ARXIV:2603.05438 · WORLD MODEL OPTIMIZATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model

arXiv

Develop a compact tokenizer to enhance real-time planning efficiency in world models.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Develop a compact tokenizer to enhance real-time planning efficiency in world models.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a compact tokenizer to enhance real-time planning efficiency in world models. Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control.

METHOD

Full abstract

World models provide a powerful framework for simulating environment dynamics conditioned on actions or instructions, enabling downstream tasks such as action planning or policy learning. Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control. A key bottleneck lies in latent representations: conventional tokenizers encode each observation into hundreds of tokens, making planning both slow and resource-intensive. To address this, we propose CompACT, a discrete tokenizer that compresses each observation into as few as 8 tokens, drastically reducing computational cost while preserving essential information for planning. An action-conditioned world model that occupies CompACT tokenizer achieves competitive planning performance with orders-of-magnitude faster planning, offering a practical step toward real-world deployment of world models.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. An action-conditioned world model that occupies CompACT tokenizer achieves competitive planning performance with orders-of-magnitude faster planning, offering a practical step toward real-world deployment of…

WHY NOW

World Model Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.

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

Opportunity summary

Score3.0

PainDevelop a compact tokenizer to enhance real-time planning efficiency in world models.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Develop a compact tokenizer to enhance real-time planning efficiency in world models.

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

ARXIV:2603.05438 · WORLD MODEL OPTIMIZATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model

arXiv

Develop a compact tokenizer to enhance real-time planning efficiency in world models.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Develop a compact tokenizer to enhance real-time planning efficiency in world models.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

World Model Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.

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

Opportunity summary

Score3.0

PainDevelop a compact tokenizer to enhance real-time planning efficiency in world models.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Develop a compact tokenizer to enhance real-time planning efficiency in world models.

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

Paper Pack

10.48550/arXiv.2603.05438

Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model

Develop a compact tokenizer to enhance real-time planning efficiency in world models.

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; 33% coverage.

What was readable

linkedon filenot materializedderived fallback77 indexednot indexed

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

Viability

3.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

World Model Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop a compact tokenizer to enhance real-time planning efficiency in world models. Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
World models provide a powerful framework for simulating environment dynamics conditioned on actions or instructions, enabling downstream tasks such as action planning or policy learning. Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. An action-conditioned world model that occupies CompACT tokenizer achieves competitive planning performance with orders-of-magnitude faster planning, offering a practical step toward real-world deployment of world models.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
World Model Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
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

World Model Optimization

Competitors

not indexed

Competitive landscape

Develop a compact tokenizer to enhance real-time planning efficiency in world models.

Segment

World Model Optimization

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.05438 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(77)

One Layer Is Enough: Adapting Pretrained Visual Encoders for Image Generation

2025Yuan Gao, Chen Chen et al.

Diffusion Transformers with Representation Autoencoders

2025Boyang Zheng, Nanye Ma et al.

NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environments

2025Xuan Yao, Junyu Gao et al.

DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation

2025Guosheng Zhao, Xiaofeng Wang et al.

WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation

2025Dujun Nie, Xianda Guo et al.

Discrete Codebook World Models for Continuous Control

2025Aidan Scannell, Mohammadreza Nakhaei et al.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

2025Michael Tschannen, Alexey Gritsenko et al.

FlexTok: Resampling Images into 1D Token Sequences of Flexible Length

2025Roman Bachmann, Jesse Allardice et al.

One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression

2025Keita Miwa, Kento Sasaki et al.

Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens

2025Dongwon Kim, Ju He et al.

Navigation World Models

2024Amir Bar, Gaoyue Zhou et al.

DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

2024Gaoyue Zhou, Hengkai Pan et al.

Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens

2024Lijie Fan, Tianhong Li et al.

HART: Efficient Visual Generation with Hybrid Autoregressive Transformer

2024Haotian Tang, Yecheng Wu et al.

MaskBit: Embedding-free Image Generation via Bit Tokens

2024Mark Weber, Lijun Yu et al.

Diffusion Models Are Real-Time Game Engines

2024Dani Valevski, Yaniv Leviathan et al.

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

2024Boyuan Chen, Diego Marti Monso et al.

Efficient World Models with Context-Aware Tokenization

2024Vincent Micheli, Eloi Alonso et al.

Autoregressive Image Generation without Vector Quantization

2024Tianhong Li, Yonglong Tian et al.

An Image is Worth 32 Tokens for Reconstruction and Generation

2024Qihang Yu, Mark Weber et al.

Showing 20 of 77 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

Builds On ThisPlanning-aligned Token Compression for Long-Context Autonomous Driving

0.0

Builds On ThisBack to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations

0.0

Builds On ThisATM: Action-Consistency Transfer Matrix for Diagnosing and Improving Latent World Models

0.0

Builds On ThisLearning Latent Action World Models In The Wild

2.0

Builds On ThisLatent Planning Emerges with Scale

1.0

Builds On ThisLatent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challenges

2.0

Commercially relevant

Higher ViabilityA Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens

7.0

Higher ViabilityBeyond Imitation: Reinforcement Learning for Active Latent Planning

5.0

Higher ViabilityExploring Token-Space Manipulation in Latent Audio Tokenizers

4.0

Higher ViabilityCoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving

7.0

Conflicting

none indexed

Owned Distribution

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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: 2603.05438
Route: /paper/planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model
Active tab: read
Artifact: planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model/paper-pack
REST build passport API/api/v1/paper/planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model/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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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/planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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

Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model

Canonical ID planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model | Route /paper/planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model

MCP example

{
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  }
}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Not build-ready: Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model

/buildability/planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model

Ignoreblocked

Subject: Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model

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/planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model

Paper ref

planning-in-8-tokens-a-compact-discrete-tokenizer-for-latent-world-model

arXiv id

2603.05438

Freshness

Generated at

2026-03-19T18:48:05.835Z

Evidence freshness

stale

Last verification

2026-03-19T18:48:05.835Z

Sources

References

Coverage

33%

Hash state

Lineage hash

9f08b57f5fe7328e4fe0b02b3de3e9d70af4748583f8ccd092a02911c20994fe

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: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed 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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Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model

Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model

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Brief

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Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline