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CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models
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CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models
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We introduce CausalVQA, a benchmark dataset for video question answering (VQA) composed of question-answer pairs that probe models' understanding of causality in the physical world. Existing VQA benchmarks either tend to focus on surface perceptual understanding of real-world videos, or on narrow physical reasoning questions created using simulation environments. CausalVQA fills an important gap by presenting challenging questions that are grounded in real-world scenarios, while focusing on models' ability to predict the likely outcomes of different actions and events through five question types: counterfactual, hypothetical, anticipation, planning and descriptive. We designed quality control mechanisms that prevent models from exploiting trivial shortcuts, requiring models to base their answers on deep visual understanding instead of linguistic cues. We find that current frontier multimodal models fall substantially below human performance on the benchmark, especially on anticipation and hypothetical questions. This highlights a challenge for current systems to leverage spatial-temporal reasoning, understanding of physical principles, and comprehension of possible alternatives to make accurate predictions in real-world settings.
Forward citations
Cited by 15 Pith papers
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CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models
CRONOS benchmark shows recent open-source video generators fail to preserve physical consistency under controlled changes to viewpoint, scene, object category, and appearance.
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CaST-Bench creates a benchmark with causal-chain annotations and novel metrics showing that current VLMs struggle to construct precise grounded causal chains in video QA.
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CaST-Bench: Benchmarking Causal Chain-Grounded Spatio-Temporal Reasoning for Video Question Answering
Introduces CaST-Bench, a dataset of 2,066 causal questions on 1,015 videos with annotated causal chains and metrics to evaluate VLMs on spatio-temporal causal reasoning.
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Cosmos 3: Omnimodal World Models for Physical AI
Cosmos 3 presents a unified omnimodal world model family based on mixture-of-transformers that processes language, vision, audio, and action for Physical AI applications.
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PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models
PhysEditWorld supplies 12 UE5 scenes, 60+ million frames, and explicit gravity labels via a replay paradigm to support gravity-faithful and physically editable world models.
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