Paper Citation Record · LEDGER

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.06605.

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

pith.paper-citation-record.v1
2507.06605 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:06:14.998890Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

33 of 33 outbound references displayed

verified exact0
verified fuzzy26
unresolved7
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation efefda2f-9b41-4a8e-9b12-ea84c4380d7c · outbound

This paper cites an unresolved cited work.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T19:06:14.902064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7b13b95-ccf9-4f14-8f00-7bae5912704c · outbound

This paper cites A formal basis for the heuristic determination of minimum cost paths,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration A formal basis for the heuristic determination of minimum cost paths,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T19:06:14.905884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12eb4fd0-dfc2-407f-9f42-19e94aaeb325 · outbound

This paper cites Rapidly-exploring random trees: A new tool for path planning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Rapidly-exploring random trees: A new tool for path planning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.272098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 75ff86f7-39b8-4267-b659-5f636a6973a3 · outbound

This paper cites Probabilistic roadmaps for path planning in high-dimensional configuration spaces,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Probabilistic roadmaps for path planning in high-dimensional configuration spaces,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.264307Z

Source-reported events for the cited work

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Observation f9593df6-8ef7-4f99-9e0e-c9a7ad63fb2f · outbound

This paper cites Rapidly-exploring random trees: Progress and prospects,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Rapidly-exploring random trees: Progress and prospects,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.256053Z

Source-reported events for the cited work

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Observation 00394420-8794-411a-9532-4c68f477d6c4 · outbound

This paper cites Learning-based near-optimal motion planning for intelligent vehicles with uncertain dynamics,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Learning-based near-optimal motion planning for intelligent vehicles with uncertain dynamics,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.247478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ab901361-1206-4207-af44-a987e375869c · outbound

This paper cites Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:06:14.922455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5accc70e-bfff-4a66-bb3e-662ac26237b7 · outbound

This paper cites Optimal and efficient path planning for partially-known environments,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Optimal and efficient path planning for partially-known environments,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.239396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1b937472-e40f-49d2-91ed-b1951d973b5f · outbound

This paper cites Using interpolation to improve path planning: The field d* algorithm,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Using interpolation to improve path planning: The field d* algorithm,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.231374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3c597294-5c59-41b8-843f-11aa6ca2afb1 · outbound

This paper cites Lifelong planning a*,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Lifelong planning a*,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.223471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c71fa56f-23b0-4964-9ac6-e1af86ac5c15 · outbound

This paper cites The jps pathfinding system,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration The jps pathfinding system,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.213258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6a044c86-b216-4199-8586-22e9f1bb0af3 · outbound

This paper cites Improving jump point search,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Improving jump point search,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.204512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d8727142-c72f-4a0d-94be-5be64684bb24 · outbound

This paper cites Theta*: Any-angle path planning on grids,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Theta*: Any-angle path planning on grids,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.196297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a9ab879a-f74d-4710-8d7a-4833cedcc5f4 · outbound

This paper cites Randomized kinodynamic planning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Randomized kinodynamic planning,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:06:14.943230Z

Source-reported events for the cited work

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Observation 37cb964d-db82-44c0-b8cc-a97e01322607 · outbound

This paper cites Sampling-based algorithms for optimal motion planning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Sampling-based algorithms for optimal motion planning,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T19:06:14.946592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22f2f44b-c0ad-4ab6-a572-f14743611f72 · outbound

This paper cites Rrt-connect: An efficient approach to single-query path planning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Rrt-connect: An efficient approach to single-query path planning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.187753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 48cc1b9d-c66d-43ce-a1cd-49f0a8b1e626 · outbound

This paper cites Informed rrt*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Informed rrt*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.178925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e7e5b17c-615a-4c91-a41e-20a8f98ad9ea · outbound

This paper cites Batch informed trees (bit*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Batch informed trees (bit*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.169685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 08d69303-85d9-42c0-b2bc-b083650a61d1 · outbound

This paper cites Advanced bit* (abit*): Sampling- based planning with advanced graph-search techniques,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Advanced bit* (abit*): Sampling- based planning with advanced graph-search techniques,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.160294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4e9fedca-a4b7-4548-adc8-25fd51bffbc5 · outbound

This paper cites Adaptively informed trees (ait*): Fast asymptotically optimal path planning through adaptive heuristics,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Adaptively informed trees (ait*): Fast asymptotically optimal path planning through adaptive heuristics,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.152443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0c72c5c8-6b95-4492-a577-000e8a683c53 · outbound

This paper cites Pathrl: An end-to-end path generation method for collision avoidance via deep reinforcement learning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Pathrl: An end-to-end path generation method for collision avoidance via deep reinforcement learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.143466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6aa0c1fe-b30d-4e38-8212-ee8a3cd3ac2f · outbound

This paper cites Neural MP: A Generalist Neural Motion Planner.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Neural MP: A Generalist Neural Motion Planner

Reference 22

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Source-reported events for the cited work

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Observation 9130087a-9428-4e28-a4da-ea403577b2e2 · outbound

This paper cites Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.133997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 27950d72-5480-419f-b458-7dc2c9227fca · outbound

This paper cites Rl-rrt: Kinodynamic motion planning via learning reachability estimators from rl policies,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Rl-rrt: Kinodynamic motion planning via learning reachability estimators from rl policies,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.124583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7674560c-761a-4f10-9dd4-cf0acf3e3fca · outbound

This paper cites Mobile robot path plan- ning in dynamic environments through globally guided reinforcement learning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Mobile robot path plan- ning in dynamic environments through globally guided reinforcement learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.115227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3556af6c-fc64-4cf2-90a2-7807dfb4d818 · outbound

This paper cites Learning-based motion planning in dynamic environments using gnns and temporal encoding,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Learning-based motion planning in dynamic environments using gnns and temporal encoding,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.105180Z

Source-reported events for the cited work

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Observation 20522d33-b57d-494e-9800-bd40606ae4e4 · outbound

This paper cites Deeply informed neural sampling for robot motion planning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Deeply informed neural sampling for robot motion planning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.095240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ed4c1a62-39cb-4c58-9380-d77be7eb7425 · outbound

This paper cites Learned critical probabilistic roadmaps for robotic motion planning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Learned critical probabilistic roadmaps for robotic motion planning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.086848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc262bc9-ef70-4918-9162-2306f432209b · outbound

This paper cites Motion planning networks: Bridging the gap between learning-based and classical motion planners,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Motion planning networks: Bridging the gap between learning-based and classical motion planners,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T19:06:14.988015Z

Source-reported events for the cited work

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Observation 87d6ad5a-b10f-4af6-baa9-3dbe7628f008 · outbound

This paper cites Neural rrt*: Learning-based optimal path planning,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Neural rrt*: Learning-based optimal path planning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.073146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ef00b300-eed0-4cf5-9423-d00e66bd5618 · outbound

This paper cites Neural informed rrt*: Learning-based path planning with point cloud state representations under admissible ellipsoidal constraints,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration Neural informed rrt*: Learning-based path planning with point cloud state representations under admissible ellipsoidal constraints,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.064609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 229f6891-8c56-4338-988f-0dd224fde34d · outbound

This paper cites The Open Motion Planning Library,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration The Open Motion Planning Library,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.056008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a19a51f4-38f6-4b33-9d86-447edf3f65fc · outbound

This paper cites A new approach to time-optimal path parameterization based on reachability analysis,.

Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration A new approach to time-optimal path parameterization based on reachability analysis,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:06:15.047225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.