Paper Citation Record · LEDGER

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory

As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2607.18115.

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

pith.paper-citation-record.v1
2607.18115 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T16:11:40.247272Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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unresolved44
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Outbound references

Observation 7c64246a-6553-4326-94b6-49fe8e0179c6 · outbound

This paper cites Temporal windows of integration for multisensory wireless systems as enablers of physical ai,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Temporal windows of integration for multisensory wireless systems as enablers of physical ai,

Reference 1

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Observation 0e21fa4a-4263-4582-8a79-57c9a59ddab2 · outbound

This paper cites A joint learning and communications framework for federated learning over wireless networks,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory A joint learning and communications framework for federated learning over wireless networks,

Reference 2

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Observation 31495dc4-6548-4ea7-bb4b-ebd63792cd87 · outbound

This paper cites A review of applications in federated learning,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory A review of applications in federated learning,

Reference 3

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Observation 7396beb2-e024-461e-a8fe-410b7ff4971d · outbound

This paper cites 6g networks: Beyond shannon towards semantic and goal-oriented communications,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory 6g networks: Beyond shannon towards semantic and goal-oriented communications,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.076000Z

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Observation 059b4a09-9ce5-4831-995b-529d771e2d70 · outbound

This paper cites Semantics-empowered communication for networked intelligent systems,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Semantics-empowered communication for networked intelligent systems,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.080198Z

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Observation ffe17381-d0f0-4c42-933f-879ec06661bf · outbound

This paper cites Less Data, More Knowledge: Building Next Generation Semantic Communication Networks,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Less Data, More Knowledge: Building Next Generation Semantic Communication Networks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.084252Z

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Observation 2f11255b-6425-453c-920d-14f4ac7cf484 · outbound

This paper cites Rethinking modern communication from semantic coding to semantic communication,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Rethinking modern communication from semantic coding to semantic communication,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.088807Z

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Observation 744b9da8-9faf-4045-ae70-037ab7e9cce9 · outbound

This paper cites Latent Space Align- ment for Semantic Channel Equalization,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Latent Space Align- ment for Semantic Channel Equalization,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.092678Z

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Observation 4f2d8392-ac74-4819-a245-2a88b8db173c · outbound

This paper cites Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.096613Z

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Observation 860984f2-6687-43bb-b468-52c5c7bf5637 · outbound

This paper cites Semantic channel equalizer: Modelling language mismatch in multi-user semantic communications,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Semantic channel equalizer: Modelling language mismatch in multi-user semantic communications,

Reference 10

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Observation 75ec55b0-9bc7-4e8d-a7da-14051ae0bdbe · outbound

This paper cites Semantic channel equalization strategies for deep joint source-channel coding,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Semantic channel equalization strategies for deep joint source-channel coding,

Reference 11

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Observation 15ad53fa-9739-4a5a-a86b-7daf87d3ac4c · outbound

This paper cites Soft partitioning of latent space for semantic channel equalization,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Soft partitioning of latent space for semantic channel equalization,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.108589Z

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Observation fb7e2b3a-d332-4b1e-8c2a-284fc28b08a1 · outbound

This paper cites Learning network sheaves for ai-native semantic communi- cation,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Learning network sheaves for ai-native semantic communi- cation,

Reference 13

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Observation 4145e344-d29e-4202-aedf-fae8bc9268b0 · outbound

This paper cites A Theory of Semantic Commu- nication,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory A Theory of Semantic Commu- nication,

Reference 14

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Observation ecd60dd3-0ef1-43ef-b784-06b66fc40a68 · outbound

This paper cites Resource allocation for text semantic communications,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Resource allocation for text semantic communications,

Reference 15

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Observation 7a95c988-6fdb-4386-8a11-c862b8aac882 · outbound

This paper cites Performance optimization for semantic communications: An attention- based reinforcement learning approach,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Performance optimization for semantic communications: An attention- based reinforcement learning approach,

Reference 16

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Observation 12cf3581-2613-4906-a927-cddc6524d69f · outbound

This paper cites Task-Oriented Multi- User Semantic Communications ,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Task-Oriented Multi- User Semantic Communications ,

Reference 17

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Observation bb50c981-e735-42c2-8097-486a852baf32 · outbound

This paper cites Non-orthogonal multiple access enhanced multi-user semantic communication,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Non-orthogonal multiple access enhanced multi-user semantic communication,

Reference 18

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Observation 08575bfb-52af-4031-9604-4334f56ef1b7 · outbound

This paper cites Toward semantic communication protocols: A probabilistic logic perspective,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Toward semantic communication protocols: A probabilistic logic perspective,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.135534Z

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Observation b0753363-1014-4bd6-a6f0-1bd1a56cd871 · outbound

This paper cites Swin transformer-based dynamic semantic communication for multi-user with different computing capacity,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Swin transformer-based dynamic semantic communication for multi-user with different computing capacity,

Reference 20

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Observation b87e5efa-bbad-402e-9254-1c44d994d71e · outbound

This paper cites Neuromorphic wireless cognition: Event-driven semantic communications for remote inference,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Neuromorphic wireless cognition: Event-driven semantic communications for remote inference,

Reference 21

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Observation 9776b30a-4473-4dd0-8b11-ee1ada2d1cf8 · outbound

This paper cites Collaborative semantic communication for edge inference ,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Collaborative semantic communication for edge inference ,

Reference 22

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Observation fa51bd9a-1155-45c6-85ca-43f0a1f0a547 · outbound

This paper cites Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications,

Reference 23

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Observation a1329b3e-f444-4b2e-8ebd-2b0b61dff42e · outbound

This paper cites Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.154120Z

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Observation dfff2b9a-4be8-466f-ae50-4006069fb51b · outbound

This paper cites Causal Abstraction Learning based on the Semantic Embedding Principle.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Causal Abstraction Learning based on the Semantic Embedding Principle

Reference 25

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Observation 6d3158ef-7e9f-4636-8074-a3d577f81bdb · outbound

This paper cites Learning consistent causal abstraction networks,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Learning consistent causal abstraction networks,

Reference 26

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Observation fafb6cb5-d33d-43bf-904c-db13eda4a6f6 · outbound

This paper cites A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices

Reference 27

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Observation e8c948dd-793f-4fa7-81d2-932184ce35e3 · outbound

This paper cites Predictive learning enables compositional representations,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Predictive learning enables compositional representations,

Reference 28

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Observation f41651d6-d497-4dca-ba93-b13b198eea3e · outbound

This paper cites The role of fibration symmetries in geometric deep learning,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory The role of fibration symmetries in geometric deep learning,

Reference 29

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Observation c3de7377-2eb1-4bcf-8e93-823e217c4ed6 · outbound

This paper cites Th ´eorie des topos et Cohomologie Etale des Sch ´emas ,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Th ´eorie des topos et Cohomologie Etale des Sch ´emas ,

Reference 30

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This paper cites Topos and Stacks of Deep Neural Networks.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Topos and Stacks of Deep Neural Networks

Reference 31

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This paper cites Unsupervised learning of compositional energy concepts,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Unsupervised learning of compositional energy concepts,

Reference 32

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Observation 2320ff5f-89fe-44aa-ad6c-436cea7b99fb · outbound

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Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Lenses in functional programming,

Reference 33

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Observation 5dbac4df-afa0-41fe-ad65-fcdc17853668 · outbound

This paper cites Categories of Semantic Concepts.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Categories of Semantic Concepts

Reference 34

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Observation 6e73dae2-17be-4d9f-8f80-5d568e0391ab · outbound

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Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Stackelberg game for utility-based cooperative cognitive radio networks,

Reference 35

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This paper cites Managing price uncertainty in prosumer-centric energy trading: A prospect-theoretic stackelberg game approach,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Managing price uncertainty in prosumer-centric energy trading: A prospect-theoretic stackelberg game approach,

Reference 36

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Observation a15d6c07-3138-4104-912a-4b7bcb095f24 · outbound

This paper cites A generalization of the maximum theorem,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory A generalization of the maximum theorem,

Reference 37

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Observation 2391881d-a2ad-4795-b073-9fa3f0f0da96 · outbound

This paper cites On nonlinear fractional programming,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory On nonlinear fractional programming,

Reference 38

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Observation ac3e248d-5edc-4d23-a68e-b8bdd42840e8 · outbound

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Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 39

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Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Carla: An open urban driving simulator,

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Observation a7a1da28-9f8e-4d86-97ed-9a8b5312dffd · outbound

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Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Microsoft coco: Common objects in context,

Reference 41

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Observation bfd5d574-8f82-4cba-a05b-3e7868411e0e · outbound

This paper cites Existence and uniqueness of equilibrium points for concave n-person games,.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Existence and uniqueness of equilibrium points for concave n-person games,

Reference 42

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This paper cites For composed objectsz 1 ⊕z ′ 1 ∈ Z1 and Z2 ⊕z ′ 2 ∈ Z2, the fibrational structure is preserved when p# 1 (z1 ⊕z ′.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory For composed objectsz 1 ⊕z ′ 1 ∈ Z1 and Z2 ⊕z ′ 2 ∈ Z2, the fibrational structure is preserved when p# 1 (z1 ⊕z ′

Reference 43

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Observation 23b456b8-0f02-4784-9ba5-38c427ca9cbc · outbound

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Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Unresolved cited work

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Observation ad820655-8e34-4114-a746-427064986eaa · outbound

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Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Unresolved cited work

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Observation ce66841a-2f8c-43bb-8b24-c0989e6c06a0 · outbound

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Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Unresolved cited work

Reference 46

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Observation a3d1d644-6295-4516-80a5-cc863556e7ea · outbound

This paper cites Since both map toℓ⊗ℓ ′, the composed pair remains in the fiber product overℓ⊗ℓ ′, preserving the compositional structure.

Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory Since both map toℓ⊗ℓ ′, the composed pair remains in the fiber product overℓ⊗ℓ ′, preserving the compositional structure

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T16:11:40.247272Z

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Pith citing papers

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