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Paper Citation Record · LEDGER

Parametrized Multi-Agent Routing via Deep Attention Models

As of 11 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2507.22338.

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

pith.paper-citation-record.v1
2507.22338 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:56:09.110750Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

13 of 13 outbound references displayed

  • verified exact1
  • verified fuzzy5
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 47d2091c-6de9-4667-8993-1f9a36cebbf1 · outbound

This paper cites an unresolved cited work.

Parametrized Multi-Agent Routing via Deep Attention Models Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-06T11:56:09.246510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.092488Z digest=sha256:3b0d6cc573835584481dae99515d89e069153611dc689eb06147cabcfb4c4d03

Observation 3f3c0e7b-94bd-4fdc-a32b-cfcb0ad4fbab · outbound

This paper cites an unresolved cited work.

Parametrized Multi-Agent Routing via Deep Attention Models Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-06T11:56:09.237514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.095207Z digest=sha256:ec23cc6c3fe5d4c7114239c5a5b698c39ec974179af3b2fab2f37e02e76603d9

Observation 2f907f6b-b436-473a-8e93-d5a5e63ceed8 · outbound

This paper cites This results in a total ofO M 2 PM k=1 M k k! operations.

Parametrized Multi-Agent Routing via Deep Attention Models This results in a total ofO M 2 PM k=1 M k k! operations

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:56:09.226732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.098936Z digest=sha256:97d99b29c8338a765bc68f4d1f05618ed96ce679cd9281f8cb089de8051c30ca

Observation a1155b3f-2820-428b-b84c-3a00e9623f46 · outbound

This paper cites 7.5 Worst case computational complexity of∇ Y Fβ via(12),(13)isO(N M 4).

Parametrized Multi-Agent Routing via Deep Attention Models 7.5 Worst case computational complexity of∇ Y Fβ via(12),(13)isO(N M 4)

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:56:09.217667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.101936Z digest=sha256:3fc89c6928f57a2f4f173412a029b4a95b6f1bd85ce3f434c3b7e3e380a01b5a

Observation a7ffb0a3-13ed-486a-a407-4c9cd928d00e · outbound

This paper cites Multiplication with∇ yj di k +V i k+1 adds anotherO(M 2)operations for eachj.

Parametrized Multi-Agent Routing via Deep Attention Models Multiplication with∇ yj di k +V i k+1 adds anotherO(M 2)operations for eachj

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:56:09.207835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.104878Z digest=sha256:6beccc52f284f69a402018514e60a9aa807f4941bd7c4b905dd988d3abccb385

Observation 421bcda2-cc36-4e09-a871-cf0815ac7618 · outbound

This paper cites Further, repeating these operations for each1≤k≤Mresults in a total ofO(M 4)operations.

Parametrized Multi-Agent Routing via Deep Attention Models Further, repeating these operations for each1≤k≤Mresults in a total ofO(M 4)operations

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:56:09.197434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.107814Z digest=sha256:647e954007fd4cca75a19c2a71e92f7620b5555c200ba309ca8b45bba740c433

Observation 691885c4-1d2f-4bc8-9692-9a91de72dff8 · outbound

This paper cites an unresolved cited work.

Parametrized Multi-Agent Routing via Deep Attention Models Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-06T11:56:09.188098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.110750Z digest=sha256:4b8f72688b7d34ad33676fb294f0fd0a396abfbb7f6fd8181ae11347979ff752

Observation 7dd7d721-b344-4dcc-8e95-e1013fcc2e48 · outbound

This paper cites The Transformer Network for the Traveling Salesman Problem.

Parametrized Multi-Agent Routing via Deep Attention Models The Transformer Network for the Traveling Salesman Problem

Reference 2009

Resolution
unresolved
no resolver link, observed 2026-08-06T11:56:09.076671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:56:09.076671Z digest=sha256:6ba26c090d51763d91e011d9423f05eb31072017000415c5c74bd74856e16261

Observation e09aee4e-87ae-44c0-aaa1-41387e775e64 · outbound

This paper cites Neural Combinatorial Optimization with Reinforcement Learning.

Parametrized Multi-Agent Routing via Deep Attention Models Neural Combinatorial Optimization with Reinforcement Learning

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-06T11:56:09.072941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:56:09.072941Z digest=sha256:8b0c3e70414e735c1507548e73deca579a90a99804418a5058bd19271ae83df1

Observation a901520e-3774-4572-9a5e-c0090f4012a1 · outbound

This paper cites Reinforcement Learning for Solving the Vehicle Routing Problem.

Parametrized Multi-Agent Routing via Deep Attention Models Reinforcement Learning for Solving the Vehicle Routing Problem

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-06T11:56:09.083912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:56:09.083912Z digest=sha256:a7ff594e4f084339f59794b6399087fecbb8bc2af8f8032aab55bc1b12a860c9

Observation e1149fa9-f330-49b3-aeb2-406d9c41d93d · outbound

This paper cites BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization.

Parametrized Multi-Agent Routing via Deep Attention Models BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization

Reference 2020

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T11:56:09.162352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.080195Z digest=sha256:cec85d6e1a80889d4ed582dd6a19129f5830893c3643fe9bcb32fa24cf410dcc

Observation eec56a8f-186b-4b51-9288-1dca41cd6400 · outbound

This paper cites Basiri, S.; Tiwari, D.; Papachristos, C.; and Salapaka, S.

Parametrized Multi-Agent Routing via Deep Attention Models Basiri, S.; Tiwari, D.; Papachristos, C.; and Salapaka, S

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:56:09.257309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.069375Z digest=sha256:9fe3847e2eb525bb26c7baf5cb56f3c8421e96a67f92efa9b416d70abcb7527c

Observation 928b4658-fb04-4676-8ff5-c41ceb1196e2 · outbound

This paper cites GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time.

Parametrized Multi-Agent Routing via Deep Attention Models GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time

Reference 2024

Resolution
verified exact
local_arxiv, observed 2026-08-06T11:56:09.139842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T11:56:09.088319Z digest=sha256:7c547162422a79ce042e9a4bb96811eef40e129bbbe9c7935396897dd7baec8d

Pith citing papers

No inbound Pith citation observations are available.