HieraVid: Hierarchical Token Pruning for Fast Video Large Language Models

HieraVid: Hierarchical Token Pruning for Fast Video Large Language Models | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/hieravid-hierarchical-token-pruning-for-fast-video-large-language-models

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

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Canonical ID hieravid-hierarchical-token-pruning-for-fast-video-large-language-models | Route /signal-canvas/hieravid-hierarchical-token-pruning-for-fast-video-large-language-models

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MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: HieraVid: Hierarchical Token Pruning for Fast Video Large Language Models

PDF: https://arxiv.org/pdf/2604.01881v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.576Z

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Watch and verify: HieraVid: Hierarchical Token Pruning for Fast Video Large Language Models

/buildability/hieravid-hierarchical-token-pruning-for-fast-video-large-language-models

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Subject: HieraVid: Hierarchical Token Pruning for Fast Video Large Language Models

Verdict

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Remarkably, with only 30% of tokens retained, HieraVid achieves new state-of-the-art performance
Implicationpartial
Explicitly stated in abstract with specific numeric performance metrics
Verificationpartial
partial
Evidencepartial
while maintaining over 98% and 99% of the performance of LLaVA-Video-7B and LLaVA-OneVision-7B, respectively
Implicationpartial
Direct numeric claim stated in abstract with specific percentage
Verificationpartial
partial
Evidencepartial
while maintaining over 98% and 99% of the performance of LLaVA-Video-7B and LLaVA-OneVision-7B, respectively
Implicationpartial
Direct numeric claim stated in abstract with specific percentage
Verificationpartial
partial
Evidencepartial
we decompose pruning into three levels: 1) segment-level, where video tokens are first temporally segmented and spatially merged; 2) frame-level, where similar frames within the same segment are jointly pruned to preserve diversity; 3) layer-level, redundancy gradually shrinks as LLM layer increases
Implicationpartial
Explicitly described in abstract with clear technical details
Verificationpartial
partial
Evidencepartial
Existing methods mainly prune video tokens at input level while neglecting the inherent information structure embedded in videos and large language models (LLMs)
Implicationpartial
Directly stated as motivation for the proposed method
Verificationpartial
partial
Evidencepartial
we propose HieraVid, a hierarchical pruning framework that progressively and dynamically reduces visual redundancy
Implicationpartial
Directly stated as core functionality of the proposed method
Verificationpartial
partial
Evidencepartial
Based on two observations that videos possess the segment-frame structure and LLMs internally propagate multi-modal information unidirectionally
Implicationpartial
Directly stated as foundational observations for the method design
Verificationpartial
partial
Evidencepartial
We conduct extensive experiments on four widely used video understanding benchmarks to comprehensively evaluate the effectiveness of HieraVid
Implicationpartial
Explicitly stated in abstract with clear experimental scope
Verificationpartial
partial

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HieraVid: Hierarchical Token Pruning for Fast Video Large Language Models

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