Paper Citation Record · LEDGER

Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2311.11385.

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

pith.paper-citation-record.v1
2311.11385 v2

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 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:03:23.515122Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T00:38:44.971183Z

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 7a321585-e291-49c4-9d6e-32adc78b4397 · inbound

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks cites this paper.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T11:03:23.515122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c63b9ad-fab1-45d6-b894-60df4a425c10 · inbound

Prismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems cites this paper.

Prismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:38:44.974344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd1c3ec0-4939-4987-be40-97b8ffcb68a6 · inbound

Diffusion Policy with Bayesian Expert Selection for Active Multi-Target Tracking cites this paper.

Diffusion Policy with Bayesian Expert Selection for Active Multi-Target Tracking Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:28:06.410255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8dde9a49-2bd8-4e68-9f8a-8a75aeccbca9 · inbound

FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning cites this paper.

FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:31:25.637452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f69d0391-fd13-4e3f-9638-b707064873c4 · inbound

TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing cites this paper.

TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T01:52:05.760292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6638c425-8250-4758-bcad-6f9428afe809 · inbound

Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks cites this paper.

Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-31T19:04:33.760614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.