Paper Citation Record · LEDGER

HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2505.00038.

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

pith.paper-citation-record.v1
2505.00038 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:25:08.496563Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T19:32:35.359433Z

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 657ad2b4-06a1-49ca-9bc5-671194875d54 · inbound

Prompting as Scientific Inquiry cites this paper.

Prompting as Scientific Inquiry HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T21:25:08.496563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 858f57de-4033-4547-893b-76d7fd0a6643 · inbound

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

POPI: Personalizing LLMs via Optimized Natural Language Preference Inference HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:42:24.325278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d091d356-82f2-4be0-b62d-f74e3ef4984d · inbound

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

Synthetic Interaction Data for Scalable Personalization in Large Language Models HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T23:53:02.013695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f216833-5d63-4484-9afb-6157de614ae0 · inbound

Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences cites this paper.

Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:32:35.360843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.