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Paper Citation Record · LEDGER

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime

As of 14 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.14951.

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

pith.paper-citation-record.v1
2507.14951 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:48:43.995489Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

35 of 35 outbound references displayed

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  • verified fuzzy29
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 44dbb24b-26ff-4598-b0a7-88e691a3c35f · outbound

This paper cites On the road to 6G: Visions, requirements, key tech- nologies and testbeds,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime On the road to 6G: Visions, requirements, key tech- nologies and testbeds,

Reference 1

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Observation c81a46a8-ca76-494f-8a64-d7c002e1c4d5 · outbound

This paper cites Disentangled representation learning empowered CSI feedback using implicit channel reciprocity in FDD massive MIMO,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Disentangled representation learning empowered CSI feedback using implicit channel reciprocity in FDD massive MIMO,

Reference 2

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Observation 0c371be1-d6f0-4086-9596-ec8953289057 · outbound

This paper cites A mathematical theory of communication,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime A mathematical theory of communication,

Reference 3

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Observation 2517e41c-0ae9-4d8d-a35c-8b9ae99fcd42 · outbound

This paper cites Channel coding: The road to channel capacity,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Channel coding: The road to channel capacity,

Reference 4

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Source-reported events for the cited work

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Observation 83d1240d-dc0c-47fb-82e4-37540dc8c35f · outbound

This paper cites Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,

Reference 5

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Observation c5beb9ec-6914-4feb-a951-fc5f5402681b · outbound

This paper cites Near shannon limit error- correcting coding and decoding: Turbo-codes,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Near shannon limit error- correcting coding and decoding: Turbo-codes,

Reference 6

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Source-reported events for the cited work

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Observation 417a4c12-53e2-4250-8b3f-e46ef2af171a · outbound

This paper cites Low-density parity-check codes,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Low-density parity-check codes,

Reference 7

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Observation 27edba0e-901c-455e-8c82-5e5494ea12cb · outbound

This paper cites Channel polarization: A method for constructing capacity achieving codes for symmetric binary-input memoryless channels,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Channel polarization: A method for constructing capacity achieving codes for symmetric binary-input memoryless channels,

Reference 8

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Observation 59e70d8f-094c-453d-acd5-6a926aa5a17d · outbound

This paper cites On the rate of channel polarization,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime On the rate of channel polarization,

Reference 9

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Observation b269cde8-1b1c-4fb8-b4e9-0cbb46b355d4 · outbound

This paper cites CRC-aided decoding of polar codes,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime CRC-aided decoding of polar codes,

Reference 10

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Source-reported events for the cited work

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Observation 4927e88e-8b0e-4781-9d2b-21315138631a · outbound

This paper cites 6G: A welcome chance to unify channel coding?.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime 6G: A welcome chance to unify channel coding?

Reference 11

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verified fuzzy
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Source-reported events for the cited work

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Observation bff9cfce-71e7-4041-94eb-7c38deeb22c9 · outbound

This paper cites Channel coding for 6G extreme connectivity requirements, capabilities and fundamental tradeoffs,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Channel coding for 6G extreme connectivity requirements, capabilities and fundamental tradeoffs,

Reference 12

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Source-reported events for the cited work

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Observation 4eb7a457-c1dc-4d58-805e-9d6364548734 · outbound

This paper cites Recent advances in deep learning for channel coding: A survey,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Recent advances in deep learning for channel coding: A survey,

Reference 13

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Source-reported events for the cited work

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Observation b7c8b2fd-e4e7-48db-bfd1-47aac28f4053 · outbound

This paper cites Multilayer feedforward networks are universal approximators,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Multilayer feedforward networks are universal approximators,

Reference 14

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Observation 5321fa0a-eb7d-419c-81f9-b04c51f04082 · outbound

This paper cites In-datacenter performance analysis of a tensor processing unit,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime In-datacenter performance analysis of a tensor processing unit,

Reference 15

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Observation c35eaf45-c57e-4f29-9227-86d19b9de0b6 · outbound

This paper cites Comparing energy efficiency of CPU, GPU and FPGA implementations for vision kernels,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Comparing energy efficiency of CPU, GPU and FPGA implementations for vision kernels,

Reference 16

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Observation 98ee87ea-47ea-4646-b8f5-502e9ecf7850 · outbound

This paper cites List decoding of polar codes,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime List decoding of polar codes,

Reference 17

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Observation f1cc61b9-cf82-4fc4-ac15-b4daa0bf003b · outbound

This paper cites List successive cancellation decoding of polar codes,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime List successive cancellation decoding of polar codes,

Reference 18

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Observation 7e73b354-a587-4edc-85e2-62a251e24a3e · outbound

This paper cites Stack decoding of polar codes,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Stack decoding of polar codes,

Reference 19

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Observation d03bb40c-e2ff-4b26-aede-dcd126377655 · outbound

This paper cites On deep learning- based channel decoding,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime On deep learning- based channel decoding,

Reference 20

Resolution
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Source-reported events for the cited work

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Observation 756b8505-88ca-4a12-b0ff-10f07987bb8c · outbound

This paper cites Performance evaluation of channel decoding with deep neural networks,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Performance evaluation of channel decoding with deep neural networks,

Reference 21

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Observation e3c58108-7c57-487b-b512-b30322021f45 · outbound

This paper cites Performance analysis of deep learning based on recurrent neural networks for channel coding,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Performance analysis of deep learning based on recurrent neural networks for channel coding,

Reference 22

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Observation 8513fd19-682a-4c27-9f21-1ec1f83402e3 · outbound

This paper cites Attention is all you need,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Attention is all you need,

Reference 23

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Observation 39dc030b-cc63-4fcd-a70c-88979522e5c8 · outbound

This paper cites Error correction code transformer,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Error correction code transformer,

Reference 24

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Observation 0a05804b-205b-4abc-a35d-71304c253def · outbound

This paper cites CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes

Reference 25

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Source-reported events for the cited work

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Observation 102f3a15-eb3a-46da-b517-71fabb42dce3 · outbound

This paper cites On the design and performance of machine learning based error correcting decoders,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime On the design and performance of machine learning based error correcting decoders,

Reference 26

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verified exact
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Source-reported events for the cited work

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Observation bd9cc24d-6b12-4b27-9fe3-66b24a454492 · outbound

This paper cites Efficient design and decoding of polar codes,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Efficient design and decoding of polar codes,

Reference 27

Resolution
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Source-reported events for the cited work

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Observation d7e74d98-043b-4e06-81c5-1606eea3094c · outbound

This paper cites Beyond dis- crete selection: continuous embedding space optimization for generative feature selection,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Beyond dis- crete selection: continuous embedding space optimization for generative feature selection,

Reference 28

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Source-reported events for the cited work

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Observation 35a28022-ebac-45cd-9897-fcba2d1f11f3 · outbound

This paper cites Mish: A Self Regularized Non-Monotonic Activation Function.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Mish: A Self Regularized Non-Monotonic Activation Function

Reference 29

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Source-reported events for the cited work

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Observation 1dfd3cc9-4350-4e04-a466-e8b559d10e06 · outbound

This paper cites Deep residual learning for image recognition,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Deep residual learning for image recognition,

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6266ede5-f260-435c-8309-1eb3c02afdf1 · outbound

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Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Layer Normalization

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 49b16581-cd87-4835-b772-4185be9240bd · outbound

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Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Rethinking the inception architecture for computer vision,

Reference 32

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Source-reported events for the cited work

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Observation 990e2496-74d4-44d2-91fd-52a7bf714801 · outbound

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Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Tweedie’s formula and selection bias,

Reference 33

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 42c924eb-6d24-48ff-bb04-79eeedbfbe65 · outbound

This paper cites A fast iterative shrinkage-thresholding algorithm with application to wavelet-based image deblurring,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime A fast iterative shrinkage-thresholding algorithm with application to wavelet-based image deblurring,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 549034b1-add5-46b0-b107-48774e0de3aa · outbound

This paper cites White-box transformers via sparse rate reduction,.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime White-box transformers via sparse rate reduction,

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