Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 27 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2310.20081.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-27T06:30:09.085275+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-03T18:20:53.930801Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation 3090a876-ba82-42f9-8d5e-edcfc16f6492 · inbound
CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Reference 6
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 65ab702d-2b06-4cbf-8951-6dd753364a32 · inbound
TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Reference 50
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 0cb9e098-1347-4046-8952-3bd7fc18e1e4 · inbound
Personalizing LLMs with Binary Feedback: A Preference-Corrected Optimization Framework Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Reference 5
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation a8951cc3-f943-4a96-8ab9-fa16fc55cb27 · inbound
VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Reference 25
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation b5086f4e-590a-4b9a-8f16-fd5992252f3e · inbound
Preference-Aware Rubric Learning for Personalized Evaluation Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Reference 21
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 5c103e5a-cc7e-4dd2-a233-8414391f7f08 · inbound
Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Reference 101
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 4d03f46c-bc27-462f-904d-46c3194fd7ab · inbound
Beyond Retrieval: Learning Compact User Representations for Scalable LLM Personalization Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Reference 110
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation c0255970-06f7-4397-8ea7-0e379e4bdfae · inbound
CoPersona: Collaborative Persona Graphs for Robust LLM Personalization Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Reference 49
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.