Paper Citation Record · LEDGER

iAgent: LLM Agent as a Shield between User and Recommender Systems

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.14662.

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

pith.paper-citation-record.v1
2502.14662 v4

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 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:00:26.767130Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T18:42:29.146302Z

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 9f98b56f-5568-4f7b-b4b4-5639a00bf0ea · inbound

Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents cites this paper.

Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents iAgent: LLM Agent as a Shield between User and Recommender Systems

Reference 151

Resolution: verified exact; arxiv_id, observed 2026-05-12T13:36:57.206184Z

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 77779603-556f-496b-82ee-b15af2befd56 · inbound

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders cites this paper.

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders iAgent: LLM Agent as a Shield between User and Recommender Systems

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:26.767130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f42eb2d-bdc9-4a29-84e0-33982710a516 · inbound

RecoWorld: Building Simulated Environments for Agentic Recommender Systems cites this paper.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems iAgent: LLM Agent as a Shield between User and Recommender Systems

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T17:56:38.861709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c40b7d4d-2962-4dc1-9171-b82dab80ce17 · inbound

Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation cites this paper.

Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation iAgent: LLM Agent as a Shield between User and Recommender Systems

Reference 128

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:31:22.323119Z

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 01a61dc4-8ca2-4a7a-96ff-687d9f07dd99 · inbound

RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems cites this paper.

RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems iAgent: LLM Agent as a Shield between User and Recommender Systems

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.491256Z

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 c48bad9e-8fab-4c32-801e-4d765168b2fd · inbound

Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges cites this paper.

Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges iAgent: LLM Agent as a Shield between User and Recommender Systems

Reference 271

Resolution: verified exact; arxiv_id, observed 2026-06-28T18:42:29.147943Z

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.