Paper Citation Record · LEDGER

From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2503.15463.

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

pith.paper-citation-record.v1
2503.15463 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:18:52.350333Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T01:07:30.291749Z

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 c7406791-0a5b-4b68-bfd4-2702022c5c58 · inbound

Memory OS of AI Agent cites this paper.

Memory OS of AI Agent From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:18:52.350333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 256e4fa6-0f79-4e28-b308-10bbba170862 · inbound

POPI: Personalizing LLMs via Optimized Natural Language Preference Inference cites this paper.

POPI: Personalizing LLMs via Optimized Natural Language Preference Inference From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:42:24.371256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e0fbe002-c5e9-4174-9c51-a59a83f4c723 · inbound

Synthetic Interaction Data for Scalable Personalization in Large Language Models cites this paper.

Synthetic Interaction Data for Scalable Personalization in Large Language Models From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T23:53:03.339383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 553876af-8ac2-4e1d-88bb-d280a40fac97 · inbound

Efficient Personalization of Generative User Interfaces cites this paper.

Efficient Personalization of Generative User Interfaces From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:00:57.591429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd7ed764-8da6-4f0f-b823-6bc9a3663a06 · inbound

PersonaVLM: Long-Term Personalized Multimodal LLMs cites this paper.

PersonaVLM: Long-Term Personalized Multimodal LLMs From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-15T08:05:15.530449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f48494e1-ddf4-47a6-9f6e-c55804d45b45 · inbound

UserGPT Technical Report cites this paper.

UserGPT Technical Report From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:18.692932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2e027efd-9e28-4be9-926e-4593c221da62 · inbound

Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization cites this paper.

Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:56:20.556048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e70c0c1a-9369-4c04-8520-fea93e3cb65f · inbound

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs cites this paper.

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:07:30.293648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.