Paper Citation Record · LEDGER

Everyone Deserves A Reward: Learning Customized Human Preferences

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2309.03126.

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

pith.paper-citation-record.v1
2309.03126 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:32:04.851408Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:57:23.603269Z

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 9075cef7-19a1-494c-bc6a-2a7586133b8c · inbound

On the Way to LLM Personalization: Learning to Remember User Conversations cites this paper.

On the Way to LLM Personalization: Learning to Remember User Conversations Everyone Deserves A Reward: Learning Customized Human Preferences

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T16:32:04.851408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3edb31a-b86a-4203-82c8-1c75b9488755 · inbound

Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment cites this paper.

Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment Everyone Deserves A Reward: Learning Customized Human Preferences

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:26.643340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60047f86-423c-441b-905a-e404170baa41 · inbound

Aligning VLM Assistants with Personalized Situated Cognition cites this paper.

Aligning VLM Assistants with Personalized Situated Cognition Everyone Deserves A Reward: Learning Customized Human Preferences

Reference 1989

Resolution: unresolved; no resolver link, observed 2026-08-07T11:59:39.834681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f7e42d3-f75e-4e50-993c-6f6f9a3b0a59 · inbound

PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants cites this paper.

PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants Everyone Deserves A Reward: Learning Customized Human Preferences

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T04:42:27.657938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a57490a-1db6-40cd-8f02-6deee861bca8 · inbound

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill cites this paper.

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill Everyone Deserves A Reward: Learning Customized Human Preferences

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:26:27.214211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fb0b442f-5370-4454-97c0-6fab6dfe6304 · inbound

PAFO: Pareto Fairness Optimization for Personalized Reward Modeling cites this paper.

PAFO: Pareto Fairness Optimization for Personalized Reward Modeling Everyone Deserves A Reward: Learning Customized Human Preferences

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:57:23.604901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.