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Paper Citation Record · LEDGER

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

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 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 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:31:57.854859Z

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 6df74f81-8234-487e-bfc0-bc4845252b47 · inbound

Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgment cites this paper.

Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgment From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-16T12:31:57.854859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:31:57.854859Z digest=sha256:1e0ba2bb7f745d0938280521eef821381d2ed5407a8041da66ae5f5fb70eba18

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.

source=arxiv_source observed=2026-08-07T12:18:52.350333Z digest=sha256:0ce88d46d29a81e62adb267ed5a7cd537681d8d253773137e9b20fcececf67fb

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T05:41:58.231139Z digest=sha256:934996438463ca9ad9d0059512e90dcebab404c9d37c4dbc4f27cf35af7b0b87

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.

source=pdf_text observed=2026-08-02T23:53:03.339383Z digest=sha256:abdf83392e21733592223f57539fb1e409e78ea5560faaf96af17d6b45facccd

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T16:52:34.799158Z digest=sha256:2e86701a7151edf2213c4a04fc71e030f6f70a5a7adceaf1c4e9ee199d29558a

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T08:02:10.327523Z digest=sha256:a752e9d483a8e120b6bf145d6f45b57c88e2a4955beedbeed037e640fcdc2815

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-12T03:10:51.555653Z digest=sha256:c95bf47373df889d044f540ea6963d6a2a258f52167bf0f7ada6d2cdc401b9fc

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-28T14:51:27.110839Z digest=sha256:88f2e2e1bddf12fd5a9a6a02d7dba53559b862a02dcc07608bba401477c14d2e

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-27T16:49:14.243931Z digest=sha256:e93dcd443fdb923a476b16bfa017468bfb3b171f255e295cb181388c00483fb2