ARXIV:2602.24138 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics

arXiv

Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods. Recent approaches increasingly rely on large-scale pre-training on thousands of labeled surgical videos,…

METHOD

Full abstract

Recognizing surgical phases and steps from video is a fundamental problem in computer-assisted interventions. Recent approaches increasingly rely on large-scale pre-training on thousands of labeled surgical videos, followed by zero-shot transfer to specific procedures. While effective, this strategy incurs substantial computational and data collection costs. In this work, we question whether such heavy pre-training is truly necessary. We propose Text-Augmented Action Segmentation Optimal Transport (TASOT), an unsupervised method for surgical phase and step recognition that extends Action Segmentation Optimal Transport (ASOT) by incorporating textual information generated directly from the videos. TASOT formulates temporal action segmentation as a multimodal optimal transport problem, where the matching cost is defined as a weighted combination of visual and text-based costs. The visual term captures frame-level appearance similarity, while the text term provides complementary semantic cues, and both are jointly regularized through a temporally consistent unbalanced Gromov-Wasserstein formulation. This design enables effective alignment between video frames and surgical actions without surgical-specific pretraining or external web-scale supervision. We evaluate TASOT on multiple benchmark surgical datasets and observe consistent and substantial improvements over existing zero-shot methods, including StrasBypass70 (+23.7), BernBypass70 (+4.5), Cholec80 (+16.5), and AutoLaparo (+19.6). These results demonstrate that fine-grained surgical understanding can be achieved by exploiting information already present in standard visual and textual representations, without resorting to increasingly complex pre-training pipelines. The code will be available at https://github.com/omar8ahmed9/TASOT.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. This design enables effective alignment between video frames and surgical actions without surgical-specific pretraining or external web-scale supervision.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.

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

Opportunity summary

Score6.0

PainDevelop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

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

ARXIV:2602.24138 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics

arXiv

Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.

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

Opportunity summary

Score6.0

PainDevelop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

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

Paper Pack

10.48550/arXiv.2602.24138

Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics

Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

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 fallback24 indexednot indexed

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

Viability

6.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods. Recent approaches increasingly rely on large-scale pre-training on thousands of labeled surgical videos, followed by zero-shot transfer to specific procedures.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Recognizing surgical phases and steps from video is a fundamental problem in computer-assisted interventions. Recent approaches increasingly rely on large-scale pre-training on thousands of labeled surgical videos, followed by zero-shot transfer to specific procedures.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This design enables effective alignment between video frames and surgical actions without surgical-specific pretraining or external web-scale supervision.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.
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

Medical AI

Competitors

not indexed

Competitive landscape

Develop an unsupervised tool for recognizing surgical phases from video using multimodal optimal transport with significant performance improvements over existing methods.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.24138 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(24)

VL-JEPA: Joint Embedding Predictive Architecture for Vision-language

2025Delong Chen, Mustafa Shukor et al.

DINOv3

2025Oriane Sim'eoni, Huy V. Vo et al.

ARTEMIS: animal recognition through enhanced multimodal integration system

2025Edoardo Fazzari, Donato Romano et al.

Hierarchical Vector Quantization for Unsupervised Action Segmentation

2024Federico Spurio, Emad Bahrami et al.

Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation

2024Kun Yuan, V. Srivastav et al.

HecVL: Hierarchical Video-Language Pretraining for Zero-shot Surgical Phase Recognition

2024Kun Yuan, V. Srivastav et al.

Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation

2024Ming Xu, Stephen Gould

Gemma: Open Models Based on Gemini Research and Technology

2024Gemma Team Thomas Mesnard, Cassidy Hardin et al.

Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery

2023Joël L. Lavanchy, Sanat Ramesh et al.

Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures

2023Kun Yuan, V. Srivastav et al.

Acquisition and usage of robotic surgical data for machine learning analysis

2023Nasseh Hashemi, M. Svendsen et al.

LoViT: Long Video Transformer for surgical phase recognition

2023Yang Liu, Maxence Boels et al.

Temporal Action Segmentation: An Analysis of Modern Techniques

2022Guodong Ding, F. Sener et al.

AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy

2022Ziyi Wang, Bo Lu et al.

Unsupervised Discriminative Embedding For Sub-Action Learning in Complex Activities

2021Sirnam Swetha, Hilde Kuehne et al.

Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation

2021M. Sarfraz, Naila Murray et al.

Learning Transferable Visual Models From Natural Language Supervision

2021Alec Radford, Jong Wook Kim et al.

Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures

2021Sanat Ramesh, D. Dall’Alba et al.

TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks

2020Tobias Czempiel, Magdalini Paschali et al.

Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences

2020Rosaura G. VidalMata, W. Scheirer et al.

Showing 20 of 24 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

none indexed

Extension

none indexed

Commercially relevant

Higher ViabilityTowards Unified Surgical Scene Understanding:Bridging Reasoning and Grounding via MLLMs

8.0

Higher ViabilityTraining-free Temporal Object Tracking in Surgical Videos

7.0

Higher ViabilityAn Approach to Enriching Surgical Video Datasets for Fine-Grained Spatial-Temporal Understanding of Vision-Language Models

7.0

Higher ViabilitySurgCUT3R: Surgical Scene-Aware Continuous Understanding of Temporal 3D Representation

7.0

Higher ViabilitySurgPhase: Time efficient pituitary tumor surgery phase recognition via an interactive web platform

8.0

Higher ViabilitySurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Understanding

8.0

Higher ViabilityCliPPER: Contextual Video-Language Pretraining on Long-form Intraoperative Surgical Procedures for Event Recognition

7.0

Higher ViabilitySAW: Toward a Surgical Action World Model via Controllable and Scalable Video Generation

7.0

Higher ViabilitySurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition

8.0

Conflicting

Competing ApproachSurgAtt-Tracker: Online Surgical Attention Tracking via Temporal Proposal Reranking and Motion-Aware Refinement

5.0

Related Resources

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Owned Distribution

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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: 2602.24138
Route: /paper/multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics
Active tab: read
Artifact: multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics/paper-pack
REST build passport API/api/v1/paper/multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics/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": "9793f3a5-72c5-4d60-9d0a-122e520bd4b9",
  "arxiv_id": "2602.24138",
  "canonical_route": "/paper/multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics",
  "endpoints": {
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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/multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics

stale

Proof freshness: stale
Proof status: unverified
Display score: 6/10
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

Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics

Canonical ID multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics | Route /paper/multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.24138"
  }
}

source_context

{
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  "mode": "paper",
  "query": "Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics",
  "normalized_query": "2602.24138",
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  "paper_ref": "multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics",
  "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: Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics

/buildability/multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics

Watchwatch

Subject: Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics

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/multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics

Paper ref

multimodal-optimal-transport-for-unsupervised-temporal-segmentation-in-surgical-robotics

arXiv id

2602.24138

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

01c9b75967f7a2c79eae374447c94e7311bfc85cfab3bd7d3e0a65d7ed8e9e5a

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: 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.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics

Multimodal Optimal Transport for Unsupervised Temporal Segmentation in Surgical Robotics

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