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Paper Citation Record · LEDGER

Few-Shot Vision-Language Action-Incremental Policy Learning

As of 18 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 1 inbound Pith citation observation for arXiv:2504.15517.

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

pith.paper-citation-record.v1
2504.15517 v1

Coverage vector

measured 76 of 76 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:28:38.346281Z

measured 77 of 77 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T14:07:10.387869Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T14:10:13.363551Z

Reference resolution

76 of 76 outbound references displayed

  • verified exact0
  • verified fuzzy36
  • unresolved40
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45b6e310-05e1-4f1c-af64-331aea30d7b9 · outbound

This paper cites VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models.

Few-Shot Vision-Language Action-Incremental Policy Learning VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Reference 1

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Observation b5b6eed3-e314-4976-b558-6988bd8ca556 · outbound

This paper cites Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model.

Few-Shot Vision-Language Action-Incremental Policy Learning Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model

Reference 2

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Observation 82c160ff-4226-4aaf-b5f2-421213d9c9de · outbound

This paper cites Manipllm: Embodied multimodal large language model for object-centric robotic manipulation,.

Few-Shot Vision-Language Action-Incremental Policy Learning Manipllm: Embodied multimodal large language model for object-centric robotic manipulation,

Reference 3

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Observation f8aefcc9-e586-47a3-b566-98f34a7f622d · outbound

This paper cites Hierarchical diffusion policy for kinematics-aware multi-task robotic manipulation,.

Few-Shot Vision-Language Action-Incremental Policy Learning Hierarchical diffusion policy for kinematics-aware multi-task robotic manipulation,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation dfee80a3-fba9-40f4-bafe-1299e518776f · outbound

This paper cites Dexterous grasp transformer,.

Few-Shot Vision-Language Action-Incremental Policy Learning Dexterous grasp transformer,

Reference 5

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ccaebaae-2638-4706-9d40-928be1b299b6 · outbound

This paper cites Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,.

Few-Shot Vision-Language Action-Incremental Policy Learning Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation cd4b7b48-bc2e-4767-bd25-6043bb7f1067 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

Few-Shot Vision-Language Action-Incremental Policy Learning PaLM-E: An Embodied Multimodal Language Model

Reference 7

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Observation 80ac79e0-dac4-4283-a2a2-b87bb4058a4c · outbound

This paper cites VIMA: General Robot Manipulation with Multimodal Prompts.

Few-Shot Vision-Language Action-Incremental Policy Learning VIMA: General Robot Manipulation with Multimodal Prompts

Reference 8

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Observation 71e493e2-e7ca-468e-b27b-5bfdc5f744ba · outbound

This paper cites Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning.

Few-Shot Vision-Language Action-Incremental Policy Learning Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning

Reference 9

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source=pdf_text observed=2026-08-16T11:28:36.982128Z digest=sha256:e12c4bfd34ea5b7fd6b1f5d2e8e3f2fd3d1b53fbc53e81aad66222b55032cce6

Observation 4905bc07-f014-48fd-a638-e09a907a01af · outbound

This paper cites Instruction-driven history-aware policies for robotic manipulations,.

Few-Shot Vision-Language Action-Incremental Policy Learning Instruction-driven history-aware policies for robotic manipulations,

Reference 10

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source=pdf_text observed=2026-08-16T11:28:36.986840Z digest=sha256:c227efcda87c2f9574d6a7d7a63499c53a018bcdfb8942321f56bc078ec177c3

Observation dfc89061-bdea-4928-b854-21bd5d539e74 · outbound

This paper cites Rvt: Robotic view transformer for 3d object manipulation,.

Few-Shot Vision-Language Action-Incremental Policy Learning Rvt: Robotic view transformer for 3d object manipulation,

Reference 11

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source=pdf_text observed=2026-08-16T11:28:36.992104Z digest=sha256:99c5a462d88032ce446959d62dd728170fe32ff50c71427ef357059e19f5546c

Observation ebc20fa3-01b1-49cb-acef-073d7ce5a279 · outbound

This paper cites RVT-2: Learning Precise Manipulation from Few Demonstrations.

Few-Shot Vision-Language Action-Incremental Policy Learning RVT-2: Learning Precise Manipulation from Few Demonstrations

Reference 12

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Observation 49f59e59-cb56-4d65-857c-dcb23c3a3442 · outbound

This paper cites SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation.

Few-Shot Vision-Language Action-Incremental Policy Learning SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation

Reference 13

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Observation aa2c7d9c-8dc1-47a9-bb79-9ed000ba10e8 · outbound

This paper cites Sugar: Pre-training 3d visual representations for robotics,.

Few-Shot Vision-Language Action-Incremental Policy Learning Sugar: Pre-training 3d visual representations for robotics,

Reference 14

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f15bb195-a663-423e-9321-2577ab31b135 · outbound

This paper cites Semantic prompt for few-shot image recognition,.

Few-Shot Vision-Language Action-Incremental Policy Learning Semantic prompt for few-shot image recognition,

Reference 15

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d9935f88-0f51-4870-9258-5ab164697838 · outbound

This paper cites Winclip: Zero-/few-shot anomaly classification and segmentation,.

Few-Shot Vision-Language Action-Incremental Policy Learning Winclip: Zero-/few-shot anomaly classification and segmentation,

Reference 16

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Observation ee1da1c6-780e-40b4-90f8-c039b1343204 · outbound

This paper cites Not all features matter: Enhancing few-shot clip with adaptive prior refinement,.

Few-Shot Vision-Language Action-Incremental Policy Learning Not all features matter: Enhancing few-shot clip with adaptive prior refinement,

Reference 17

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Observation 1231075f-ccf2-49e9-852e-cbfc1e5c2c52 · outbound

This paper cites Tip-adapter: Training-free adaption of clip for few-shot classification,.

Few-Shot Vision-Language Action-Incremental Policy Learning Tip-adapter: Training-free adaption of clip for few-shot classification,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation efb10b7e-25c9-4c56-9f01-49a54fdf069c · outbound

This paper cites Conditional prompt learning for vision-language models,.

Few-Shot Vision-Language Action-Incremental Policy Learning Conditional prompt learning for vision-language models,

Reference 19

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Observation 54d3a263-02fa-492b-9c57-96e11e718806 · outbound

This paper cites Smae: Few-shot learning for hdr deghosting with saturation-aware masked autoencoders,.

Few-Shot Vision-Language Action-Incremental Policy Learning Smae: Few-shot learning for hdr deghosting with saturation-aware masked autoencoders,

Reference 20

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Observation 4e897649-de53-4e54-8123-bfa25fb38308 · outbound

This paper cites Styleadv: Meta style adversarial training for cross-domain few-shot learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Styleadv: Meta style adversarial training for cross-domain few-shot learning,

Reference 21

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ec65f50c-6e7c-420d-94d6-163259f6ceff · outbound

This paper cites Rethinking few-shot medical segmentation: a vector quantization view,.

Few-Shot Vision-Language Action-Incremental Policy Learning Rethinking few-shot medical segmentation: a vector quantization view,

Reference 22

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Observation 51f64e91-8339-4a10-9e74-d1889ba09bf1 · outbound

This paper cites Hyperbolic insights with knowledge distillation for cross-domain few-shot learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Hyperbolic insights with knowledge distillation for cross-domain few-shot learning,

Reference 23

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Observation 3efc7876-2bb3-4cab-a6f3-416bc685b74c · outbound

This paper cites Cross-modal contrastive learning network for few-shot action recognition,.

Few-Shot Vision-Language Action-Incremental Policy Learning Cross-modal contrastive learning network for few-shot action recognition,

Reference 24

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:28:37.050923Z digest=sha256:9bad8814ffda298d4c714e59c439dcaae2f6c81ec5e82c55b897232f9499b78a

Observation d1a0e223-9f51-4bfb-b9b7-47278773ee63 · outbound

This paper cites Transductive few-shot learning with enhanced spectral-spatial embedding for hyperspectral image classification,.

Few-Shot Vision-Language Action-Incremental Policy Learning Transductive few-shot learning with enhanced spectral-spatial embedding for hyperspectral image classification,

Reference 25

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:28:37.102883Z digest=sha256:0b5dc760015056c10746357ea3a5aec9b1aa1b66a9c1c30c8b1401faccb4fc2e

Observation ca36be71-f1bf-4395-907d-6d7fc56c0147 · outbound

This paper cites Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning,

Reference 26

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 10e23fc2-4de2-457a-9727-5209e33edbea · outbound

This paper cites One-shot imitation learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning One-shot imitation learning,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:28:37.235860Z digest=sha256:531d6831f2c7290ec6c2dfcef624ef16e1cfb6b24100b08b576c073bc7de6689

Observation cecf90c9-b8ac-4834-9976-6201cacff462 · outbound

This paper cites One-shot visual imitation learning via meta-learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning One-shot visual imitation learning via meta-learning,

Reference 28

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source=pdf_text observed=2026-08-16T11:28:37.277696Z digest=sha256:91cae5c450ee9ada7cb7529eed8f18c6e12899bd01ef0170fe87b50ea599c760

Observation a4043ee3-640a-412d-8596-a64925d3f697 · outbound

This paper cites Task-embedded control networks for few-shot imitation learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Task-embedded control networks for few-shot imitation learning,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:28:37.334042Z digest=sha256:8e99e4b7abc43d225df0256881cb817467b95bf5a13aba7975b9d610564158dc

Observation f91d1f96-dab3-47b0-bbde-62bb9af8b1e5 · outbound

This paper cites One-Shot Imitation Learning with Invariance Matching for Robotic Manipulation.

Few-Shot Vision-Language Action-Incremental Policy Learning One-Shot Imitation Learning with Invariance Matching for Robotic Manipulation

Reference 30

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source=pdf_text observed=2026-08-16T11:28:37.339066Z digest=sha256:8d7b60af3635c799a97675c2c5319f7fb72832ceea2ce7f832fae6e590a5fc08

Observation 8904cda0-24ff-42b3-a2c4-f4320ba78da7 · outbound

This paper cites R3M: A Universal Visual Representation for Robot Manipulation.

Few-Shot Vision-Language Action-Incremental Policy Learning R3M: A Universal Visual Representation for Robot Manipulation

Reference 31

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source=pdf_text observed=2026-08-16T11:28:37.344262Z digest=sha256:41e24515c54cc7be5c5c18825d7698f16a3d0a296f952956ebf3c50fc7ac3613

Observation ae3dce56-2969-4f91-be76-a4bfdb9fbe35 · outbound

This paper cites Robocat: A self-improving generalist agent for robotic manipulation,.

Few-Shot Vision-Language Action-Incremental Policy Learning Robocat: A self-improving generalist agent for robotic manipulation,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:28:37.348717Z digest=sha256:45c4ab4fe697ea627c696a6d38d51d0e1ffa317683feaa04d21e44236d9b0e63

Observation 001322ab-2afc-494b-a596-ebfa36c0c6e3 · outbound

This paper cites Octo: An Open-Source Generalist Robot Policy.

Few-Shot Vision-Language Action-Incremental Policy Learning Octo: An Open-Source Generalist Robot Policy

Reference 33

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source=pdf_text observed=2026-08-16T11:28:37.352835Z digest=sha256:90dc776e026d0cdcf65bbb94b13fa4cbc35f28d89ca668bdc4f9482ee8a81be9

Observation 7f7f3444-bda7-497f-85fe-7a8dd9d77d45 · outbound

This paper cites OpenVLA: An Open-Source Vision-Language-Action Model.

Few-Shot Vision-Language Action-Incremental Policy Learning OpenVLA: An Open-Source Vision-Language-Action Model

Reference 34

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source=pdf_text observed=2026-08-16T11:28:37.357311Z digest=sha256:fc8be7e73b2d4b9bf904125231bb918d157b201ce918a608132be4fc3c29f2d6

Observation c546b114-a9bb-40d0-9d60-6ad344ce8277 · outbound

This paper cites Perceiver-actor: A multi-task transformer for robotic manipulation,.

Few-Shot Vision-Language Action-Incremental Policy Learning Perceiver-actor: A multi-task transformer for robotic manipulation,

Reference 35

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raw_fallback, observed 2026-08-16T11:28:39.448066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:28:37.362090Z digest=sha256:ee180bd8ee2cc9a0aadb6936b666ee262002a23761253a9e09a4afd94eeeb263

Observation f02303a9-ffa8-4fc6-af9b-6b57db3dcfba · outbound

This paper cites Gnfactor: Multi-task real robot learning with generalizable neural feature fields,.

Few-Shot Vision-Language Action-Incremental Policy Learning Gnfactor: Multi-task real robot learning with generalizable neural feature fields,

Reference 36

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source=pdf_text observed=2026-08-16T11:28:37.366156Z digest=sha256:a0917cc5fa3a269f0007cee2b1bab4d4df240bf36f708402fd201eab81260bc5

Observation 49e78541-aaaf-4bba-a75d-b9709ba01a32 · outbound

This paper cites Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need.

Few-Shot Vision-Language Action-Incremental Policy Learning Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need

Reference 37

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source=pdf_text observed=2026-08-16T11:28:37.370308Z digest=sha256:3c1027c46121113bd84250b3d66834d4bb502fef07d270be9ae2e02c577ce994

Observation d0c2a9a6-b0be-4101-886d-a6a2bcd8cc42 · outbound

This paper cites Foster: Feature boosting and compression for class-incremental learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Foster: Feature boosting and compression for class-incremental learning,

Reference 38

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source=pdf_text observed=2026-08-16T11:28:37.405691Z digest=sha256:278bf44c82fcf00144e97f28ef9638c5e0b40a30055c01bc14ae53476e1d57a8

Observation ab0e73df-20bb-433c-84af-df25cd0d0d45 · outbound

This paper cites Topology-preserving class-incremental learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Topology-preserving class-incremental learning,

Reference 39

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source=pdf_text observed=2026-08-16T11:28:37.479481Z digest=sha256:c4c993c98c7ecc5a389d4f4cb0690f5cce7e4fa3908fa15ebcaa6312dd00966a

Observation 11d41a8d-e730-4594-9616-b5041fd6b769 · outbound

This paper cites Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning.

Few-Shot Vision-Language Action-Incremental Policy Learning Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning

Reference 40

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Observation fea481b9-a149-4cc0-af9f-b7a5a8020094 · outbound

This paper cites Multimodal parameter-efficient few-shot class incremental learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Multimodal parameter-efficient few-shot class incremental learning,

Reference 41

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Observation 750d2ab2-51d9-4655-942b-3e8906215ca8 · outbound

This paper cites Few-shot class-incremental learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Few-shot class-incremental learning,

Reference 42

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source=pdf_text observed=2026-08-16T11:28:37.665361Z digest=sha256:152e52fe6a09e89a674825c84f4d934533d9e87fbdd7456ebc1500cfb2f0d685

Observation 60b5447e-6467-4714-b82c-1ca6f08adeed · outbound

This paper cites Few-shot incremental learning with continually evolved classifiers,.

Few-Shot Vision-Language Action-Incremental Policy Learning Few-shot incremental learning with continually evolved classifiers,

Reference 43

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source=pdf_text observed=2026-08-16T11:28:37.670494Z digest=sha256:b3cf9ef370b0b48a800911323dd6cfe9ab85dd76d174108bb106e199cd959058

Observation 249e56c2-03e1-4ed6-b81b-948961ee5f12 · outbound

This paper cites Forward compatible few-shot class-incremental learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Forward compatible few-shot class-incremental learning,

Reference 44

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source=pdf_text observed=2026-08-16T11:28:37.675419Z digest=sha256:56718512b0979f5ec4453f3a464cdf18a5c562879d1a8112d9b59ff4312a036b

Observation d5d591fd-1341-48e4-ad8b-0f047831a253 · outbound

This paper cites Gkeal: Gaussian kernel embedded analytic learning for few-shot class incremental task,.

Few-Shot Vision-Language Action-Incremental Policy Learning Gkeal: Gaussian kernel embedded analytic learning for few-shot class incremental task,

Reference 45

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source=pdf_text observed=2026-08-16T11:28:37.680179Z digest=sha256:dce092d506549a7c0941db760e02f45dd955fe6f9f46dc32f41b75d52fb7b496

Observation 8fc32642-5eee-49af-93e3-af15b0d2c475 · outbound

This paper cites Der: Dynamically expandable representation for class incremental learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Der: Dynamically expandable representation for class incremental learning,

Reference 46

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source=pdf_text observed=2026-08-16T11:28:37.686091Z digest=sha256:902c93608d15a3b56af10bdffeeaaec595a67a76bedefc188b745ff877dd11c5

Observation 886ed712-ea57-4c06-8fee-ac43b82897a4 · outbound

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

Few-Shot Vision-Language Action-Incremental Policy Learning Ntk-guided few-shot class incremental learning,

Reference 47

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source=pdf_text observed=2026-08-16T11:28:37.691213Z digest=sha256:112ccc595bd638e8b831726b22b3b88187ecd7ab825b0af89f9bf86b3a76b86a

Observation 1d80a1fa-80c9-4c07-84be-85088d55d8b2 · outbound

This paper cites Relationship-incremental scene graph generation by a divide-and-conquer pipeline with feature adapter,.

Few-Shot Vision-Language Action-Incremental Policy Learning Relationship-incremental scene graph generation by a divide-and-conquer pipeline with feature adapter,

Reference 48

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source=pdf_text observed=2026-08-16T11:28:37.696201Z digest=sha256:327568861813750d5e54ca6b72e7127d33cc55cde93cd6ec59a682802d911342

Observation 338e8860-34b1-4615-9eb6-083459e72876 · outbound

This paper cites Memorizing complementation network for few-shot class-incremental learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Memorizing complementation network for few-shot class-incremental learning,

Reference 49

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Observation f97badff-121e-4dae-ab9b-e9b7ee8a184f · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

Few-Shot Vision-Language Action-Incremental Policy Learning Overcoming catastrophic forgetting in neural networks,

Reference 50

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source=pdf_text observed=2026-08-16T11:28:37.771344Z digest=sha256:4dbb2ca501f2a30ca87db6c4dbde65423250f4fa199e9986057be548db59d58b

Observation 3fe86e29-5a1a-4330-b157-bd75edc964f3 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget,.

Few-Shot Vision-Language Action-Incremental Policy Learning Memory aware synapses: Learning what (not) to forget,

Reference 51

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source=pdf_text observed=2026-08-16T11:28:37.858025Z digest=sha256:a2ffc5e76a9a64395b78cfc8aa7fdde595f91af9882cccfb219036b8cd0fac83

Observation 51efef6d-4a45-4a28-bcae-b4cb63fb9d20 · outbound

This paper cites Continual learning through synaptic intelligence,.

Few-Shot Vision-Language Action-Incremental Policy Learning Continual learning through synaptic intelligence,

Reference 52

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source=pdf_text observed=2026-08-16T11:28:37.918593Z digest=sha256:54a6019acb9741f6dff596d4305d6660a8c48357a1999ae98026b200c3d733e7

Observation c25d4fca-fb2d-4247-8454-77822d8d99b3 · outbound

This paper cites icarl: Incremental classifier and representation learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning icarl: Incremental classifier and representation learning,

Reference 53

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Observation 69f6fc49-33ae-492d-89aa-8c83928fd9a2 · outbound

This paper cites Memory-efficient incremental learning through feature adaptation,.

Few-Shot Vision-Language Action-Incremental Policy Learning Memory-efficient incremental learning through feature adaptation,

Reference 54

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source=pdf_text observed=2026-08-16T11:28:37.965545Z digest=sha256:8013468098475694681b0eb1c8f06e1a521014a4be5d37f01c69a18672815d4b

Observation f4944077-6024-410a-952c-62edd7c7747d · outbound

This paper cites Incremental learning using conditional adversarial networks,.

Few-Shot Vision-Language Action-Incremental Policy Learning Incremental learning using conditional adversarial networks,

Reference 55

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source=pdf_text observed=2026-08-16T11:28:37.969649Z digest=sha256:2040017c425a2f5769d6d943b25aff0fcc30aed5486166100c045018bb208587

Observation 181d0b2b-763b-49d6-8f51-17abec2ce897 · outbound

This paper cites Adaptive memory replay for continual learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Adaptive memory replay for continual learning,

Reference 56

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source=pdf_text observed=2026-08-16T11:28:37.974331Z digest=sha256:af6c134b8519bafef5555f62c7909b1c9969aaaca001b5acc965cdf5293abe0a

Observation e82b44f8-054a-4752-b065-dc8e650f1379 · outbound

This paper cites Balanced destruction- reconstruction dynamics for memory-replay class incremental learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Balanced destruction- reconstruction dynamics for memory-replay class incremental learning,

Reference 57

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source=pdf_text observed=2026-08-16T11:28:37.978283Z digest=sha256:ee4e89fcada31b085bd944b427c8de9f4b06cfa8888a3ce2aab2181092b54e36

Observation c4973e73-3e72-4196-860a-50ebd111c063 · outbound

This paper cites Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning,

Reference 58

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source=pdf_text observed=2026-08-16T11:28:37.982989Z digest=sha256:e36fdfed946e003550d242d406ab961a289f5013a96d19b030e1b781ff32d58e

Observation 1d218712-2788-4626-958f-0b12ff82b75e · outbound

This paper cites Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub-optimality,.

Few-Shot Vision-Language Action-Incremental Policy Learning Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub-optimality,

Reference 59

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source=pdf_text observed=2026-08-16T11:28:37.988364Z digest=sha256:acbd9fde616838d3f285b2dadd781f47784aab78c19682f2b348af88ef5d77d7

Observation d4a6e616-d9dc-4f90-8f63-8043970d5620 · outbound

This paper cites Dualprompt: Complementary prompting for rehearsal-free continual learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Dualprompt: Complementary prompting for rehearsal-free continual learning,

Reference 60

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source=pdf_text observed=2026-08-16T11:28:37.993060Z digest=sha256:2fa22e4447780dc44f503469b7715286a5ea537eeb39b3accf8d432191559889

Observation 1b4be507-8415-4d3a-bd28-4f9fc41b0fef · outbound

This paper cites S-prompts learning with pre- trained transformers: An occam’s razor for domain incremental learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning S-prompts learning with pre- trained transformers: An occam’s razor for domain incremental learning,

Reference 61

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source=pdf_text observed=2026-08-16T11:28:37.997505Z digest=sha256:8b9b5e5fc2a6d4d1d719d47b8b6d56b0653ef3a3be9cc20df2706ef7ea5c0c19

Observation 3bed6a3b-e36d-4b8e-a623-80379d190f19 · outbound

This paper cites Few-shot continual active learning by a robot,.

Few-Shot Vision-Language Action-Incremental Policy Learning Few-shot continual active learning by a robot,

Reference 62

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source=pdf_text observed=2026-08-16T11:28:38.002073Z digest=sha256:fcd6a7016a404f85ad7513417ce47e1403ad74facebf444d058c2a852cce0d4a

Observation a84dcf91-e1a9-4670-8034-a00027e43e5b · outbound

This paper cites Vision-Language Navigation with Continual Learning.

Few-Shot Vision-Language Action-Incremental Policy Learning Vision-Language Navigation with Continual Learning

Reference 63

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source=pdf_text observed=2026-08-16T11:28:38.007310Z digest=sha256:eb37b2a372ff58e330496e4b45f96db0c7e9d4edffac67a997ad99614da45056

Observation eba34cc5-11c5-4337-b1b6-92ece43c9894 · outbound

This paper cites Continual vision-and-language navigation,.

Few-Shot Vision-Language Action-Incremental Policy Learning Continual vision-and-language navigation,

Reference 64

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source=pdf_text observed=2026-08-16T11:28:38.012814Z digest=sha256:a4ce1c858f54321e993c878d0b4f5613b96039665e56fadd8caad216676a472b

Observation 8ae993ac-88eb-435b-bfa6-c064e9fcb9bc · outbound

This paper cites Libero: Benchmarking knowledge transfer for lifelong robot learning,.

Few-Shot Vision-Language Action-Incremental Policy Learning Libero: Benchmarking knowledge transfer for lifelong robot learning,

Reference 65

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source=pdf_text observed=2026-08-16T11:28:38.017317Z digest=sha256:305f5cdfdbcad44d19ff6663286aefdb1ccbc2192a2ec147abca145a2344a21d

Observation 309c0454-3beb-453d-aed0-b98be63b4000 · outbound

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

Few-Shot Vision-Language Action-Incremental Policy Learning Lotus: Continual imitation learning for robot manipulation through unsupervised skill discovery,

Reference 66

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source=pdf_text observed=2026-08-16T11:28:38.083145Z digest=sha256:dd82745385c7a19fd3c5a45d20f2204a62da22939740541c2499bfd0fe7befe6

Observation 51a470d6-e9cb-4bfd-92ac-6e7550537f40 · outbound

This paper cites M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning.

Few-Shot Vision-Language Action-Incremental Policy Learning M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning

Reference 67

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source=pdf_text observed=2026-08-16T11:28:38.185263Z digest=sha256:02d1bae41a48b04ddb233afb6cece7ca6d691117842889b100ffb4bf6c46a1d8

Observation e93fac9f-ead8-4314-a21c-9bc8d6846927 · outbound

This paper cites TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models.

Few-Shot Vision-Language Action-Incremental Policy Learning TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models

Reference 68

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source=pdf_text observed=2026-08-16T11:28:38.191031Z digest=sha256:db7f46eb626a25f22d46e767da9e74336f40ce6e3758a8847e67351ef6c2cd19

Observation 65328927-efc3-4e14-9b4d-83c468585b8a · outbound

This paper cites Attention is all you need,.

Few-Shot Vision-Language Action-Incremental Policy Learning Attention is all you need,

Reference 69

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source=pdf_text observed=2026-08-16T11:28:38.195795Z digest=sha256:094e8a7edc3a700bc267e904bb0f22f5a8a0a8e325db2b5ad39f629af451c09f

Observation b6b915dd-4c65-42f3-a184-b20c7d16ca96 · outbound

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

Few-Shot Vision-Language Action-Incremental Policy Learning Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning

Reference 70

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source=pdf_text observed=2026-08-16T11:28:38.200461Z digest=sha256:1369bc49476afb70d8cf118f4f17783305ad2f1c5961f492812daff0175725fe

Observation 750d2d91-fed4-49a5-a8d3-97a3aa504f7e · outbound

This paper cites Vision-language foundation models as effective robot imitators,.

Few-Shot Vision-Language Action-Incremental Policy Learning Vision-language foundation models as effective robot imitators,

Reference 71

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source=pdf_text observed=2026-08-16T11:28:38.204743Z digest=sha256:acbb993d72caddb09f07d4ccbd746b7d0a0645ec0d6af523d5e2b5e85924b57f

Observation 8730354e-9aff-4c28-8731-7c872ac65aa6 · outbound

This paper cites Rlbench: The robot learning benchmark & learning environment,.

Few-Shot Vision-Language Action-Incremental Policy Learning Rlbench: The robot learning benchmark & learning environment,

Reference 72

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source=pdf_text observed=2026-08-16T11:28:38.209891Z digest=sha256:20c9048ac40273f4e7f0456a6d5b2d1d5502f322e5cb90710d50d6c96fac57c0

Observation 98e9e242-766d-49c0-87de-a47bd9bc4470 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Few-Shot Vision-Language Action-Incremental Policy Learning LoRA: Low-Rank Adaptation of Large Language Models

Reference 73

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:28:38.213648Z digest=sha256:73bdef86ddc0c92fccf3b80e31b3f36f9cfbe316a6d36b240b8fb16e7a369e5a

Observation 979c7fb0-3b49-4c5c-8fdf-379c67bb808e · outbound

This paper cites Segment anything,.

Few-Shot Vision-Language Action-Incremental Policy Learning Segment anything,

Reference 74

Resolution
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no resolver link, observed 2026-08-16T11:28:38.218572Z

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source=pdf_text observed=2026-08-16T11:28:38.218572Z digest=sha256:07a9ff005749c0048d52314a34e34ad218b87106f8f47efcd9fec4edda301bc4

Observation 49d0e2f2-4bb4-4ab6-a0cc-b16becb0d623 · outbound

This paper cites Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation.

Few-Shot Vision-Language Action-Incremental Policy Learning Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

Reference 75

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no resolver link, observed 2026-08-16T11:28:38.266401Z

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source=pdf_text observed=2026-08-16T11:28:38.266401Z digest=sha256:6fc7abe08f090da96ee5b10119157766612eda5b4d2198ab05cf857a2fc2f60d

Observation 4c53faf3-80af-48da-aed4-a19e3d868c0b · outbound

This paper cites Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware.

Few-Shot Vision-Language Action-Incremental Policy Learning Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Reference 76

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no resolver link, observed 2026-08-16T11:28:38.346281Z

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source=pdf_text observed=2026-08-16T11:28:38.346281Z digest=sha256:abf69d44e8c9b2cedf6614ef42909a9f8068ba44fe5ccd096908e15cf1811c12

Pith citing papers

Observation 5bc533c4-d2cf-4636-9034-f533ab98e694 · inbound

Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning cites this paper.

Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning Few-Shot Vision-Language Action-Incremental Policy Learning

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-21T14:10:13.365500Z

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