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Paper Citation Record · LEDGER

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding

As of 13 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2505.19219.

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

pith.paper-citation-record.v1
2505.19219 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:20:18.987183Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-23T02:03:47.244209Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T02:05:19.509866Z

Reference resolution

29 of 29 outbound references displayed

  • verified exact11
  • verified fuzzy6
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dfb277bb-8cd6-40e1-b17b-682d376615bd · outbound

This paper cites Odrm* optimal multirobot path plan- ning in low dimensional search spaces.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Odrm* optimal multirobot path plan- ning in low dimensional search spaces

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:20:21.995003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:16.837612Z digest=sha256:cb90a16da27ecc98d99de620befb3153f781b39f3b2783716836e0df689d6bc6

Observation b78f58d8-3e88-4e50-b540-3083e5adb3fa · outbound

This paper cites Social Behavior as a Key to Learning-based Multi-Agent Pathfinding Dilemmas.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Social Behavior as a Key to Learning-based Multi-Agent Pathfinding Dilemmas

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:21.020894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:16.985547Z digest=sha256:6b163832a60c2f7cb82c12f2e1b71f4f078695680fb7365fb860ccad55e12281

Observation 58361718-96d4-4821-a2cc-317fb00c7498 · outbound

This paper cites Formalization of Optimality Conditions for Smooth Constrained Optimization Problems.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Formalization of Optimality Conditions for Smooth Constrained Optimization Problems

Reference 8

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unresolved
no resolver link, observed 2026-08-07T14:20:17.064529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:17.064529Z digest=sha256:f10d7217307addc2062b296edff9de2bc302c8dfb47b1aaab272b7b561b4f155

Observation 9c2142cb-a13d-493c-a3f5-33d1cd687e0d · outbound

This paper cites Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective

Reference 11

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unresolved
no resolver link, observed 2026-08-07T14:20:17.394115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:17.394115Z digest=sha256:1e568db7ce725dd479adc7602dfae15113751e081d8c36a3c2a6160cabaaa17b

Observation 1683716c-45e9-40d5-94c4-add1a99b62ae · outbound

This paper cites Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks

Reference 12

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unresolved
no resolver link, observed 2026-08-07T14:20:17.490938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:17.490938Z digest=sha256:773724d90e34bcce73888ec9d93c56d3a67393f35be6766e16a73f7629da2756

Observation a634e927-9015-4928-9217-6413656ac55b · outbound

This paper cites Flatland-RL : Multi-Agent Reinforcement Learning on Trains.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Flatland-RL : Multi-Agent Reinforcement Learning on Trains

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T14:20:17.624351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:17.624351Z digest=sha256:f1051f3ff8c6e9ddbc0fee8dac5fe7fe0e84041dccd21164548e32ad8a00160a

Observation 928d5bad-3f1c-457f-a701-9dee3a73c0ae · outbound

This paper cites Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:20.710669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:17.723877Z digest=sha256:acf1df15080cff29861645b0cde1210a9103b9690b3de2886c0058bead4a1283

Observation 5299c64e-8d16-4d58-b320-80cd5ad0b5c6 · outbound

This paper cites winPIBT: Extended Prioritized Algorithm for Iterative Multi-agent Path Finding.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding winPIBT: Extended Prioritized Algorithm for Iterative Multi-agent Path Finding

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:20.579937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:17.819213Z digest=sha256:5020cad880ab17cd81eb9277867154594d4ad3617cd303308ad9c09753793410

Observation 7e006c41-b4bd-44d5-99d4-23e6e9723200 · outbound

This paper cites Multi Agent Path Finding using Evolutionary Game Theory.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Multi Agent Path Finding using Evolutionary Game Theory

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:20.320352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:18.012983Z digest=sha256:38b45240fde8d5ebe773b5b458707ff57e1f221061dcac2832f75d3f8b50288b

Observation 6a867521-a2df-44b0-b4b2-a0956c7f4129 · outbound

This paper cites Confidence-Based Curriculum Learning for Multi-Agent Path Finding.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Confidence-Based Curriculum Learning for Multi-Agent Path Finding

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:20.173975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:18.108613Z digest=sha256:646cc633c54bf0d09d6514719846a56e9250560f09231b1c0d1b70d722fb072d

Observation 9ede09fa-f5a9-4aff-8cf1-62b209db12c9 · outbound

This paper cites Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:20.032582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:18.185373Z digest=sha256:89433abd1c988b08682849a8fa138618f7b0b78c73ef0fe16610e535838d24b8

Observation 0e395897-4864-47a2-b562-63e671047807 · outbound

This paper cites MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:19.913009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:18.259433Z digest=sha256:cd191f5db570d9ed7f652ab539b7b43681a77700d7baae38dfc65858382515cd

Observation 4293428d-4f2f-4acc-a09e-a32d35bdd65f · outbound

This paper cites LLMDR: LLM-Driven Deadlock Detection and Resolution in Multi-Agent Pathfinding.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding LLMDR: LLM-Driven Deadlock Detection and Resolution in Multi-Agent Pathfinding

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T14:20:18.362271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:18.362271Z digest=sha256:94e141b5bb9b59eec532a733fa03078694b77498f791aa6d1be4540bbc069ca6

Observation 24fde042-6bf6-4a0d-b6a4-a22220d28497 · outbound

This paper cites Automatic Algorithm Selection In Multi-agent Pathfinding.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Automatic Algorithm Selection In Multi-agent Pathfinding

Reference 22

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:19.745297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:18.454334Z digest=sha256:938c200245bfbbcc1820746457b63eadbaaa63a2deab8e0f63a933ca4f86ece5

Observation 96f940d7-249e-4ec9-a9f1-011e2d5fadee · outbound

This paper cites POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding

Reference 23

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no resolver link, observed 2026-08-07T14:20:18.526685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:18.526685Z digest=sha256:49fbaef742fa48dbea103027c5d2c058e8e186f3f7387d46f7a1ec62576e62d4

Observation 20663c9a-4c9f-4936-beb9-82b8afad35db · outbound

This paper cites Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:19.399604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:18.686763Z digest=sha256:00ab428f8301452f5b98b47139149332e6cd72fc29056853abfc5fd20388f1aa

Observation 7e9a7097-30a1-45b8-8d1a-1d6d2ba52413 · outbound

This paper cites Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T14:20:18.775994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:18.775994Z digest=sha256:5ad26ed4353292bbb06675eeb2f07987f346e544fd3504d9f1e165e07b810447

Observation 7629778c-1ebc-4642-9c78-d6a525aa491a · outbound

This paper cites M*: A complete multirobot path planning algorithm with performance bounds.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding M*: A complete multirobot path planning algorithm with performance bounds

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:20:21.394136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:18.830412Z digest=sha256:df11b70a760afeb789c53837be7e7fd0d10df4711a2339b8b097c84efc231379

Observation decdae40-8fa6-4c67-ba2d-f7112c8a436e · outbound

This paper cites LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:19.207331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:18.909676Z digest=sha256:5459303f1693ff8e6ad47d068e0d21a1c0890ef3c4761a753a43b7327ad20250

Observation 7a2ac8c6-6b36-48a7-9eb3-9d4fba257dd4 · outbound

This paper cites Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T14:20:18.987183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:18.987183Z digest=sha256:c01421c89a996c8a4d3edcde6e8a6b71151a06c67a1c81648dee71ed47fb117a

Observation c03aa72e-6554-4b21-b2a1-d82aa71eebbf · outbound

This paper cites Formal verification of piece-wise linear feed-forward neural networks.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Formal verification of piece-wise linear feed-forward neural networks

Reference 2016

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:20:22.212145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:16.688404Z digest=sha256:2f3016d1d0527c01c8b3da70cb5a1a56989f72a3564c18007ce899c35e43bd01

Observation 6a67b3f0-0759-4470-869d-400fc974abd9 · outbound

This paper cites Scalable mechanism design for multi-agent path finding.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Scalable mechanism design for multi-agent path finding

Reference 2017

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no resolver link, observed 2026-08-07T14:20:16.915874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:16.915874Z digest=sha256:86fa4dcedf41cec101c5d9390a85101e14c47337896e40afd5fb07058e169a90

Observation ff82c637-eb77-4dc8-a463-e8c0ecfea60f · outbound

This paper cites Multi-agent Path Finding with Continuous Time Viewed Through Satisfiability Modulo Theories (SMT).

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Multi-agent Path Finding with Continuous Time Viewed Through Satisfiability Modulo Theories (SMT)

Reference 2018

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no resolver link, observed 2026-08-07T14:20:18.615429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:18.615429Z digest=sha256:8a56671aa56632f299aa7b0c4083beed14dd8d465fb119399d2f7e2df4117ec7

Observation b0964045-ac46-478c-ab61-438d5c8a7697 · outbound

This paper cites Offline Time-Independent Multi-Agent Path Planning.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Offline Time-Independent Multi-Agent Path Planning

Reference 2019

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:20:20.449584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:17.917831Z digest=sha256:1131a57766d5dc7c678d50f26cf52052f8869885418f61b6d8b872d0686eb1f2

Observation a5ce5b98-f0c5-4eec-ac24-032c1b1d8d5f · outbound

This paper cites Anytime multi- agent path finding via large neighborhood search.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Anytime multi- agent path finding via large neighborhood search

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:20:21.635207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:17.164612Z digest=sha256:d0ee5fed7796cee84f1ff59688bf0b15f7e8a4dc936a126b8412729a2e638017

Observation 76b0651f-42ae-4715-990e-e9fd5eb80e1a · outbound

This paper cites Accessed: 2024-12-03.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Accessed: 2024-12-03

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:20:22.285935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:16.478315Z digest=sha256:f36173c02ef0e751c5f20c8a29d96b965004be19ad5e96906bc062db7dcbf59d

Observation 8f72e3f5-ef28-4dca-985c-ba1c71128c81 · outbound

This paper cites Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet

Reference 2022

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unresolved
no resolver link, observed 2026-08-07T14:20:16.605756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:16.605756Z digest=sha256:790a3695cf3e332698d7537cba94c98743dcf00cae8ac96ccba93e0335c0fc04

Observation 951789c2-afc1-435d-bc43-18f6214094f2 · outbound

This paper cites Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T14:20:17.296657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:17.296657Z digest=sha256:6417e85161948e6a7428442f1a70ecf74d2fba644790cc67673cb613a6c0d7c7

Observation 1aa2af82-e184-4e40-92f6-d61bd00085c1 · outbound

This paper cites Mapfaster: A faster and simpler take on multi-agent path finding algorithm selection.

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Mapfaster: A faster and simpler take on multi-agent path finding algorithm selection

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:20:22.482261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:20:16.416386Z digest=sha256:f516f256cdaaeaaed87f0ed859b1c1bbaefe778e45839da47ef42725cd40f458

Pith citing papers

Observation 6a6d821e-f344-472a-9214-c2edb4757088 · inbound

Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART) cites this paper.

Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART) Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-23T02:05:19.514246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-23T02:03:47.244209Z digest=sha256:daab14b73f1e2607468a5417ae95c1fe68959960680f247bc315b4b1e238d282