Paper Citation Record · LEDGER

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus

As of 19 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2504.17700.

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

pith.paper-citation-record.v1
2504.17700 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:38:11.194026Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

26 of 26 outbound references displayed

verified exact1
verified fuzzy15
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0436b69-26d9-471b-a63a-fe218d93f154 · outbound

This paper cites Distributed Multi-agent Coordination over Cellular Sheaves.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Distributed Multi-agent Coordination over Cellular Sheaves

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T10:38:11.042517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41b2dcee-6c0c-4c08-8609-d658f7340ac6 · outbound

This paper cites Lattice Theory in Multi-Agent Systems.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Lattice Theory in Multi-Agent Systems

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-16T10:38:11.367230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ddb71fd3-00b5-4570-85da-450566fc7c85 · outbound

This paper cites A Gentle Introduction to Sheaves on Graphs.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus A Gentle Introduction to Sheaves on Graphs

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.679268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a084a9e-8faa-421a-890a-3f5d468ab391 · outbound

This paper cites Sheaves, Cosheaves and Applications.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Sheaves, Cosheaves and Applications

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T10:38:11.060950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17c3723b-93a2-43f1-b8b7-7092501de887 · outbound

This paper cites Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.664279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3cce9177-594b-4b32-8392-9606213fbfd9 · outbound

This paper cites Sheaf Neural Networks with Connection Laplacians.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Sheaf Neural Networks with Connection Laplacians

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T10:38:11.078542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 183a1aaa-9974-4678-88f4-4c4172a3f2a9 · outbound

This paper cites Learning Sheaf Laplaci ans from Smooth Signals.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Learning Sheaf Laplaci ans from Smooth Signals

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T10:38:11.094070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff608ea1-b68c-4f98-a3ec-334db2429e01 · outbound

This paper cites Constructive Sheaf Semantics.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Constructive Sheaf Semantics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.650015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9b737ef0-f2c7-4b5c-aac3-78bc8942c4df · outbound

This paper cites Type-Topology in Univalent Foundations.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Type-Topology in Univalent Foundations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.635082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ed979c58-dde9-433c-993c-9546f41153f8 · outbound

This paper cites Homotopy Type Theo ry: Univalent Foundations of Mathematics.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Homotopy Type Theo ry: Univalent Foundations of Mathematics

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.620763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5087d58b-77f1-4912-af41-7342c0d71ed3 · outbound

This paper cites HVM2: A Parallel Evaluator for Interaction C ombinators.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus HVM2: A Parallel Evaluator for Interaction C ombinators

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.605409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 94d05f45-e2f8-45f1-afcd-dc9764d8281a · outbound

This paper cites Sheaves in Geometry and Logi c: A First Introduction to Topos Theory.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Sheaves in Geometry and Logi c: A First Introduction to Topos Theory

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.590899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d512d7fd-7a16-4953-ab46-3b99d73baa65 · outbound

This paper cites Interaction Nets.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Interaction Nets

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.576530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 971cc6d2-4c1a-40c0-83e5-565436ceffc6 · outbound

This paper cites Interaction Combinators.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Interaction Combinators

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.561796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f7282427-b77f-4b93-b710-bf556ec43b5d · outbound

This paper cites A Denotational Semantics for the Symmetric In teraction Combinators.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus A Denotational Semantics for the Symmetric In teraction Combinators

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.546703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1c3687d2-3bcc-4137-995c-4fd236fcc703 · outbound

This paper cites Sheaf Neural Networks.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Sheaf Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T10:38:11.142571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f21b5436-1428-434a-9544-9035e713b09b · outbound

This paper cites Topology and data.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Topology and data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.531499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08d1b124-67e6-480a-8cf8-17068a35f3de · outbound

This paper cites an unresolved cited work.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:38:11.515286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee9ed508-4e93-467a-ba17-5c92e341724c · outbound

This paper cites Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T10:38:11.158619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b612b2e-5fe4-465b-ab5e-5eb8219ce9fd · outbound

This paper cites an unresolved cited work.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:38:11.500664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 49148ed4-1448-436d-8bc4-f56a6c75cf5b · outbound

This paper cites Vakil, The Rising Sea: Foundations of Algebraic Geometry.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Vakil, The Rising Sea: Foundations of Algebraic Geometry

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.484044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db9557f0-a316-40cb-a648-0d2482ccbdfc · outbound

This paper cites an unresolved cited work.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:38:11.467123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1cee5197-efab-42e9-be06-e2315a40c4ff · outbound

This paper cites an unresolved cited work.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:38:11.451695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd4706f9-7111-413d-98d1-45dc7b614804 · outbound

This paper cites Jagathese, ”Math 553: Algebraic Geometry II Lecture Notes (Spring 2023, UIC),” 2023.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Jagathese, ”Math 553: Algebraic Geometry II Lecture Notes (Spring 2023, UIC),” 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.434943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f3e1344-bd61-4656-ba38-cf3d7ef948e4 · outbound

This paper cites Hartshorne, Algebraic Geometry, vol.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Hartshorne, Algebraic Geometry, vol

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.419090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 099748f0-b753-4bf7-8072-3ee594fccad6 · outbound

This paper cites Grothendieck, ”Sur quelques points d’alg` ebre homo logique,” Tˆ ohoku Math.

Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus Grothendieck, ”Sur quelques points d’alg` ebre homo logique,” Tˆ ohoku Math

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:38:11.401819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.