Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2409.20163.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T20:02:12.525604Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T21:53:59.997347Z
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 a8f072c7-c1e6-44bc-be4d-f1997ceec172 · inbound
From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs cites this paper.
From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Reference 76
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T11:05:09.757302Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c41aafb3-6bb9-4ef1-bd72-70b85bed56c3 · inbound
Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework cites this paper.
Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:02:12.525604Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 17a35a34-af8f-42fd-989a-85aeace11b05 · inbound
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory cites this paper.
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Reference 135
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-14T23:13:16.131595Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e4fe2318-5b24-4a6e-b052-8c04070d6751 · inbound
Opal: Private Memory for Personal AI cites this paper.
Opal: Private Memory for Personal AI MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Reference 278
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T21:13:16.710924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 15ecdc81-8d4e-4685-a94e-e21073cdd042 · inbound
From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents cites this paper.
From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Reference 12
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T13:16:03.474741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7bcd85a1-bc08-4bf2-87be-01229720713e · inbound
Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory cites this paper.
Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T15:51:26.918367Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 053a0ac7-26a2-43cc-95d0-9b481184416a · inbound
MEMAUDIT: An Exact Package-Oracle Evaluation Protocol for Budgeted Long-Term LLM Memory Writing cites this paper.
MEMAUDIT: An Exact Package-Oracle Evaluation Protocol for Budgeted Long-Term LLM Memory Writing MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T05:50:26.115340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6d3303a7-8b14-415d-9408-4e91e5d6e03d · inbound
Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents cites this paper.
Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T21:53:59.998819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 52797fc5-9c99-412b-ae54-eabc3e9912c0 · inbound
VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions cites this paper.
VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T17:13:44.719403Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.