ARXIV:2602.15257 · DOCUMENT-UNDERSTANDING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

How to Train Your Long-Context Visual Document Model

Q: What products could be built from this research?

Leverage the open-source synthetic data pipelines and training recipes provided to rapidly develop a robust API or enterprise software that enables advanced document understanding and question answering using visual inputs.

Q: What are the practical use cases?

Develop a cloud-based service for enterprises to automate processing and querying massive datasets of complex documents such as legal contracts, academic papers, or policy documents, improving workflow efficiency in document-heavy industries.

Q: What industries could this research disrupt?

Could potentially replace less efficient text-to-text document analysis solutions that struggle with information loss due to format conversion, offering a more comprehensive solution that directly processes visual document formats like PDFs.

arXiv

Build a high-performance API for visual document question answering with long-context capabilities.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Build a high-performance API for visual document question answering with long-context capabilities.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Build a high-performance API for visual document question answering with long-context capabilities. While several such strong are open-weight, namely Qwen3 VL and GLM 4.5/6V, their training recipes and data pipelines are not reproducible.

METHOD

Full abstract

We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual question answering with measured transfer to long-context text. While several such strong are open-weight, namely Qwen3 VL and GLM 4.5/6V, their training recipes and data pipelines are not reproducible. We systematically study continued pretraining, supervised finetuning, and preference optimization for 24B and 32B parameter models, backed by extensive LC evaluations and ablations to bridge this gap, and achieve state-of-the-art performance on MMLongBenchDoc for both parameter scales. In addition to this, our key findings include: (i) training on context lengths that match evaluation context lengths outperforms training on longer contexts, (ii) training and evaluating with page indices provides a simple, high-impact boost to long-document performance, (iii) our synthetic data pipelines enable self-improvement via continued pretraining and supervised finetuning, and (iv) we extend the known text-to-visual long context transfer to the reverse, showing that visual long context training transfers to long-context text performance. We also release MMLBD-C, a manually corrected version of MMLongBenchDoc to reduce erroneous and low quality examples in the benchmark.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. We systematically study continued pretraining, supervised finetuning, and preference optimization for 24B and 32B parameter models, backed by extensive LC evaluations and ablations to…

WHY NOW

document-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

PainBuild a high-performance API for visual document question answering with long-context capabilities.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Build a high-performance API for visual document question answering with long-context capabilities.

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

ARXIV:2602.15257 · DOCUMENT-UNDERSTANDING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

How to Train Your Long-Context Visual Document Model

arXiv

Build a high-performance API for visual document question answering with long-context capabilities.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Build a high-performance API for visual document question answering with long-context capabilities.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

document-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

PainBuild a high-performance API for visual document question answering with long-context capabilities.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Build a high-performance API for visual document question answering with long-context capabilities.

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

Paper Pack

10.48550/arXiv.2602.15257

How to Train Your Long-Context Visual Document Model

Build a high-performance API for visual document question answering with long-context capabilities.

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; 17% coverage.

What was readable

linkedon filenot materializedderived fallback45 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

document-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
Build a high-performance API for visual document question answering with long-context capabilities. While several such strong are open-weight, namely Qwen3 VL and GLM 4.5/6V, their training recipes and data pipelines are not reproducible.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual question answering with measured transfer to long-context text. While several such strong are open-weight, namely Qwen3 VL and GLM 4.5/6V, their training recipes and data pipelines are not reproducible.
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. We systematically study continued pretraining, supervised finetuning, and preference optimization for 24B and 32B parameter models, backed by extensive LC evaluations and ablations to bridge this gap, and achieve state-of-the-art performance on MMLongBenchDoc for both parameter scales.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
document-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

document-understanding

Competitors

not indexed

Competitive landscape

Build a high-performance API for visual document question answering with long-context capabilities.

Segment

document-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 2602.15257 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

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

References(45)

InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models

2025Hongyuan Tao, Bencheng Liao et al.

Luth: Efficient French Specialization for Small Language Models and Cross-Lingual Transfer

2025Maxence Lasbordes, Sinou'e Gad

Temporal Chain of Thought: Long-Video Understanding by Thinking in Frames

2025Anurag Arnab, Ahmet Iscen et al.

GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

2025GLM-V Team Wenyi Hong, Wenmeng Yu et al.

SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization

2025Huashan Sun, Shengyi Liao et al.

MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly

2025Zhaowei Wang, Wenhao Yu et al.

BOLT: Boost Large Vision-Language Model Without Training for Long-Form Video Understanding

2025Shuming Liu, Chen Zhao et al.

WildLong: Synthesizing Realistic Long-Context Instruction Data at Scale

2025Jiaxi Li, Xingxing Zhang et al.

LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization

2025Guanzheng Chen, Xin Li et al.

Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuracy

2025Yunhang Shen, Chaoyou Fu et al.

The Best Instruction-Tuning Data are Those That Fit

2025Dylan Zhang, Qirun Dai et al.

NExtLong: Toward Effective Long-Context Training without Long Documents

2025Chaochen Gao, Xing Wu et al.

LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks

2024Yushi Bai, Shangqing Tu et al.

V2PE: Improving Multimodal Long-Context Capability of Vision-Language Models with Variable Visual Position Encoding

2024Junqi Ge, Ziyi Chen et al.

Self-Preference Bias in LLM-as-a-Judge

2024Koki Wataoka, Tsubasa Takahashi et al.

How to Train Long-Context Language Models (Effectively)

2024Tianyu Gao, Alexander Wettig et al.

HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly

2024Howard Yen, Tianyu Gao et al.

MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

2024Xiang Yue, Tianyu Zheng et al.

VLMEvalKit: An Open-Source ToolKit for Evaluating Large Multi-Modality Models

2024Haodong Duan, Junming Yang et al.

SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers

2024Shraman Pramanick, R. Chellappa et al.

Showing 20 of 45 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 WorkParallel In-context Learning for Large Vision Language Models

7.0

Prior WorkContext Training with Active Information Seeking

7.0

Extension

Builds On ThisLong Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization

5.0

Builds On ThisStacked from One: Multi-Scale Self-Injection for Context Window Extension

6.0

Builds On ThisMMCL-Bench: Multimodal Context Learning from Visual Rules, Procedures, and Evidence

3.0

Builds On ThisRetrieval and Multi-Hop Reasoning in 1M-Token Context Windows: Evaluating LLMs on Classical Chinese Text

4.0

Builds On ThisA Decomposition Perspective to Long-context Reasoning for LLMs

4.0

Builds On ThisEmergence of Context Characteristics Sensitivity in Large Language Models

0.0

Commercially relevant

Higher ViabilityContexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets

8.0

Higher ViabilityDocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding

8.0

Conflicting

none indexed

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: 2602.15257
Route: /paper/how-to-train-your-long-context-visual-document-model
Active tab: read
Artifact: how-to-train-your-long-context-visual-document-model

Available agents

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

API/MCP endpoints

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REST build passport API/api/v1/paper/how-to-train-your-long-context-visual-document-model/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

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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

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stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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

How to Train Your Long-Context Visual Document Model

Canonical ID how-to-train-your-long-context-visual-document-model | Route /paper/how-to-train-your-long-context-visual-document-model

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/how-to-train-your-long-context-visual-document-model

MCP example

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source_context

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Buildability Receipt

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

Paper proof page receipt window

Watch and verify: How to Train Your Long-Context Visual Document Model

/buildability/how-to-train-your-long-context-visual-document-model

Watchwatch

Subject: How to Train Your Long-Context Visual Document Model

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/how-to-train-your-long-context-visual-document-model

Paper ref

how-to-train-your-long-context-visual-document-model

arXiv id

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Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

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Sources

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Coverage

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Hash state

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Canonical opportunity-kernel lineage hash.

Signature state

External signature

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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: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded 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.

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How to Train Your Long-Context Visual Document Model

How to Train Your Long-Context Visual Document Model

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