Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.18074.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:53:56.861783Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T03:57:29.566505Z
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 4ba7139b-9677-4681-8c28-d094e6eb3daa · inbound
How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators cites this paper.
How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts
Reference 50
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T03:57:29.569434Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e77fd1d1-d101-4307-981e-d7c0825b57df · inbound
Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition cites this paper.
Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:53:56.861783Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a279eaf7-73e6-4a95-a82c-a8700d157bfd · inbound
Incentivizing High-Quality Human Annotations with Golden Questions cites this paper.
Incentivizing High-Quality Human Annotations with Golden Questions Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T13:42:19.355427Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2a5d5be8-b2db-4bc6-9ff9-38e96037ce2e · inbound
Clutter Detection and Removal by Multi-Objective Analysis for Photographic Guidance cites this paper.
Clutter Detection and Removal by Multi-Objective Analysis for Photographic Guidance Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:58:35.265420Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8a2cea7-0a82-4ab3-b5de-826e458c5c16 · inbound
Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective cites this paper.
Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:06:23.215338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.