Pith. sign in

Paper Citation Record · LEDGER

RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2306.17100.

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

pith.paper-citation-record.v1
2306.17100 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:13:49.366109Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T11:54:38.423068Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 88b25c24-2535-4cd6-9a8d-531a080178c0 · inbound

Design And Optimization Of Multi-rendezvous Manoeuvres Based On Reinforcement Learning And Convex Optimization cites this paper.

Design And Optimization Of Multi-rendezvous Manoeuvres Based On Reinforcement Learning And Convex Optimization RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T18:13:49.366109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:13:49.366109Z digest=sha256:3a4bbb87466eeb69102e552ed622aa423303e4b9eb17ba6e54470b46402af8c3

Observation 577c570d-6401-426c-a907-f12d9c42f40d · inbound

Multi-Agent Environments for Vehicle Routing Problems cites this paper.

Multi-Agent Environments for Vehicle Routing Problems RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-23T17:15:43.652664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-23T17:14:04.287133Z digest=sha256:8d8d4c4a3afb830e2968eeb419d1e191b2f8b674ccef2862d0bf87150b2ac419

Observation 5668ecdd-8c7d-415c-ab36-37f2ea349be4 · inbound

TransPlace: Transferable Circuit Global Placement via Graph Neural Network cites this paper.

TransPlace: Transferable Circuit Global Placement via Graph Neural Network RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-10T21:19:09.804502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:19:09.804502Z digest=sha256:7481d5ffed831198234634f3418854e3b844aa1545c6b3becd6edd8271015c26

Observation fc86a519-a3bf-44ae-8b0b-2c73b28e54d1 · inbound

Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning cites this paper.

Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-08T19:03:05.351089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:03:05.351089Z digest=sha256:f2424687f05f4904e246b03178540c461c70dcaf174c3b4660976c02ee9db199

Observation 3691a9cd-791f-4cc4-bcaa-b0b353b5cceb · inbound

Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding cites this paper.

Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T19:00:13.910344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:00:13.910344Z digest=sha256:bc240376e14492b30e20dc75363f082a51b8e92ab048e753d67c9daa1ffc5896

Observation 44c83f64-be6a-46bd-b7b2-48cc55bcb1af · inbound

SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchy cites this paper.

SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchy RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T05:18:12.672425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:18:12.672425Z digest=sha256:3c4a2680d1afd38ebeb4985f18fc8e9ed5b811a64ecde74ed24034d9530012d2

Observation 0ab2884f-543f-40f6-beb9-8426ec43656c · inbound

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial cites this paper.

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Reference 187

Resolution
unresolved
no resolver link, observed 2026-08-05T04:50:32.214536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T04:50:32.214536Z digest=sha256:ca915262cee73d42deaebf1717b61dfb93aa6fe4d28afc50e26f6dfc6052d68e

Observation 9646f662-11cc-4532-bb0e-7f54b0aaac81 · inbound

Constraint-Anchored Attribution: Feasibility-Certified Counterfactuals and Bonferroni-PAC Sufficient Subsets for Neural CO Policies cites this paper.

Constraint-Anchored Attribution: Feasibility-Certified Counterfactuals and Bonferroni-PAC Sufficient Subsets for Neural CO Policies RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-06-30T11:54:38.424829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-30T11:49:22.620795Z digest=sha256:6efe6ddd4a42120aa37d35afc8834ea6dc41dfe8bd17c3100e72c70664af25d0