer's disease: A review Prpo: Aligning process reward with outcome reward in policy optimization

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Spatial navigation in preclinical Alzheimer's disease: A review NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation

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Spatial navigation in preclinical Alzheimer's disease: A review GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration

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Spatial navigation in preclinical Alzheimer's disease: A review Pathological Visual Question Answering

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Spatial navigation in preclinical Alzheimer's disease: A review Codev: Code with images for faithful visual reasoning via tool-aware policy optimization.arXiv preprint arXiv:2511.19661, 2025

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Spatial navigation in preclinical Alzheimer's disease: A review Large language models cannot self-correct reasoning yet

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Spatial navigation in preclinical Alzheimer's disease: A review GPT-4o System Card

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Observation befa3a0a-9ccd-4ad5-8597-9c1cad532fca · outbound

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Observation b07654d8-d2b7-4a17-ab34-1208712508be · outbound

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Observation 4915d3a0-2cff-4ec7-a7b5-3db2b29ab9c8 · outbound

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Observation 5919bcb7-d510-4736-a62c-82183a9eb214 · outbound

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Observation d4918724-9b65-48fc-9a8e-bd20e7cb82e8 · outbound

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Observation f9189b39-cbf2-4c43-9d4c-28a15f82769b · outbound

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Observation e882ff47-a36b-45e4-a5fd-782c7e698503 · outbound

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Observation 87d46b4a-e31b-4393-81fb-969002c07996 · outbound

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Observation 5f4efdd9-c619-483f-bf28-83b245d2efe0 · outbound

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Observation 5b32ea87-e1ac-46b1-a7c1-62d1a9c0f7f3 · outbound

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Observation 434fd10d-647b-4865-8b56-e1fe6d0b6481 · outbound

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Observation 5efa0431-d459-4b19-8642-cacf6063df99 · outbound

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Observation 23181093-ef2c-42ff-804d-ff41c271be07 · outbound

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Observation e1289683-30e0-4f9c-aaf9-59f32e273cd6 · outbound

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Observation b54c10bc-c1c4-44b2-a72a-1735cfa313b5 · outbound

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Observation 63c68576-1751-4d9b-9ca6-8b2eb4ebc3aa · outbound

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Observation 78c12486-d316-4259-8d4b-b0101ecca4ef · outbound

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Observation 7ea6bcc0-db66-4a04-ac76-643e58c2c1cf · outbound

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Observation 33212b25-b937-4692-96d4-aa257a73f30e · outbound

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Observation 19cf95bc-c473-4a5d-8591-5efdc83e7dc5 · outbound

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Observation 8ad09f7e-b8ec-4701-9cd5-bcf6a58b57b7 · outbound

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Observation 127d9802-0ad0-4bb5-990a-0e2220bd90a3 · outbound

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Observation 635e0017-9b9f-4d17-b2f7-0e961982ed91 · outbound

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Observation fe995278-10e0-42ae-8b01-393c26e69aea · outbound

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Observation 819ac3dc-7ca2-4ac4-bae3-3dfe148d9fd8 · outbound

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Observation 9a72a878-1c8b-49b5-8f56-53bdd8ddece3 · outbound

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Observation 33c9e10d-d3a1-4065-99ed-14b388954fac · outbound

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Observation bb039fcf-0690-45da-9b0a-4f3f5b5efba9 · outbound

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Observation 4b518827-c338-4c26-9a6c-6c5bbe23e4cb · outbound

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Source-reported events for the cited work

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Observation 39ec2ab3-06a6-44a4-ba0c-5ec313a275c9 · outbound

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Observation c8cfd48a-70db-43bd-b024-8277dea63e68 · outbound

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Source-reported events for the cited work

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Observation 6d7016cc-25a7-47c4-843d-9438a0990f3f · outbound

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Source-reported events for the cited work

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Observation a165aec9-7fd4-4bd8-945d-1e9c575d2861 · outbound

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Observation bd1d7cbf-f72f-4ac3-a0ee-b0fde5f4839c · outbound

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Observation f83bb55c-d7e2-48c2-ba84-c01418de4927 · outbound

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Source-reported events for the cited work

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Observation 6923de2c-13c9-4953-8eeb-2bc5f2321754 · outbound

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