Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/learning-transferable-temporal-primitives-for-video-reasoning-via-synthetic-videos

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID learning-transferable-temporal-primitives-for-video-reasoning-via-synthetic-videos | Route /signal-canvas/learning-transferable-temporal-primitives-for-video-reasoning-via-synthetic-videos

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/learning-transferable-temporal-primitives-for-video-reasoning-via-synthetic-videos

MCP example

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    "paper_ref": "learning-transferable-temporal-primitives-for-video-reasoning-via-synthetic-videos",
    "query_text": "Summarize Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos"
  }
}

source_context

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  "query": "Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos",
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  "paper_ref": "learning-transferable-temporal-primitives-for-video-reasoning-via-synthetic-videos",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

PDF: https://arxiv.org/pdf/2603.17693v1

Repository: https://github.com/jiangsongtao/Synthetic-Video

Source count: Pending verification

Coverage: 50%

Last proof check: 2026-03-19T21:58:08.360Z

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Ready for execution: Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

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Subject: Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

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4/1/2026

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
existing datasets often lack temporal-centricity, where answers can be inferred from isolated keyframes rather than requiring holistic temporal integration
Implicationpartial
Directly stated in the abstract as a critical limitation of current methods
Verificationpartial
partial
Evidencepartial
training data generated by proprietary models contains systematic errors in fundamental temporal perception, such as confusing motion directions or misjudging speeds
Implicationpartial
Directly stated in the abstract as a specific limitation with clear examples
Verificationpartial
partial
Evidencepartial
SynRL, a post-training framework that teaches models temporal primitives, the fundamental building blocks of temporal understanding including direction, speed, and state tracking
Implicationpartial
Directly stated in the abstract as the core method and key insight
Verificationpartial
partial
Evidencepartial
We decompose temporal understanding into short-term perceptual primitives (speed, direction) and long-term cognitive primitives
Implicationpartial
Directly stated in the abstract as a specific methodological approach
Verificationpartial
partial
Evidencepartial
SynRL achieves substantial improvements across 15 benchmarks spanning temporal grounding, complex reasoning, and general video understanding
Implicationpartial
Directly stated in the abstract with specific scope (15 benchmarks) and domains
Verificationpartial
partial
Evidencepartial
our 7.7K synthetic CoT samples outperform Video-R1 with 165K real-world samples
Implicationpartial
Directly stated in the abstract with specific numeric comparison (7.7K vs 165K samples)
Verificationpartial
partial
Evidencepartial
video temporal learning through carefully designed synthetic data provides a more cost efficient scaling path
Implicationpartial
Directly stated in the abstract as the established paradigm, though 'more cost efficient' is comparative without explicit cost metrics
Verificationpartial
partial
Evidencepartial
fundamental temporal skills, such as tracking frame by frame changes and comparing velocity, that transfer effectively from abstract synthetic patterns to complex real-world scenarios
Implicationpartial
Directly stated in the abstract as the explanation for performance improvements
Verificationpartial
partial

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Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

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