Paper Citation Record · LEDGER

Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2104.09122.

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

pith.paper-citation-record.v1
2104.09122 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:32:45.197714Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:03:29.510145Z

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 00df51e9-3f83-425c-9503-fa60fe2fb8de · inbound

SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks cites this paper.

SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T13:32:45.197714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea0db956-54b8-494e-8960-ad7c8ce5a80b · 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 Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:38:44.978941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 94c67878-6e7c-49af-a7fb-a6104e4d653d · inbound

Revisiting Mixture Policies in Entropy-Regularized Actor-Critic cites this paper.

Revisiting Mixture Policies in Entropy-Regularized Actor-Critic Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:11:22.787704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0b15a8b1-b9e7-4dbb-b601-533ec584e0e5 · inbound

Moment Matching Q-Learning cites this paper.

Moment Matching Q-Learning Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T14:03:29.511711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 60c14fc4-9aca-485b-8690-346e3ce95059 · inbound

LooperMuscle: Fast and Stable Learning of Humanoid Whole-Body Tracking via Structured Mixture-of-Experts cites this paper.

LooperMuscle: Fast and Stable Learning of Humanoid Whole-Body Tracking via Structured Mixture-of-Experts Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T00:17:17.853909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.