Paper Citation Record · LEDGER

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 3 inbound Pith citation observations for arXiv:2502.05943.

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

pith.paper-citation-record.v1
2502.05943 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:22:21.894785Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:02:53.985148Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:59:55.699873Z

Reference resolution

35 of 35 outbound references displayed

verified exact1
verified fuzzy22
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f300f75-ce42-4637-a48a-b2167c1e0245 · outbound

This paper cites Technology de- velopments and impacts of connected and autonomous vehicles: An overview,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Technology de- velopments and impacts of connected and autonomous vehicles: An overview,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.441078Z

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 d7a21cfd-ad2b-410d-b488-b4453fa3207b · outbound

This paper cites An efficient self-evolution method of autonomous driving for any given algorithm,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network An efficient self-evolution method of autonomous driving for any given algorithm,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.425068Z

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 69a61ddf-d3cd-45f0-ab4d-bc87e93a64c2 · outbound

This paper cites Learning to drive by imitation: An overview of deep behavior cloning methods,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Learning to drive by imitation: An overview of deep behavior cloning methods,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.408988Z

Source-reported events for the cited work

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Observation e0fbdc78-f0fd-4fb0-a588-6574796e1400 · outbound

This paper cites Dense reinforcement learning for safety validation of autonomous vehicles,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Dense reinforcement learning for safety validation of autonomous vehicles,

Reference 4

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Observation bded6917-93de-4822-9681-3c9c7b3cf0b4 · outbound

This paper cites Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T17:22:21.753311Z

Source-reported events for the cited work

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Observation f304cfa9-15ec-4a18-a806-6a3525594e7b · outbound

This paper cites Solving the problem of dynamic adaptability of artificial intelligence systems that control dynamic technical objects,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Solving the problem of dynamic adaptability of artificial intelligence systems that control dynamic technical objects,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.382795Z

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 b55ef108-a982-4f5b-815c-c7ecc46ab955 · outbound

This paper cites Imitation learning for agile autonomous driving,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Imitation learning for agile autonomous driving,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.366842Z

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 a758c5ee-bae5-44ac-8137-fda4a3398b26 · outbound

This paper cites High-level decision making for automated highway driving via behavior cloning,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network High-level decision making for automated highway driving via behavior cloning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.351282Z

Source-reported events for the cited work

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Observation 6802ca13-23c7-4663-9279-fbf929b696d2 · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network End-to-end autonomous driving: Challenges and frontiers,

Reference 9

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Source-reported events for the cited work

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Observation ae788f7d-25d5-41e8-9018-d7106d3af042 · outbound

This paper cites Driver behavioral cloning for route following in autonomous vehicles using task knowledge distillation,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Driver behavioral cloning for route following in autonomous vehicles using task knowledge distillation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.324810Z

Source-reported events for the cited work

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Observation c95ce571-3ff9-4a49-aacf-5951679b953b · outbound

This paper cites Dynamically conservative self-driving planner for long-tail cases,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Dynamically conservative self-driving planner for long-tail cases,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.308015Z

Source-reported events for the cited work

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Observation e1faa849-02c1-4b76-b711-695ffa48e027 · outbound

This paper cites Long-tailed distribution adaptation,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Long-tailed distribution adaptation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.290707Z

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 e9e2e846-258a-47cb-ae02-0de0f566ed85 · outbound

This paper cites A compre- hensive study on self-learning methods and implications to autonomous driving,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network A compre- hensive study on self-learning methods and implications to autonomous driving,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.274863Z

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 88e71421-2105-4f90-aba7-86a2a684a33d · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Deep reinforcement learning for autonomous driving: A survey,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T17:22:21.796906Z

Source-reported events for the cited work

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Observation 59c08dff-0517-40c2-b467-f08567b6d086 · outbound

This paper cites A survey on recent advancements in autonomous driving using deep reinforcement learning: Applications, challenges, and solutions,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network A survey on recent advancements in autonomous driving using deep reinforcement learning: Applications, challenges, and solutions,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.248401Z

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 60b25cb1-6bda-43a8-9b3b-e08c98b22a58 · outbound

This paper cites Effectiveness of transfer learning in au- tonomous driving using model car,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Effectiveness of transfer learning in au- tonomous driving using model car,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.233907Z

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 5fd3395d-372a-4a53-b4ea-c9ccf61fc578 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Overcoming catastrophic forgetting in neural networks,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T17:22:21.811243Z

Source-reported events for the cited work

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Observation f34d6579-b64a-4359-84d4-e96200a332c2 · outbound

This paper cites Meta learning Framework for Automated Driving.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Meta learning Framework for Automated Driving

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-08T17:22:21.986712Z

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 f266fced-65c4-42bd-89f9-e32346dcc2b3 · outbound

This paper cites A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T17:22:21.820820Z

Source-reported events for the cited work

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Observation f273eaea-1859-4141-a137-cfa063d0241e · outbound

This paper cites Knowledge distillation-based edge- decision hierarchies for interactive behavior-aware planning in au- tonomous driving system,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Knowledge distillation-based edge- decision hierarchies for interactive behavior-aware planning in au- tonomous driving system,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.198753Z

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 89b79d04-1675-4849-b5d4-e901f9474089 · outbound

This paper cites End-to-end urban driving by imitating a reinforcement learning coach,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network End-to-end urban driving by imitating a reinforcement learning coach,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.184461Z

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 e02faf63-7414-4b37-bb20-19ef9c9040df · outbound

This paper cites Lotus: Continual imitation learning for robot manipulation through unsupervised skill discovery,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Lotus: Continual imitation learning for robot manipulation through unsupervised skill discovery,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.168635Z

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 6ab74386-96d7-493c-9ca3-8c85cedfeeae · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 23

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Source-reported events for the cited work

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Observation 601d9866-9993-4d64-a292-80b0bc3e99a3 · outbound

This paper cites Theory on Mixture-of-Experts in Continual Learning.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Theory on Mixture-of-Experts in Continual Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T17:22:21.844545Z

Source-reported events for the cited work

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Observation 94210517-f069-47a5-9727-53d29242fe0a · outbound

This paper cites Towards understanding the mixture-of-experts layer in deep learning,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Towards understanding the mixture-of-experts layer in deep learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.140552Z

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 712a71d4-36b4-44fb-b96a-23a03555cc89 · outbound

This paper cites Progressive neural architecture search,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Progressive neural architecture search,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.124104Z

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 20b27c3f-f47b-4a27-8f4c-cc24d02e124f · outbound

This paper cites The evolution of mixture of experts: A survey from basics to breakthroughs,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network The evolution of mixture of experts: A survey from basics to breakthroughs,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.107999Z

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 dcf8cb34-e266-410c-bfc5-a6cf1c78334f · outbound

This paper cites Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T17:22:21.863425Z

Source-reported events for the cited work

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Observation cfd6bad1-1ed7-4c3f-99a2-85ecd2d65e0b · outbound

This paper cites Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T17:22:21.867900Z

Source-reported events for the cited work

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Observation 9cb7b18c-691e-4c51-9461-3ff38c474241 · outbound

This paper cites Same state, different task: Continual reinforcement learning without interference,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Same state, different task: Continual reinforcement learning without interference,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.072566Z

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 7fd35549-e2b2-4efb-95e2-6eda24177982 · outbound

This paper cites Scenario adaptation: An approach to customizing computer-based training games and simulations,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Scenario adaptation: An approach to customizing computer-based training games and simulations,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.055515Z

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 8a8d3db5-0dca-4def-bb05-565668cd5d3f · outbound

This paper cites Critical test cases generalization for autonomous driving object detection algorithms,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Critical test cases generalization for autonomous driving object detection algorithms,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.038780Z

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 f1ff14a3-4644-456c-8bf3-7dfdc5026ddd · outbound

This paper cites Generalizing Motion Planners with Mixture of Experts for Autonomous Driving.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Generalizing Motion Planners with Mixture of Experts for Autonomous Driving

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T17:22:21.885877Z

Source-reported events for the cited work

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Observation d7b1c86b-eb8f-432d-aa22-c97acd32991d · outbound

This paper cites Task-failure-driven rebalancing of disas- sembly lines,.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Task-failure-driven rebalancing of disas- sembly lines,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:22:22.020709Z

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 e4be560d-a7b4-4fd9-b8b6-48c3b01cd7df · outbound

This paper cites Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning.

Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-08T17:22:21.894785Z

Source-reported events for the cited work

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Pith citing papers

Observation 01ca70c2-8d5d-400c-8768-4e9791b2371c · inbound

Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects cites this paper.

Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T14:02:53.985148Z

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Observation c543517a-49d1-4931-9921-4d60c94cb158 · inbound

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving cites this paper.

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:48.465599Z

Source-reported events for the cited work

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Observation d7a10a39-b572-453a-9427-209fa17fc469 · inbound

GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving cites this paper.

GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:59:55.781637Z

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.