Paper Citation Record · LEDGER

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks

As of 16 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2505.06378.

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

pith.paper-citation-record.v1
2505.06378 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:50:34.309518Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:53:20.563856Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy28
unresolved12
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8bd5002d-6a41-4976-aec1-963a96238ca8 · outbound

This paper cites Multiply: A multisensory object-centric embodied large language model in 3D world,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Multiply: A multisensory object-centric embodied large language model in 3D world,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.971165Z

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Observation 209f36d3-ee0b-4cf6-82c2-8138426d526c · outbound

This paper cites Embodied AI with large language models: A survey and new hri framework,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Embodied AI with large language models: A survey and new hri framework,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.952792Z

Source-reported events for the cited work

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Observation 1207cc8a-eba4-4b98-a898-cbed80ed1cbd · outbound

This paper cites Language models meet world models: Embodied experiences enhance language models,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Language models meet world models: Embodied experiences enhance language models,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.930301Z

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Observation 38816c56-d77a-4b3b-80ea-b32dc89f6c9a · outbound

This paper cites Llm-planner: Few-shot grounded planning for embodied agents with large language models,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Llm-planner: Few-shot grounded planning for embodied agents with large language models,

Reference 4

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Observation 81f8aa1d-dfc0-4fc4-81ef-a69de76eab5b · outbound

This paper cites Artificial intelligence based object detection and traffic prediction by autonomous vehicles–a review,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Artificial intelligence based object detection and traffic prediction by autonomous vehicles–a review,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.899291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d101cfc2-bbde-435f-8df7-292945f06156 · outbound

This paper cites Vehicle-road-cloud collaborative perception framework and key technologies: A review,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Vehicle-road-cloud collaborative perception framework and key technologies: A review,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.882565Z

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Observation 77c41048-080a-4d5a-b051-1ae510fbf7d7 · outbound

This paper cites Embodied intelligence-based perception, decision-making, and control for autonomous operations of rail transportation,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Embodied intelligence-based perception, decision-making, and control for autonomous operations of rail transportation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.865781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 96be5786-3fb8-4933-a406-dfe58fc0b1a3 · outbound

This paper cites Generative diffusion-based contract design for efficient AI twin migration in vehicular embodied AI networks,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Generative diffusion-based contract design for efficient AI twin migration in vehicular embodied AI networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.849947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 79602bce-921f-4424-ba0e-fce8204480f8 · outbound

This paper cites Blockchain-assisted twin migration for vehicular metaverses: A game theory approach,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Blockchain-assisted twin migration for vehicular metaverses: A game theory approach,

Reference 9

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Source-reported events for the cited work

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Observation 3c1becd2-f87f-403b-bb32-b04dab8c5952 · outbound

This paper cites Q-IoT: QoS-aware multilayer service architecture for multiclass IoT data traffic management,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Q-IoT: QoS-aware multilayer service architecture for multiclass IoT data traffic management,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.821204Z

Source-reported events for the cited work

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Observation 6bea10ee-6dc0-472c-99c5-1f16cf4c5b55 · outbound

This paper cites When metaverses meet vehicle road cooperation: Multi-agent DRL-based stackelberg game for vehicular twins migration,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks When metaverses meet vehicle road cooperation: Multi-agent DRL-based stackelberg game for vehicular twins migration,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.803276Z

Source-reported events for the cited work

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Observation d1bdb784-da06-406c-9264-eb5091028442 · outbound

This paper cites Learning-based incentive mechanism for task freshness-aware vehic- ular twin migration,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Learning-based incentive mechanism for task freshness-aware vehic- ular twin migration,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.786685Z

Source-reported events for the cited work

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Observation 830be7a6-9fcd-4a62-907f-fcc92f8d6298 · outbound

This paper cites Deep reinforcement learning based multi-attribute auction model for resource allocation in vehicular aigc services,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Deep reinforcement learning based multi-attribute auction model for resource allocation in vehicular aigc services,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.768996Z

Source-reported events for the cited work

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Observation 4d898c8c-5afe-4829-a068-5b4d65c17ecd · outbound

This paper cites Blockchain-empowered resource allocation in haps- assisted iov digital twin networks: A federated drl approach,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Blockchain-empowered resource allocation in haps- assisted iov digital twin networks: A federated drl approach,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.753318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0140c227-ccd1-4b01-a280-08c775f47a8d · outbound

This paper cites Bidirectional LSTM-CRF Models for Sequence Tagging.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Bidirectional LSTM-CRF Models for Sequence Tagging

Reference 15

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Observation ce5dbf40-3591-455e-a7bf-b59ac8d90598 · outbound

This paper cites Finding lottery tickets in vision models via data-driven spectral foresight pruning,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Finding lottery tickets in vision models via data-driven spectral foresight pruning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.737481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d146dc18-69a9-408c-882a-28d645eae82c · outbound

This paper cites Conversational voice agents are preferred and lead to better driving performance in con- ditionally automated vehicles,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Conversational voice agents are preferred and lead to better driving performance in con- ditionally automated vehicles,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.721678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5e6a6a80-8655-41a2-a379-0140d95d0720 · outbound

This paper cites Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:34.204776Z

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Observation ead15adb-4933-436f-ace6-ef8c47f382c5 · outbound

This paper cites Embodied understanding of driving scenarios,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Embodied understanding of driving scenarios,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.703130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 575f17df-e289-4518-a337-4780f57977d7 · outbound

This paper cites Contextvlm: Zero-shot and few-shot context understanding for autonomous driving using vision language models,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Contextvlm: Zero-shot and few-shot context understanding for autonomous driving using vision language models,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:34.214533Z

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Observation f203a791-023a-488f-9fd4-ed6c6bf0b208 · outbound

This paper cites A compre- hensive survey of few-shot learning: Evolution, applications, challenges, and opportunities,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks A compre- hensive survey of few-shot learning: Evolution, applications, challenges, and opportunities,

Reference 21

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Observation b6d19971-c1d0-4a84-9ba9-9be9703b0115 · outbound

This paper cites A fine-grained self-adapting prompt learning approach for few-shot learning with pre-trained language models,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks A fine-grained self-adapting prompt learning approach for few-shot learning with pre-trained language models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.661503Z

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Observation 3e64304f-029e-4668-8923-3796976f2ddb · outbound

This paper cites Primal: Profit maximization avatar placement for mobile edge computing,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Primal: Profit maximization avatar placement for mobile edge computing,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.643226Z

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Observation fa74671a-faed-4399-b57a-bc71f6fae24e · outbound

This paper cites Multiagent deep reinforcement learning for dynamic avatar migration in AIoT-enabled vehicular metaverses with trajectory prediction,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Multiagent deep reinforcement learning for dynamic avatar migration in AIoT-enabled vehicular metaverses with trajectory prediction,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.625471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0a1954d3-7e6c-4f87-b0da-b7ba4404e658 · outbound

This paper cites Stack- elberg game-based multi-agent algorithm for resource allocation and task offloading in mec-enabled c-its,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Stack- elberg game-based multi-agent algorithm for resource allocation and task offloading in mec-enabled c-its,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.606757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 27e0185d-3390-42cf-bd58-017d318f4909 · outbound

This paper cites Tiny multi-agent DRL for twins migration in UA V metaverses: A multi-leader multi-follower stackelberg game approach,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Tiny multi-agent DRL for twins migration in UA V metaverses: A multi-leader multi-follower stackelberg game approach,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.590538Z

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Observation 0dfb80c5-cde2-4ed4-8fce-5813dd1035ca · outbound

This paper cites Pops: Policy pruning and shrinking for deep reinforcement learning,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Pops: Policy pruning and shrinking for deep reinforcement learning,

Reference 27

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Observation 399550a7-a057-49d5-8a79-add1dfc2e489 · outbound

This paper cites Qlp: Deep q-learning for pruning deep neural networks,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Qlp: Deep q-learning for pruning deep neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.561992Z

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Observation 6516e4ea-c8fd-4027-bcc8-f12ef81b72bc · outbound

This paper cites Knowledge distillation based cooperative reinforcement learning for connectivity preservation in UA V networks,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Knowledge distillation based cooperative reinforcement learning for connectivity preservation in UA V networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.545633Z

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Observation 05ab0b4c-5f74-47c9-a36f-e9deca5366fe · outbound

This paper cites Five facets of 6G: Research challenges and opportunities,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Five facets of 6G: Research challenges and opportunities,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.529112Z

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Observation 1309b924-a006-4490-bf9f-d214fb4ae9b6 · outbound

This paper cites An overview of otfs for internet of things: Concepts, benefits, and challenges,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks An overview of otfs for internet of things: Concepts, benefits, and challenges,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.510428Z

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Observation a9654ca4-d4bb-4583-a8f0-603986e5b8ce · outbound

This paper cites Data-aided channel estimation for otfs systems with a superimposed pilot and data transmission scheme,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Data-aided channel estimation for otfs systems with a superimposed pilot and data transmission scheme,

Reference 32

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Observation 762890f1-205c-4785-9b55-b1403b93700f · outbound

This paper cites A mathematical theory of communication,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks A mathematical theory of communication,

Reference 33

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Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:50:34.465897Z

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Observation 0e2d9cad-8014-479e-8f61-46db8d73f6ac · outbound

This paper cites Joint user association and resource pricing for metaverse: Distributed and centralized approaches,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Joint user association and resource pricing for metaverse: Distributed and centralized approaches,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.450441Z

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Observation 20f6ac83-88bc-43a8-8115-1c7a7b074658 · outbound

This paper cites Stackelberg- game-based computation offloading method in cloud–edge computing networks,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Stackelberg- game-based computation offloading method in cloud–edge computing networks,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:34.290023Z

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Observation 5323512c-8a7c-4134-b572-a8a192b0d4cd · outbound

This paper cites Privacy- preserving incentive mechanism for multi-leader multi-follower IoT- edge computing market: A reinforcement learning approach,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Privacy- preserving incentive mechanism for multi-leader multi-follower IoT- edge computing market: A reinforcement learning approach,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.422833Z

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Observation 64c798be-75ec-4036-94c5-eb424e78cd34 · outbound

This paper cites Ntk-guided few-shot class incremental learning,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Ntk-guided few-shot class incremental learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:50:34.404006Z

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Observation 9701a019-cf48-4bec-9897-c3f8b496db21 · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks The surprising effectiveness of ppo in cooperative multi-agent games,

Reference 39

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This paper cites Asynchronous methods for deep rein- forcement learning,.

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks Asynchronous methods for deep rein- forcement learning,

Reference 40

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Pith citing papers

Observation 49801851-17f5-4551-99c5-f45b12d2af0d · inbound

Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach cites this paper.

Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks

Reference 42

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