ARXIV:2601.05230 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Learning Latent Action World Models In The Wild

arXiv

Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

Blocked on Code›Score2.0Evidence unverified

Opportunity summary

Pain Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks. While world models possess this capability, they most often require action labels, that can be complex to obtain at scale.

METHOD

Full abstract

Agents capable of reasoning and planning in the real world require the ability of predicting the consequences of their actions. While world models possess this capability, they most often require action labels, that can be complex to obtain at scale. This motivates the learning of latent action models, that can learn an action space from videos alone. Our work addresses the problem of learning latent actions world models on in-the-wild videos, expanding the scope of existing works that focus on simple robotics simulations, video games, or manipulation data. While this allows us to capture richer actions, it also introduces challenges stemming from the video diversity, such as environmental noise, or the lack of a common embodiment across videos. To address some of the challenges, we discuss properties that actions should follow as well as relevant architectural choices and evaluations. We find that continuous, but constrained, latent actions are able to capture the complexity of actions from in-the-wild videos, something that the common vector quantization does not. We for example find that changes in the environment coming from agents, such as humans entering the room, can be transferred across videos. This highlights the capability of learning actions that are specific to in-the-wild videos. In the absence of a common embodiment across videos, we are mainly able to learn latent actions that become localized in space, relative to the camera. Nonetheless, we are able to train a controller that maps known actions to latent ones, allowing us to use latent actions as a universal interface and solve planning tasks with our world model with similar performance as action-conditioned baselines. Our analyses and experiments provide a step towards scaling latent action models to the real world.

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our analyses and experiments provide a step towards scaling latent action models to the real world.

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 2.0/10.

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

Opportunity summary

Score2.0

PainDevelop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

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

ARXIV:2601.05230 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Learning Latent Action World Models In The Wild

arXiv

Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

Blocked on Code›Score2.0Evidence unverified

Opportunity summary

Pain Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

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 2.0/10 on the public viability pass. Our analyses and experiments provide a step towards scaling latent action models to the real world.

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 2.0/10.

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

Opportunity summary

Score2.0

PainDevelop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

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

Paper Pack

10.48550/arXiv.2601.05230

Learning Latent Action World Models In The Wild

Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Parse run linked

A document parse run is attached to this paper.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon file20 anchorsderived fallback75 indexednot indexed

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

Viability

2.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our analyses and experiments provide a step towards scaling latent action models to the real world.

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 2.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks. While world models possess this capability, they most often require action labels, that can be complex to obtain at scale.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Agents capable of reasoning and planning in the real world require the ability of predicting the consequences of their actions. While world models possess this capability, they most often require action labels, that can be complex to obtain at scale.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our analyses and experiments provide a step towards scaling latent action models to the real world.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Agents moved forward this cycle; last verified April 2026. Public score 2.0/10.
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 linkedDocument parse run

Markets

Agents

Competitors

not indexed

Competitive landscape

Develop latent action world models from diverse in-the-wild videos for improved real-world planning tasks.

Segment

Agents

Adoption evidence

No public code link in the paper record yet

Commercial read

2.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2601.05230 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(75)

Reference metadata pending (35fc38884dacfaf54e87d739fdb4104bc49b63e5)

Reference metadata pending (55f7cb10e86c49ee02d6a1abda2919306bb2affa)

Reference metadata pending (6eab7b59bac9246b577dcbbadd0ab0a230bf5432)

Reference metadata pending (5dd952114ea40a012c80c8494fea57a4ed4f9a0a)

Reference metadata pending (b202faf38efbffcb26470c702e1140b047d6f6e7)

Reference metadata pending (a457360428673433157dbc1d71d47359667a10d2)

Reference metadata pending (3400ad801cba5b356e15d346c6eb88ad95b7253f)

Reference metadata pending (f2025c3ceb4cf9c2034d62ea583d49a7ba356514)

Reference metadata pending (23ec5b0184fe48558f14401247a411d2a48bd75f)

Reference metadata pending (b44a7e1ffbe48e62c20d75ccac11e62764680f9d)

Reference metadata pending (1190c204564459b46171e2e179816deb40320769)

Reference metadata pending (d4dee189fd92a8ab065123645d67383fd7438e5c)

Reference metadata pending (b95baf93d1cfb406b50aa370ce5ba18788b5891d)

Reference metadata pending (d3c3777d72556d9bcc3b7937142336ee35e9a28c)

Reference metadata pending (8855ed01763594cd8b88989ff8b0fd1a40173e0c)

Reference metadata pending (af21819ea36a2522a4a49ceaf9ef8301683c5512)

Reference metadata pending (94fb3041f7fa85d889950c5f98848795a2b90cd6)

Reference metadata pending (9ca72cef4487494cd835e7abca65794407db62ee)

Reference metadata pending (badf5d00486111d936b056e52f13c26c1bbbd573)

Reference metadata pending (5e25c253b0c3fb159c9581131e22b6e9f615619c)

Showing 20 of 75 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 WorkLatent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challenges

2.0

Extension

Builds On ThisWorld-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis

0.0

Commercially relevant

Higher ViabilityOlaf-World: Orienting Latent Actions for Video World Modeling

6.0

Higher ViabilityLearning Visual Feature-Based World Models via Residual Latent Action

8.0

Higher ViabilityChain of World: World Model Thinking in Latent Motion

5.0

Higher ViabilityDiLA: Disentangled Latent Action World Models

4.0

Higher ViabilityDo World Action Models Generalize Better than VLAs? A Robustness Study

7.0

Higher ViabilityWorld2Act: Latent Action Post-Training via Skill-Compositional World Models

8.0

Higher ViabilityHierarchical Planning with Latent World Models

7.0

Higher ViabilityLatent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving

7.0

Conflicting

none indexed

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Owned Distribution

Subscribe to the weekly brief

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: 2601.05230
Route: /paper/learning-latent-action-world-models-in-the-wild
Active tab: read
Artifact: learning-latent-action-world-models-in-the-wild

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/learning-latent-action-world-models-in-the-wild/paper-pack
REST build passport API/api/v1/paper/learning-latent-action-world-models-in-the-wild/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": "377ace21-41b0-4a4a-840a-00ab03172720",
  "arxiv_id": "2601.05230",
  "canonical_route": "/paper/learning-latent-action-world-models-in-the-wild",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "learning-latent-action-world-models-in-the-wild",
  "endpoints": {
    "paper_pack": "/api/v1/paper/learning-latent-action-world-models-in-the-wild/paper-pack",
    "build_passport": "/api/v1/paper/learning-latent-action-world-models-in-the-wild/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/learning-latent-action-world-models-in-the-wild

stale

Proof freshness: stale
Proof status: unverified
Display score: 2/10
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

Learning Latent Action World Models In The Wild

Canonical ID learning-latent-action-world-models-in-the-wild | Route /paper/learning-latent-action-world-models-in-the-wild

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/learning-latent-action-world-models-in-the-wild

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2601.05230"
  }
}

source_context

{
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  "mode": "paper",
  "query": "Learning Latent Action World Models In The Wild",
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  "route": "/paper/learning-latent-action-world-models-in-the-wild",
  "paper_ref": "learning-latent-action-world-models-in-the-wild",
  "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

Not build-ready: Learning Latent Action World Models In The Wild

/buildability/learning-latent-action-world-models-in-the-wild

Ignoreblocked

Subject: Learning Latent Action World Models In The Wild

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

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/learning-latent-action-world-models-in-the-wild

Paper ref

learning-latent-action-world-models-in-the-wild

arXiv id

2601.05230

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

3ea60c1b9e42079f22b98f641ff21471d58f92e95c7be1ae90063f46a584b39e

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.

Source proof

Visual citation anchors from the paper document graph.

20 anchors

proof blockPage 482%

This equation captures one of the core mathematical components of the system. Var(Z·,d) V CM(Z) =λV 1 D X + λC 1 D(D − X i̸=j Cov(Z)2 i,j

Page and bbox are available; crop image is pending.

proof blockPage 482%

This equation captures one of the core mathematical components of the system. st+1 = f(s0:t, at). If an environment is stochastic, we

Page and bbox are available; crop image is pending.

proof blockPage 482%

This equation captures one of the core mathematical components of the system. d max 1 − q Var(Z·,d) V CM(Z) =λV 1 D X + λC 1 D(D − X

Page and bbox are available; crop image is pending.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Learning Latent Action World Models In The Wild

Learning Latent Action World Models In The Wild

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Scientific founder

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Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(75)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline