Paper Citation Record · LEDGER

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization

As of 17 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 2 inbound Pith citation observations for arXiv:2411.19719.

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

pith.paper-citation-record.v1
2411.19719 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:57:05.990156Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:45:03.878204Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:23:14.821020Z

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy13
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84402e1c-b43b-43d3-ad58-fc3f450c9b6c · outbound

This paper cites The mathematical theory of communication,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization The mathematical theory of communication,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.251321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e3cfa3de-bb1e-4771-bb4d-cd9a1d456735 · outbound

This paper cites Deep joint source- channel coding for wireless image transmission,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Deep joint source- channel coding for wireless image transmission,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.237012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 86c27606-ce8c-4898-a7be-0d08e248933c · outbound

This paper cites Effective communi- cations: A joint learning and communication framework for multi-agent reinforcement learning over noisy channels,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Effective communi- cations: A joint learning and communication framework for multi-agent reinforcement learning over noisy channels,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.222517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4085a00d-0b46-4014-8808-6cbd9c6c04a1 · outbound

This paper cites Relative representations enable zero-shot latent space com- munication,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Relative representations enable zero-shot latent space com- munication,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.207577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1ab5dac-61b3-456c-a119-18725ca17016 · outbound

This paper cites Learning semantics: An opportunity for effective 6g communications,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Learning semantics: An opportunity for effective 6g communications,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.193097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 21c9b8af-1452-44c3-acad-3d26cc12d526 · outbound

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

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Semantic channel equalizer: Modelling language mismatch in multi-user semantic communications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.178821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a7441650-2ce9-4bbe-80af-05e82bf2d43b · outbound

This paper cites Soft Partitioning of Latent Space for Semantic Channel Equalization.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Soft Partitioning of Latent Space for Semantic Channel Equalization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T05:57:05.947917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6220cf33-5dd8-4eb6-a8b8-b6f89a069a25 · outbound

This paper cites Zero-shot stitching in reinforcement learning using relative representations,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Zero-shot stitching in reinforcement learning using relative representations,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.164220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2dabfa28-3048-48a5-8d50-275688afab7d · outbound

This paper cites From bricks to bridges: Product of invariances to enhance latent space communication,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization From bricks to bridges: Product of invariances to enhance latent space communication,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.149273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7a39f69a-603f-4aa7-8356-136caf29842a · outbound

This paper cites Asif: Coupled data turns unimodal models to multimodal without training,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Asif: Coupled data turns unimodal models to multimodal without training,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.131743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 21321d1a-dab8-47b3-a88f-12bd7b663e85 · outbound

This paper cites Dynamic Relative Representations for Goal-Oriented Semantic Communications.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Dynamic Relative Representations for Goal-Oriented Semantic Communications

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T05:57:05.966194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c357a58c-e41f-4c49-98ce-55a69c1c4622 · outbound

This paper cites Latent Space Translation via Inverse Relative Projection.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Latent Space Translation via Inverse Relative Projection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T05:57:05.972275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf124216-852c-4d85-b03d-ba360f4019b8 · outbound

This paper cites Prototypical networks for few- shot learning,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Prototypical networks for few- shot learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.117008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 348744cb-e76e-47d2-bf37-9b1a6a5d565e · outbound

This paper cites Hugging face’s tiny-imagenet repository.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Hugging face’s tiny-imagenet repository

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.102317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5bca9afb-18ec-44d6-95b8-0ecf6602650e · outbound

This paper cites Hugging face.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Hugging face

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.087043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bcbf89f9-26ff-45d0-bc60-e2f6e0629f38 · outbound

This paper cites Adam: A method for stochastic optimization,.

Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization Adam: A method for stochastic optimization,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:57:06.071730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 7b037b16-3529-4fd9-8d4b-0f03388c095d · inbound

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

Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:03.878204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56260e08-d826-4fe4-8a43-df5fb855bf23 · inbound

Improving Relative Representations with Learned Anchors and Whitened Inner Products cites this paper.

Improving Relative Representations with Learned Anchors and Whitened Inner Products Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:23:14.822516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.