Paper Citation Record · LEDGER

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation

As of 21 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2606.01844.

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

pith.paper-citation-record.v1
2606.01844 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T12:52:29.176466Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T04:08:29.767676Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

verified exact3
verified fuzzy0
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 07050517-3747-4144-8bd4-145882ca9189 · outbound

This paper cites Recommender systems with generative retrieval.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Recommender systems with generative retrieval

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-28T12:52:29.176466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b31a8b9a-863f-42af-a2c0-bf0dd896215a · outbound

This paper cites FineVideo: Afine-graineddatasetforvideounderstanding.arXiv preprint arXiv:2405.00000.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation FineVideo: Afine-graineddatasetforvideounderstanding.arXiv preprint arXiv:2405.00000

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:06:23.971995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 12fcf5f4-a5c2-4f6d-b2bf-1f1a103d5bc2 · outbound

This paper cites Product quantization for nearest neighbor search.IEEE transactions on pattern analysis and machine intelligence, 33(1):117–128, 2010.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Product quantization for nearest neighbor search.IEEE transactions on pattern analysis and machine intelligence, 33(1):117–128, 2010

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T12:52:29.176466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adbf2808-b061-47fb-b8a7-28d72bf02daa · outbound

This paper cites Soundstream: An end-to-end neural audio codec.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Soundstream: An end-to-end neural audio codec

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-28T12:52:29.176466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 755bbb94-492f-48cd-afef-c0873d1c20d9 · outbound

This paper cites Autocoder: Autonomous code generation with large language models,.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Autocoder: Autonomous code generation with large language models,

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:06:23.978323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5dbd4501-012b-4bb7-97be-6d359b59ff08 · outbound

This paper cites Neural discrete representation learning.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Neural discrete representation learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T12:52:29.176466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab2a0eab-521a-496a-b2ee-1841d1786efa · outbound

This paper cites Understanding dimen- sional collapse in contrastive self-supervised learning.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Understanding dimen- sional collapse in contrastive self-supervised learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-28T12:52:29.176466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1619940f-962d-44df-b26c-e4a2d9cb39b8 · outbound

This paper cites Least squares quantization in pcm.IEEE transactions on information theory, 28(2):129–137, 1982.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Least squares quantization in pcm.IEEE transactions on information theory, 28(2):129–137, 1982

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T12:52:29.176466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe84ab6a-733e-49ed-bcf0-0bf996c23137 · outbound

This paper cites Rq-gmm: Residual quantized gaussian mixture model for multimodal semantic discretization in ctr prediction.arXiv preprint arXiv:2602.12593, 2026.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Rq-gmm: Residual quantized gaussian mixture model for multimodal semantic discretization in ctr prediction.arXiv preprint arXiv:2602.12593, 2026

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:06:23.975495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e5e076bf-022a-4fb5-86e6-71417086ccbf · outbound

This paper cites Latent space oddity: on the curvature of deep generative models.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Latent space oddity: on the curvature of deep generative models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T12:52:29.176466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9a5dcfa-2fe8-4c26-a8a3-00db49ec2109 · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Understanding contrastive representation learning through alignment and uniformity on the hypersphere

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T12:52:29.176466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2464641e-d40b-44a6-a1fa-3ac219290a29 · outbound

This paper cites Representation Degeneration Problem in Training Natural Language Generation Models.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Representation Degeneration Problem in Training Natural Language Generation Models

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:06:23.981168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bef2a60e-0158-4c14-a449-57e70ceec6c4 · outbound

This paper cites How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:06:23.978491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9c294a8e-6e72-4f5d-a0bb-fa52dacca218 · outbound

This paper cites Deep learning and the information bottleneck principle.IEEE information theory workshop (ITW), pages 1–5, 2015.

Decoupled Residual Quantization for Robust Semantic IDs in Recommendation Deep learning and the information bottleneck principle.IEEE information theory workshop (ITW), pages 1–5, 2015

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T12:52:29.176466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation f0380569-2a6f-4f54-8da1-836cf75dff9e · inbound

Understanding Semantic IDs: From Item Representation to Item Selection in Generative Recommendation cites this paper.

Understanding Semantic IDs: From Item Representation to Item Selection in Generative Recommendation Decoupled Residual Quantization for Robust Semantic IDs in Recommendation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-31T04:08:29.767676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.