Pith. sign in

Paper Citation Record · LEDGER

Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:29:51.318798Z

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 3b3aecd5-822b-468f-9cbd-88eff00ee65b · inbound

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning cites this paper.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-10T19:29:51.318798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.318798Z digest=sha256:1eb4b852650d2fceedd29277d91fca2306db3c617d4b481b50ec1956fa8e5cb7

Observation 998767c1-e8e4-42ac-935d-36bbe357f3fd · inbound

Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer cites this paper.

Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:13.079539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:35:13.079539Z digest=sha256:8df54332ca4002b3bfcd131b78f0c0dc16180c9e83f7ad89a4eda8a32669035f

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.

source=arxiv_source observed=2026-08-06T11:03:23.515122Z digest=sha256:cd70199c1f7cd4eaf90461ef36c690bb2d9a3cf0ba7dd20f6d0935aa5096cf57

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-17T00:36:26.202547Z digest=sha256:d9be6d2a120152f32cccddbc6ce26375278893f3063596605d8badf69b25dc1c

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-13T18:27:25.541838Z digest=sha256:fb0cf545d43abbb84c3cfb2c602bead69eacc22538df5994ec33504ec4b023cb

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-12T04:14:04.151375Z digest=sha256:f2a92758af41bdd39524a08f87ac007b7307e8c884b7b824f76602912951a873

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-13T01:48:13.679862Z digest=sha256:8a300eb5f8ad1a73654e73db8192c99624097b26e7c063f2896edb53e6903107

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.

source=pdf_text observed=2026-07-31T19:04:33.760614Z digest=sha256:a8797ac84720c189aec813323529462a01bc9f082c3f3d6b7ea0982aeddebf7d