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Paper Citation Record · LEDGER

SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2405.17890.

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

pith.paper-citation-record.v1
2405.17890 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:16:55.401740Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:59:36.447967Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 534e8948-2c21-4721-a58e-99ade183079e · inbound

Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents cites this paper.

Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 149

Resolution
verified exact
arxiv_id, observed 2026-05-12T13:36:57.199148Z

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.

source=arxiv_source observed=2026-05-12T13:36:57.011451Z digest=sha256:f0b146b66a67dd4864f1e521a4280522fc84f313d7633a97da335a5972047074

Observation 7415d9d1-2109-4623-ab93-251efe366dc1 · inbound

A Survey on Sequential Recommendation cites this paper.

A Survey on Sequential Recommendation SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 161

Resolution
unresolved
no resolver link, observed 2026-08-11T13:47:37.859294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:47:37.859294Z digest=sha256:62afc21b9b8e7cfd0cb9538ef9604f567773a4b3d50d76b5b3e570379e07e815

Observation a5a068b5-2ecf-4367-b303-45f100bdab14 · inbound

Large Language Model Enhanced Recommender Systems: A Survey cites this paper.

Large Language Model Enhanced Recommender Systems: A Survey SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 93

Resolution
unresolved
no resolver link, observed 2026-08-11T13:11:48.804796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:11:48.804796Z digest=sha256:edd990833b51b410f670eda1f0909596b0dca60dc12dc69759b28709317be987

Observation 598d393b-b37e-47a5-a6cc-d94a09ac037e · inbound

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models cites this paper.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:17.811380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:17.811380Z digest=sha256:0c3858cbc80d6c46b99c3b4fa8283a8dabf68d5775d80e3777364e4249189e36

Observation e17eb3dd-9035-4ab3-a4fa-8bb090dd240c · inbound

Sensory-Aware Sequential Recommendation via Review-Distilled Representations cites this paper.

Sensory-Aware Sequential Recommendation via Review-Distilled Representations SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-15T17:40:11.657433Z

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.

source=pdf_text observed=2026-05-15T17:38:39.607878Z digest=sha256:056ef763b6be39a7d7ed38ca9eca360a895a1095285445050f625feda91658e4

Observation 8b2a197d-5def-4226-bee7-33564836cdde · inbound

LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning cites this paper.

LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-10T11:20:10.457959Z

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.

source=arxiv_source observed=2026-05-10T11:17:43.769244Z digest=sha256:d38d41710941d46f7b7a6add0cf8c4d2d7f6d6e15ee078a67b49d030831de8a9

Observation 21adb2fa-ae19-4a18-98a7-a2cac64849d5 · inbound

Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation cites this paper.

Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 129

Resolution
verified exact
arxiv_id, observed 2026-05-15T17:31:22.138127Z

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.

source=pdf_text observed=2026-05-15T17:30:44.919870Z digest=sha256:ff43b3a6d9e426bd80dccbfd3b5684483c12c659cd356cc47f6289212e00a497

Observation 99d34f89-89a2-455a-a6f5-048d61e9693c · inbound

Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation cites this paper.

Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:59:36.451418Z

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.

source=pdf_text observed=2026-06-26T15:16:26.994672Z digest=sha256:9389bde495d44fb4227f6f8e87dd6da8def4ba0a11823ca8f9fef902c11ae7de

Observation 33e640b5-cb3a-498e-a290-0c8c883d9727 · inbound

TSPORec: Token Selection via Preference Optimization for LLM-Based Sequential Recommendation cites this paper.

TSPORec: Token Selection via Preference Optimization for LLM-Based Sequential Recommendation SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-11T14:16:55.401740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:16:55.401740Z digest=sha256:51568f650eaf116224f5492667eb42517a268058feca988fbec8adf77d46a332