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ARXIV:2605.21308 · VISION STATE SPACE MODELS · SUBMITTED 21 MAY · 20:31 UTC · FRESHNESS STALE
ARXIV:2605.21308VISION STATE SPACE MODELSSUBMITTED 21 MAY · 20:31 UTCFRESHNESS STALEHongyu Ke · Jack Morris · Yongkang Liu · Satoshi Kitai · Kentaro Oguchi · Yi Ding · +1 at arXiv
Deformba is a vision state space model that uses adaptive state fusion to improve performance on 2D and 3D vision tasks.
Opportunity summary
Pain Deformba is a vision state space model that uses adaptive state fusion to improve performance on 2D and 3D vision tasks.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
Deformba is a vision state space model that uses adaptive state fusion to improve performance on 2D and 3D vision tasks. However, their application to vision tasks remains challenging.
State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional sequence modeling capabilities. However, their application to vision tasks remains challenging.
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This is a result of the inherently causal and self-referential nature of SSMs designed for 1D sequence modeling tasks. A public repository is linked,…
Vision State Space Models moved forward this cycle; last verified May 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
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Score6.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Deformba is a vision state space model that uses adaptive state fusion to improve performance on 2D and 3D vision tasks.
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Paper Pack
10.48550/arXiv.2605.21308Deformba is a vision state space model that uses adaptive state fusion to improve performance on 2D and 3D vision tasks.
Abstract
State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional sequence modeling capabilities. However, their application to vision tasks remains challenging. First, existing vision SSMs largely depend on manually designed fixed scanning methods to flatten image patches into sequences, which imposes predefined geometric structures and increases the complexity. Second, the broader adoption of vision SSMs is hindered in domains that require query-based interactions between distinct information streams. This is a result of the inherently causal and self-referential nature of SSMs designed for 1D sequence modeling tasks. This fusion mechanism is indispensable for critical perception tasks such as multi-view 3D fusion. To address these limitations, we propose Deformba, a context adaptive method that dynamically augments the spatial structural information while maintaining the linear complexity of SSMs. Deformba also allows multi-modal fusion like cross attention. To demonstrate the effectiveness and general applicability of Deformba, we test its performance on general 2D vision tasks such as image classification, object detection, and segmentation, as well as 3D vision tasks like BEV perception. Extensive experiments show that Deformba achieves strong performance across various visual perception benchmarks.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
unverified0 refs; 4 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 6.0
PROBLEM
Deformba is a vision state space model that uses adaptive state fusion to improve performance on 2D and 3D vision tasks. However, their application to vision tasks remains challenging.
METHOD
State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional sequence modeling capabilities. However, their application to vision tasks remains challenging.
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This is a result of the inherently causal and self-referential nature of SSMs designed for 1D sequence modeling tasks. A public repository is linked, so build verification can inspect implementation evide...
WHY NOW
Vision State Space Models moved forward this cycle; last verified May 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
Deformba is a vision state space model that uses adaptive state fusion to improve performance on 2D and 3D vision tasks. However, their application to vision tasks remains challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional sequence modeling capabilities. However, their application to vision tasks remains challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This is a result of the inherently causal and self-referential nature of SSMs designed for 1D sequence modeling tasks. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision State Space Models moved forward this cycle; last verified May 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
Deformba is a vision state space model that uses adaptive state fusion to improve performance on 2D and 3D vision tasks.
Segment
Vision State Space Models
Adoption evidence
Public code linked for build inspection
Commercial read
6.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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Hacker News
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Bluesky
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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
Extension
Commercially relevant
Conflicting
Owned Distribution
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 4 sources / 67% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 4 sources, 67% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
No verified competitive landscape changes yet.
RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.