Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2407.17466.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T19:53:09.027483Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T06:44:00.901151Z
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 b41119ee-d208-4a11-b610-82002cf7e145 · inbound
Multi-objective Large Language Model Alignment with Hierarchical Experts cites this paper.
Multi-objective Large Language Model Alignment with Hierarchical Experts Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:52:12.434324Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 51ab15c2-aa1a-4d51-9507-df65c499ee88 · inbound
Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach cites this paper.
Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T12:56:58.106357Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf288d27-4fa1-4a5c-8e0b-ee6f1ca3725e · inbound
A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning cites this paper.
A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:51:29.689165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7d4ad250-cbd8-4c5f-9b50-c20acec4f67b · inbound
Contextual Multi-Objective Optimization: Rethinking Objectives in Frontier AI Systems cites this paper.
Contextual Multi-Objective Optimization: Rethinking Objectives in Frontier AI Systems Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T23:41:18.942894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ce8807aa-1619-4c9c-816a-47c858360871 · inbound
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization cites this paper.
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T19:32:51.671927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fdf2ba28-614f-43f0-b7f6-e57dda7c0738 · inbound
SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front cites this paper.
SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Reference 73
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T06:44:00.902598Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 573a5b80-221b-4ab5-b35d-a7b27625e9ea · inbound
Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability cites this paper.
Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T05:38:58.035337Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c10cf1e-909c-499b-9ac3-035e593912f3 · inbound
Efficient Online Lexicographic Generalized Low-Rank Matrix Bandits cites this paper.
Efficient Online Lexicographic Generalized Low-Rank Matrix Bandits Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:53:09.027483Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0972ff51-7be1-487c-8a16-2764c24ce67a · inbound
Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluation cites this paper.
Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluation Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:48:08.805698Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.