MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality

MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality | Signal Canvas | ScienceToStartup

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Signal Canvas proof surface

Canonical route: /signal-canvas/must-modality-specific-representation-aware-transformer-for-diffusion-enhanced-survival-prediction-with-missing-modality

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 60
Source count: 3
Coverage: 50%

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

Agent Handoff

Canonical ID must-modality-specific-representation-aware-transformer-for-diffusion-enhanced-survival-prediction-with-missing-modality | Route /signal-canvas/must-modality-specific-representation-aware-transformer-for-diffusion-enhanced-survival-prediction-with-missing-modality

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/must-modality-specific-representation-aware-transformer-for-diffusion-enhanced-survival-prediction-with-missing-modality

MCP example

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    "paper_ref": "must-modality-specific-representation-aware-transformer-for-diffusion-enhanced-survival-prediction-with-missing-modality",
    "query_text": "Summarize MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality"
  }
}

source_context

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  "query": "MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality",
  "normalized_query": "2603.26071",
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  "paper_ref": "must-modality-specific-representation-aware-transformer-for-diffusion-enhanced-survival-prediction-with-missing-modality",
  "topic_slug": null,
  "benchmark_ref": null,
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}

Evidence Receipt

Route status: building

Claims: 12

References: 60

Proof: Verification pending

Freshness state: computing

Source paper: MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality

PDF: https://arxiv.org/pdf/2603.26071v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:55:13.285Z

Signal Canvas receipt window

Watch and verify: MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality

/buildability/must-modality-specific-representation-aware-transformer-for-diffusion-enhanced-survival-prediction-with-missing-modality

Watchwatch

Subject: MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality

Verdict

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Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
We propose MUST (Modality-Specific representation-aware Transformer), a novel framework that explicitly decomposes each modality's representation into modality-specific and cross-modal contextualized components through algebraic constraints in a learned low-rank shared subspace.
Implicationpartial
This is a core technical contribution explicitly stated in the abstract and elaborated in the method section.
Verificationpartial
partial
Evidencepartial
Extensive experiments on five TCGA cancer datasets demonstrate that MUST achieves state-of-the-art performance with complete data (C-index: 0.742) while maintaining robust predictions under missing genomics (0.716) and missing pathology (0.739), substantially outperforming existing methods in all scenarios.
Implicationpartial
The abstract and results section explicitly state this achievement, supported by a table of C-index values.
Verificationpartial
partial
Evidencepartial
Extensive experiments on five TCGA cancer datasets demonstrate that MUST achieves state-of-the-art performance with complete data while maintaining robust predictions in both missing pathology and missing genomics conditions, with clinically acceptable inference latency.
Implicationpartial
This is a key performance claim for handling missing modalities, explicitly stated in the abstract and supported by numerical results.
Verificationpartial
partial
Evidencepartial
For the truly modality-specific information that cannot be inferred from available modalities, we employ conditional latent diffusion models to generate high-quality representations conditioned on recovered shared information and learned structural priors.
Implicationpartial
This describes a specific technical component of the MUST framework for handling missing data, as detailed in the abstract and method section.
Verificationpartial
partial
Evidencepartial
Extensive experiments on five TCGA cancer datasets demonstrate that MUST achieves state-of-the-art performance with complete data (C-index: 0.742) while maintaining robust predictions under missing genomics (0.716) and missing pathology (0.739), substantially outperforming existing methods in all scenarios.
Implicationpartial
Specific numerical result provided in the abstract and results section, directly comparing performance.
Verificationpartial
partial
Evidencepartial
Extensive experiments on five TCGA cancer datasets demonstrate that MUST achieves state-of-the-art performance with complete data (C-index: 0.742) while maintaining robust predictions under missing genomics (0.716) and missing pathology (0.739), substantially outperforming existing methods in all scenarios.
Implicationpartial
Specific numerical result provided in the abstract and results section, demonstrating performance with missing data.
Verificationpartial
partial
Evidencepartial
Extensive experiments on five TCGA cancer datasets demonstrate that MUST achieves state-of-the-art performance with complete data (C-index: 0.742) while maintaining robust predictions under missing genomics (0.716) and missing pathology (0.739), substantially outperforming existing methods in all scenarios.
Implicationpartial
Specific numerical result provided in the abstract and results section, demonstrating performance with missing data.
Verificationpartial
partial
Evidencepartial
Extensive experiments on five TCGA cancer datasets demonstrate that MUST achieves state-of-the-art performance with complete data while maintaining robust predictions in both missing pathology and missing genomics conditions, with clinically acceptable inference latency.
Implicationpartial
This is a practical performance characteristic mentioned in the abstract, indicating its suitability for clinical use.
Verificationpartial
partial
Evidencepartial
We propose MUST (Modality-Specific representation-aware Transformer), a novel framework that explicitly decomposes each modality's representation into modality-specific and cross-modal contextualized components through algebraic constraints in a learned low-rank shared subspace.
Implicationpartial
This is a core technical contribution explicitly described in the abstract and elaborated in the method section.
Verificationpartial
partial
Evidencepartial
Extensive experiments on five TCGA cancer datasets demonstrate that MUST achieves state-of-the-art performance with complete data (C-index: 0.742)
Implicationpartial
The abstract and results section explicitly state this achievement, supported by a performance table.
Verificationpartial
partial
Evidencepartial
while maintaining robust predictions in both missing pathology and missing genomics conditions, with clinically acceptable inference latency.
Implicationpartial
This result is explicitly stated in the abstract and supported by the performance table.
Verificationpartial
partial
Evidencepartial
Extensive experiments on five TCGA cancer datasets demonstrate that MUST achieves state-of-the-art performance with complete data (C-index: 0.742) while maintaining robust predictions in both missing pathology (0.739) and missing genomics (0.716)
Implicationpartial
This result is explicitly stated in the abstract and supported by the performance table.
Verificationpartial
partial

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MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality

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