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Paper Citation Record · LEDGER

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2608.04612.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.04612 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:50:11.933800Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

  • verified exact1
  • verified fuzzy8
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 411f3068-df21-4009-9480-bf4cf1642d93 · outbound

This paper cites Progressive Learning for Physics-informed Neural Motion Planning.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Progressive Learning for Physics-informed Neural Motion Planning

Reference 1

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no resolver link, observed 2026-08-06T20:50:10.903679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:10.903679Z digest=sha256:536df070fc96714e10b1e6fea07f7d1b32e7db00e659814b15bd0ff4c08216f4

Observation 18a3a329-ca76-4c04-b7d4-04026e9ad2b1 · outbound

This paper cites Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks

Reference 2

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verified exact
local_arxiv, observed 2026-08-06T20:50:12.113492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:10.975132Z digest=sha256:19769fa46cfa09782a030c1c078becb7feed4b6cc4ab3da0d620c9da7e0b2172

Observation c0a0c329-d168-469c-ac06-a6ac53744ad9 · outbound

This paper cites DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning

Reference 3

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unresolved
no resolver link, observed 2026-08-06T20:50:11.088891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.088891Z digest=sha256:f6d9acbef7560a3e11dd3b36e603e643c716d4e4f96f469201f5b5435f4d4adf

Observation 6f022b49-bb8b-4a64-ba4b-48bdb5456236 · outbound

This paper cites Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

Reference 4

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unresolved
no resolver link, observed 2026-08-06T20:50:11.167208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.167208Z digest=sha256:6badbbd522fd07a0cd956f03d686a0b7bc34917abf12038ac812f9a4ec71f3a4

Observation 0fcf4eee-3e2a-4864-a27a-e5ccdd39a6ec · outbound

This paper cites Fast kinodynamic planning on the constraint manifold with deep neural networks,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Fast kinodynamic planning on the constraint manifold with deep neural networks,

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-06T20:50:13.058656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.276733Z digest=sha256:1e8cf9a73cbf4b81fabbcab28ae988f736b2b3bdbb6b456a2ac3a666d66523b1

Observation e49da02e-9155-4cec-b93b-6b704a941810 · outbound

This paper cites cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots

Reference 6

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unresolved
no resolver link, observed 2026-08-06T20:50:11.362387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.362387Z digest=sha256:5787fd09b98a9135626115122f19370fd2767832d427605978d3f4f52d2f5e16

Observation 8f5b44f8-571b-4ee3-bbad-f8f200e2e150 · outbound

This paper cites Speeding up deep neural network- based planning of local car maneuvers via efficient b-spline path construction,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Speeding up deep neural network- based planning of local car maneuvers via efficient b-spline path construction,

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-06T20:50:13.049384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.451848Z digest=sha256:52fba4ccf750a834ef094d2dd421d2cbdfcf0d330998fa54573f38a84735cf49

Observation 7a941ec7-127b-44fd-9bc2-bde6aa427b01 · outbound

This paper cites Jerk-limited Real-time Trajectory Generation with Arbitrary Target States.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Jerk-limited Real-time Trajectory Generation with Arbitrary Target States

Reference 8

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unresolved
no resolver link, observed 2026-08-06T20:50:11.525934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.525934Z digest=sha256:4e943572e658469eac640f17b4721aac052691a4a150bd8d2ace1cf8e4d39b2b

Observation e5e5d6d7-edd4-4c05-80cf-0df357e61dfa · outbound

This paper cites cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation

Reference 9

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unresolved
no resolver link, observed 2026-08-06T20:50:11.574674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.574674Z digest=sha256:f1a925327ab3f2132cf193b1d3ec1a563fd7eb21f5d6a2ae35ea32664cf13bed

Observation 4cc0abae-af76-41b4-a99f-8340d4e1829e · outbound

This paper cites Chomp: Gradient optimization techniques for efficient motion planning,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Chomp: Gradient optimization techniques for efficient motion planning,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.955557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.602365Z digest=sha256:8e8abdaf86dcea6d5e3e60157a5a0c8518635b7ef47fbb98d4792388366d34fa

Observation 228bedec-2e8f-4a64-be51-d928f466bea3 · outbound

This paper cites G-mapp: Gpu-accelerated multi-agent planning and perception for reactive motion generation,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling G-mapp: Gpu-accelerated multi-agent planning and perception for reactive motion generation,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.932837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.649990Z digest=sha256:e76a58254279a552683626a9206096090bd41570654e2e10519e65de56bcbed8

Observation 50b9e79f-2a3f-4c3c-af06-2520a8c3dedd · outbound

This paper cites Motion planning diffusion: Learning and adapting robot motion planning with diffusion models,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Motion planning diffusion: Learning and adapting robot motion planning with diffusion models,

Reference 12

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unresolved
no resolver link, observed 2026-08-06T20:50:11.692357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.692357Z digest=sha256:4a0c161db8e754da9d4068b581dc89fc3b6b5c91f8bf40dc40d79c6a47e55cd0

Observation 5869d352-9738-41ae-8acc-8e07dbfb20cf · outbound

This paper cites Flow Matching Policy Gradients.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Flow Matching Policy Gradients

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T20:50:11.728877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.728877Z digest=sha256:d67b2ef37ec882fadbbef5fdb75078745d8e6947817fc065246d93c066130dff

Observation a4fbc34b-a807-403d-83c9-92f958b30084 · outbound

This paper cites Outplaying elite table tennis players with an au- tonomous robot,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Outplaying elite table tennis players with an au- tonomous robot,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.770717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.898279Z digest=sha256:df3a80df243f14ce25ace68723866d83f44976d84fdd87279b892cb24eaff7bd

Observation 82495025-9154-4d8e-97a5-4bcfa73e4927 · outbound

This paper cites Tra- jectories are then decoded by batched matrix multiplication.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Tra- jectories are then decoded by batched matrix multiplication

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.613741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.913338Z digest=sha256:6a3f4bca49c12e8aea81d5ce110ebc93530fdd76bea86da8299bf865bb8e764c

Observation 0edd695e-31d7-4fb0-b056-3a5f92a01739 · outbound

This paper cites The default minibatch size is64; because samples are generated online, each epoch is defined as256optimizer steps, with validation every epoch on four batches of size256.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling The default minibatch size is64; because samples are generated online, each epoch is defined as256optimizer steps, with validation every epoch on four batches of size256

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.478213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.916290Z digest=sha256:5705e4b185131fd5cc305f7ca3e7e455b059afd3ca057bfd23f5c1aa2f35c8d8

Observation 8c897c0b-4b0b-4d1b-9f71-fd49815692cd · outbound

This paper cites an unresolved cited work.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Unresolved cited work

Reference 17

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unresolved
raw_fallback, observed 2026-08-06T20:50:12.433007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.918955Z digest=sha256:5deb356c876ef05b2853815be0ede556ad6dc773ad908491769463d507186719

Observation 9b6de909-7eda-4fa3-a753-a7f35b71b4ec · outbound

This paper cites Several losses are evaluated only on the learnable interior part of the spline, namely betweenc 2 andc nc−2 as defined in 6.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Several losses are evaluated only on the learnable interior part of the spline, namely betweenc 2 andc nc−2 as defined in 6

Reference 18

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verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.402910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.921556Z digest=sha256:6e8ceeab761e77256c0962974001108e54314185fbc74ec21f68c524f4b78bc3

Observation e2756483-b018-49fd-aa6f-d4034025c3c8 · outbound

This paper cites an unresolved cited work.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Unresolved cited work

Reference 19

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unresolved
raw_fallback, observed 2026-08-06T20:50:12.249466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.933800Z digest=sha256:bf230effddb5b0e87da274463523f414e519bac80e2009a754d06929dfa55384

Pith citing papers

No inbound Pith citation observations are available.