ARXIV:2603.12254 · VIDEO_UNDERSTANDING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

Q: What products could be built from this research?

This could be productized as a tool or API that integrates with existing video processing suites to enhance efficiency and capability, particularly useful for media companies and any enterprises dealing with large-scale video content processing.

Q: What are the practical use cases?

Implement AutoGaze in video surveillance systems to enhance real-time monitoring capabilities by focusing on vital changes and movements rather than processing entire frames, reducing the need for extensive computational resources.

Q: What industries could this research disrupt?

This could replace current video processing and analysis systems that inefficiently sweep through entire video frames, leading to higher costs and slower processing.

arXiv

AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content. We introduce AutoGaze, a lightweight module that removes redundant patches before processed by a ViT…

METHOD

Full abstract

Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos -- they process every pixel equally in their vision transformers (ViTs) or LLMs despite significant spatiotemporal redundancy. We introduce AutoGaze, a lightweight module that removes redundant patches before processed by a ViT or an MLLM. Trained with next-token prediction and reinforcement learning, AutoGaze autoregressively selects a minimal set of multi-scale patches that can reconstruct the video within a user-specified error threshold, eliminating redundancy while preserving information. Empirically, AutoGaze reduces visual tokens by 4x-100x and accelerates ViTs and MLLMs by up to 19x, enabling scaling MLLMs to 1K-frame 4K-resolution videos and achieving superior results on video benchmarks (e.g., 67.0% on VideoMME). Furthermore, we introduce HLVid: the first high-resolution, long-form video QA benchmark with 5-minute 4K-resolution videos, where an MLLM scaled with AutoGaze improves over the baseline by 10.1% and outperforms the previous best MLLM by 4.5%. Project page: https://autogaze.github.io/.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirically, AutoGaze reduces visual tokens by 4x-100x and accelerates ViTs and MLLMs by up to 19x, enabling scaling MLLMs to 1K-frame 4K-resolution videos and…

WHY NOW

video_understanding moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainAutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.12254 · VIDEO_UNDERSTANDING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

arXiv

AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

video_understanding moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainAutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.12254

Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 33% coverage.

What was readable

linkedon filenot materializedderived fallback100 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

video_understanding moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content. We introduce AutoGaze, a lightweight module that removes redundant patches before processed by a ViT or an MLLM.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos -- they process every pixel equally in their vision transformers (ViTs) or LLMs despite significant spatiotemporal redundancy. We introduce AutoGaze, a lightweight module that removes redundant patches before processed by a ViT or an MLLM.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirically, AutoGaze reduces visual tokens by 4x-100x and accelerates ViTs and MLLMs by up to 19x, enabling scaling MLLMs to 1K-frame 4K-resolution videos and achieving superior results on video benchmarks (e.g., 67.0% on VideoMME).
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
video_understanding moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

video_understanding

Competitors

not indexed

Competitive landscape

AutoGaze accelerates video processing by selectively attending to critical patches, enabling scalable and efficient video analysis for high-resolution content.

Segment

video_understanding

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.12254 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(100)

FOCUS: Efficient Keyframe Selection for Long Video Understanding

2025Zi-Xuan Zhu, Hailun Xu et al.

OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM

2025Hanrong Ye, Chao-Han Huck Yang et al.

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

2025Weiyun Wang, Zhangwei Gao et al.

VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding

2025Shihao Wang, Guo Chen et al.

Single-pass Adaptive Image Tokenization for Minimum Program Search

2025Shivam Duggal, Sanghyun Byun et al.

Scaling RL to Long Videos

2025Yukang Chen, Wei Huang et al.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

2025Gheorghe Comanici, Eric Bieber et al.

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

2025Mahmoud Assran, Adrien Bardes et al.

MR. Video: "MapReduce" is the Principle for Long Video Understanding

2025Ziqi Pang, Yu-Xiong Wang

Perception Encoder: The best visual embeddings are not at the output of the network

2025Daniel Bolya, Po-Yao Huang et al.

Re-thinking Temporal Search for Long-Form Video Understanding

2025Jinhui Ye, Zihan Wang et al.

Understanding R1-Zero-Like Training: A Critical Perspective

2025Zi-Yan Liu, Changyu Chen et al.

Scaling Vision Pre-Training to 4K Resolution

2025Baifeng Shi, Boyi Li et al.

Improving LLM Video Understanding with 16 Frames Per Second

2025Yixuan Li, Changli Tang et al.

FastVID: Dynamic Density Pruning for Fast Video Large Language Models

2025Leqi Shen, Guoqiang Gong et al.

STORM: Token-Efficient Long Video Understanding for Multimodal LLMs

2025Jindong Jiang, Xiuyu Li et al.

Adaptive Keyframe Sampling for Long Video Understanding

2025Xi Tang, Jihao Qiu et al.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

2025Michael Tschannen, Alexey Gritsenko et al.

Qwen2.5-VL Technical Report

2025Shuai Bai, Keqin Chen et al.

FlexTok: Resampling Images into 1D Token Sequences of Flexible Length

2025Roman Bachmann, Jesse Allardice et al.

Showing 20 of 100 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkVideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning

7.0

Prior WorkGazeQwen: Lightweight Gaze-Conditioned LLM Modulation for Streaming Video Understanding

7.0

Prior WorkSmall Vision-Language Models are Smart Compressors for Long Video Understanding

7.0

Prior WorkGroundVTS: Visual Token Sampling in Multimodal Large Language Models for Video Temporal Grounding

7.0

Extension

Builds On ThisProgressive Video Condensation with MLLM Agent for Long-form Video Understanding

5.0

Builds On ThisVideoAtlas: Navigating Long-Form Video in Logarithmic Compute

6.0

Builds On ThisWatch, Remember, Reason: Human-View Video Understanding with MLLMs

0.0

Builds On ThisGazeVLM: Active Vision via Internal Attention Control for Multimodal Reasoning

6.0

Builds On ThisSpava: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention

6.0

Commercially relevant

Higher ViabilityAdaptToken: Entropy-based Adaptive Token Selection for MLLM Long Video Understanding

8.0

Conflicting

none indexed

Related Resources

Why is real-time reasoning important in video understanding?(question)
Why is real-time reasoning important in video understanding?(question)
Why is real-time reasoning important in video understanding?(question)

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.12254
Route: /paper/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing
Active tab: read
Artifact: attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing/paper-pack
REST build passport API/api/v1/paper/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "paper_id": "01f73596-f417-4235-970b-17a7893bc12c",
  "arxiv_id": "2603.12254",
  "canonical_route": "/paper/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing",
  "endpoints": {
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    "build_passport": "/api/v1/paper/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

Canonical ID attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing | Route /paper/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.12254"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing",
  "normalized_query": "2603.12254",
  "route": "/paper/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing",
  "paper_ref": "attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

/buildability/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing

Watchwatch

Subject: Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing

Paper ref

attend-before-attention-efficient-and-scalable-video-understanding-via-autoregressive-gazing

arXiv id

2603.12254

Freshness

Generated at

2026-03-19T18:48:05.835Z

Evidence freshness

stale

Last verification

2026-03-19T18:48:05.835Z

Sources

References

Coverage

33%

Hash state

Lineage hash

54560945b58d1da0c11f5a0e1d465f04111f52f75954c3d54020ccce4032c747

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

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