ARXIV:2603.09715 · VISION-LANGUAGE MODELS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

arXiv

CVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain CVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

CVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning. However, many samples can be solved via linguistic patterns or common-sense shortcuts, without genuine cross-modal…

METHOD

Full abstract

Visual instruction tuning is crucial for improving vision-language large models (VLLMs). However, many samples can be solved via linguistic patterns or common-sense shortcuts, without genuine cross-modal reasoning, limiting the effectiveness of multimodal learning. Prior data selection methods often rely on costly proxy model training and focus on difficulty or diversity, failing to capture a sample's true contribution to vision-language joint reasoning. In this paper, we propose CVS, a training-free data selection method based on the insight that, for high-quality multimodal samples, introducing the question should substantially alter the model's assessment of answer validity given an image. CVS leverages a frozen VLLM as an evaluator and measures the discrepancy in answer validity with and without conditioning on the question, enabling the identification of samples that require vision-language joint reasoning while filtering semantic-conflict noise. Experiments on Vision-Flan and The Cauldron show that CVS achieves solid performance across datasets. On Vision-Flan, CVS outperforms full-data training by 3.5% and 4.8% using only 10% and 15% of the data, respectively, and remains robust on the highly heterogeneous Cauldron dataset. Moreover, CVS reduces computational cost by 17.3% and 44.4% compared to COINCIDE and XMAS.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Experiments on Vision-Flan and The Cauldron show that CVS achieves solid performance across datasets.

WHY NOW

Vision-Language Models moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainCVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

CVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

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

ARXIV:2603.09715 · VISION-LANGUAGE MODELS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

arXiv

CVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain CVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Experiments on Vision-Flan and The Cauldron show that CVS achieves solid performance across datasets.

WHY NOW

Vision-Language Models moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainCVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

CVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

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

Paper Pack

10.48550/arXiv.2603.09715

Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

CVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

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 materialized8 extracted45 indexednot indexed

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

Viability

8.0

Time to MVP

MVP estimate missing

Commercial

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Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Experiments on Vision-Flan and The Cauldron show that CVS achieves solid performance across datasets.

WHY NOW

Vision-Language Models moved forward this cycle; last verified April 2026. Public score 8.0/10.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
we propose CVS, a training-free data selection method
Implicationpartial
The abstract explicitly states 'we propose CVS, a training-free data selection method'.
Verificationpartial
partial
Evidencepartial
measures the discrepancy in answer validity with and without conditioning on the question, enabling the identification of samples that require vision-language joint reasoning
Implicationpartial
The abstract clearly describes the core mechanism of CVS: 'measures the discrepancy in answer validity with and without conditioning on the question, enabling the identification of samples that require vision-language joint reasoning'.
Verificationpartial
partial
Evidencepartial
On Vision-Flan, CVS outperforms full-data training by 3.5% ... using only 10% of the data
Implicationpartial
This is a specific quantitative result directly stated in the abstract.
Verificationpartial
partial
Evidencepartial
and 4.8% using only 15% of the data, respectively
Implicationpartial
This is a specific quantitative result directly stated in the abstract.
Verificationpartial
partial
Evidencepartial
Moreover, CVS reduces computational cost by 17.3% and 44.4% compared to COINCIDE and XMAS.
Implicationpartial
This is a specific quantitative comparison of computational cost.
Verificationpartial
partial
Evidencepartial
Moreover, CVS reduces computational cost by 17.3% and 44.4% compared to COINCIDE and XMAS.
Implicationpartial
This is a specific quantitative comparison of computational cost.
Verificationpartial
partial
Evidencepartial
Prior data selection methods often rely on costly proxy model training
Implicationpartial
The abstract explicitly mentions this as a limitation of prior methods.
Verificationpartial
partial
Evidencepartial
and remains robust on the highly heterogeneous Cauldron dataset.
Implicationpartial
The abstract states that CVS 'remains robust on the highly heterogeneous Cauldron dataset'.
Verificationpartial
partial

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Methods

Materials

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Markets

Vision-Language Models

Competitors

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Competitive landscape

CVS is a training-free data selection method that enhances vision-language model performance by identifying samples requiring genuine cross-modal reasoning.

Segment

Vision-Language Models

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

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Buzz

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References(45)

Why Less is More (Sometimes): A Theory of Data Curation

2025Elvis Dohmatob, Mohammad Pezeshki et al.

CoIDO: Efficient Data Selection for Visual Instruction Tuning via Coupled Importance-Diversity Optimization

2025Yichen Yan, Ming Zhong et al.

Mitigating Modal Imbalance in Multimodal Reasoning

2025C. Wu, Neil Kale et al.

Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories

2025Nilay Naharas, Dang Nguyen et al.

A comprehensive survey of Vision-Language Models: Pretrained models, fine-tuning, prompt engineering, adapters, and benchmark datasets

2025Sufyan Danish, A. Sadeghi-Niaraki et al.

Eliminating language bias in visual question answering with potential causality models

2025Qiwen Lu, Shengbo Chen et al.

Learning to Instruct for Visual Instruction Tuning

2025Zhihan Zhou, Feng Hong et al.

MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuning

2025Yiwei Ma, Guohai Xu et al.

Can VLMs Actually See and Read? A Survey on Modality Collapse in Vision-Language Models

2025Mong Yuan Sim, Wei Emma Zhang et al.

ICONS: Influence Consensus for Vision-Language Data Selection

2024Xindi Wu, Mengzhou Xia et al.

Extract Free Dense Misalignment from CLIP

2024JeongYeon Nam, Jinbae Im et al.

Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

2024Peng Wang, Shuai Bai et al.

Zero-Shot Visual Reasoning by Vision-Language Models: Benchmarking and Analysis

2024Aishik Nagar, Shantanu Jaiswal et al.

Concept-skill Transferability-based Data Selection for Large Vision-Language Models

2024Jaewoo Lee, Boyang Albert Li et al.

VLind-Bench: Measuring Language Priors in Large Vision-Language Models

2024Kang-il Lee, Minbeom Kim et al.

Detecting hallucinations in large language models using semantic entropy

2024Sebastian Farquhar, Jannik Kossen et al.

What matters when building vision-language models?

2024Hugo Laurençon, Léo Tronchon et al.

Evaluating Text-to-Visual Generation with Image-to-Text Generation

2024Zhiqiu Lin, Deepak Pathak et al.

Are We on the Right Way for Evaluating Large Vision-Language Models?

2024Lin Chen, Jinsong Li et al.

Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection

2024Ruibo Chen, Yihan Wu 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 WorkScalSelect: Scalable Training-Free Multimodal Data Selection for Efficient Visual Instruction Tuning

8.0

Extension

Builds On ThisVisionFoundry: Teaching VLMs Visual Perception with Synthetic Images

6.0

Builds On ThisSIEVES: Selective Prediction Generalizes through Visual Evidence Scoring

7.0

Builds On ThisVisualNeedle: Benchmarking Active Visual Search in Information-Dense Scenes

7.0

Builds On ThisWatch Before You Answer: Learning from Visually Grounded Post-Training

7.0

Builds On ThisCVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception

7.0

Builds On ThisThrough the Lens of Contrast: Self-Improving Visual Reasoning in VLMs

7.0

Commercially relevant

none indexed

Conflicting

Competing ApproachBalanced Thinking: Improving Chain of Thought Training in Vision Language Models

7.0

Competing ApproachVision Verification Enhanced Fusion of VLMs for Efficient Visual Reasoning

8.0

Competing ApproachDiscovering Failure Modes in Vision-Language Models using RL

7.0

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Paper: 2603.09715
Route: /paper/does-the-question-really-matter-training-free-data-selection-for-vision-language-sft
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REST build passport API/api/v1/paper/does-the-question-really-matter-training-free-data-selection-for-vision-language-sft/build-passport
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Tool contracts

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Payload preview

Inspect payload

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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

Canonical route: /paper/does-the-question-really-matter-training-free-data-selection-for-vision-language-sft

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
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

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Agent Handoff

Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

Canonical ID does-the-question-really-matter-training-free-data-selection-for-vision-language-sft | Route /paper/does-the-question-really-matter-training-free-data-selection-for-vision-language-sft

REST example

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MCP example

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source_context

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Buildability Receipt

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Paper proof page receipt window

Watch and verify: Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

/buildability/does-the-question-really-matter-training-free-data-selection-for-vision-language-sft

Watchwatch

Subject: Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

Verdict

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Compute envelope

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Evidence ids

Receipt path

/buildability/does-the-question-really-matter-training-free-data-selection-for-vision-language-sft

Paper ref

does-the-question-really-matter-training-free-data-selection-for-vision-language-sft

arXiv id

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Freshness

Generated at

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Evidence freshness

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Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

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Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(45)

Related Papers

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Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline