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ARXIV:2603.12231 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.12231AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A novel approach to improve latent planning through temporal straightening of representations.
Opportunity summary
Pain A novel approach to improve latent planning through temporal straightening of representations.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A novel approach to improve latent planning through temporal straightening of representations. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even…
Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Inspired by the perceptual straightening hypothesis in human visual processing, we introduce temporal straightening to improve representation learning for latent planning.
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A novel approach to improve latent planning through temporal straightening of representations.
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Paper Pack
10.48550/arXiv.2603.12231A novel approach to improve latent planning through temporal straightening of representations.
Abstract
Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental -- to planning. Inspired by the perceptual straightening hypothesis in human visual processing, we introduce temporal straightening to improve representation learning for latent planning. Using a curvature regularizer that encourages locally straightened latent trajectories, we jointly learn an encoder and a predictor. We show that reducing curvature this way makes the Euclidean distance in latent space a better proxy for the geodesic distance and improves the conditioning of the planning objective. We demonstrate empirically that temporal straightening makes gradient-based planning more stable and yields significantly higher success rates across a suite of goal-reaching tasks.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
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Dimensions overall score 4.0
PROBLEM
A novel approach to improve latent planning through temporal straightening of representations. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental -- to planning.
METHOD
Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental -- to planning.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Inspired by the perceptual straightening hypothesis in human visual processing, we introduce temporal straightening to improve representation learning for latent planning.
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A novel approach to improve latent planning through temporal straightening of representations. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental -- to planning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental -- to planning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Inspired by the perceptual straightening hypothesis in human visual processing, we introduce temporal straightening to improve representation learning for latent planning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel approach to improve latent planning through temporal straightening of representations.
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Adoption evidence
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Commercial read
4.0/10 public viability
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status
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reason
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proof status
unverified
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confidence low
next verification path
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stale
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stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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