Paper Citation Record · LEDGER

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents

As of 12 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2412.11484.

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

pith.paper-citation-record.v1
2412.11484 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:57:26.898242Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:36:24.941205Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:11:27.315136Z

Reference resolution

58 of 58 outbound references displayed

verified exact5
verified fuzzy36
unresolved17
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d1b8345c-42ef-4834-90b2-343d4f2668fc · outbound

This paper cites Decoupling representation learning from reinforcement learning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Decoupling representation learning from reinforcement learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.982350Z

Source-reported events for the cited work

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Observation 82bad470-9775-43f9-a846-b8c4c3c6a685 · outbound

This paper cites CURL: Contrastive unsupervised repre- sentations for reinforcement learning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents CURL: Contrastive unsupervised repre- sentations for reinforcement learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.965570Z

Source-reported events for the cited work

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Observation 221b2ec4-cd73-4e73-b74b-6bf2952ae033 · outbound

This paper cites Data-Efficient Reinforcement Learning with Self-Predictive Representations.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Data-Efficient Reinforcement Learning with Self-Predictive Representations

Reference 3

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Observation 851ccd24-391d-4dc9-9419-baeefc422f53 · outbound

This paper cites Unsupervised Domain Adaptation for Visual Navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Unsupervised Domain Adaptation for Visual Navigation

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:57:27.342997Z

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Observation a20924dc-322d-421f-9741-c24f4ef11812 · outbound

This paper cites Zero experience required: Plug & play modular transfer learning for semantic visual navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Zero experience required: Plug & play modular transfer learning for semantic visual navigation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.947678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1c8345a3-decb-40c9-a32b-6b0ffee81ba3 · outbound

This paper cites Zero-shot object goal visual navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Zero-shot object goal visual navigation

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:57:27.317338Z

Source-reported events for the cited work

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Observation 89c59dc3-0102-4606-8039-ed5281014ee0 · outbound

This paper cites Simple but Effective: CLIP embeddings for embodied AI.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Simple but Effective: CLIP embeddings for embodied AI

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.930969Z

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Observation a2a3bab7-6a5a-4a04-a328-1b0db7ca8529 · outbound

This paper cites ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings

Reference 8

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Observation 3e10368a-630f-47ef-b5ea-756ea3814e72 · outbound

This paper cites AI2-THOR: An Interactive 3D Environment for Visual AI.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents AI2-THOR: An Interactive 3D Environment for Visual AI

Reference 9

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Observation c0596996-e568-4af1-a788-c03bd0210599 · outbound

This paper cites CARLA: An open urban driving simulator.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents CARLA: An open urban driving simulator

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.913383Z

Source-reported events for the cited work

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Observation c308c192-2ee2-43c5-9347-40a3a81d2724 · outbound

This paper cites Sutton and Andrew G.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Sutton and Andrew G

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.896482Z

Source-reported events for the cited work

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Observation ae23b938-d931-4543-9461-5fdf36ae921d · outbound

This paper cites Learning to drive by watching youtube videos: Action-conditioned contrastive policy pretraining.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Learning to drive by watching youtube videos: Action-conditioned contrastive policy pretraining

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.879499Z

Source-reported events for the cited work

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Observation 8e738909-0f3a-4d07-9dae-8bed0a032b41 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Representation Learning with Contrastive Predictive Coding

Reference 13

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Observation 2ee38267-01be-4f37-ae1c-eb37aad9d9dc · outbound

This paper cites Meta-World: A benchmark and evaluation for multi-task and meta reinforce- ment learning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Meta-World: A benchmark and evaluation for multi-task and meta reinforce- ment learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.862701Z

Source-reported events for the cited work

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Observation 4f05cbac-d90a-4cf7-87cf-6ed542ca2939 · outbound

This paper cites Domain adaptation in reinforcement learning via latent unified state representation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Domain adaptation in reinforcement learning via latent unified state representation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.847134Z

Source-reported events for the cited work

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Observation 54113369-4747-4fb4-b825-c6c284bc53c1 · outbound

This paper cites Visual prompt tuning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Visual prompt tuning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.831497Z

Source-reported events for the cited work

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Observation 8a6eddef-5e07-4c23-840b-65809e103ac0 · outbound

This paper cites Learning to prompt for vision-language models.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Learning to prompt for vision-language models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.814812Z

Source-reported events for the cited work

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Observation c2af2a51-bbdb-4eff-be96-7a87395905a4 · outbound

This paper cites Integrating contrastive learning with dynamic models for reinforcement learning from images.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Integrating contrastive learning with dynamic models for reinforcement learning from images

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.798379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8b72e577-0cd7-4a29-80a3-da0b57fa741a · outbound

This paper cites Action-driven contrastive representation for reinforcement learning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Action-driven contrastive representation for reinforcement learning

Reference 19

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Source-reported events for the cited work

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Observation 29c254c3-0156-42ec-bfb7-879e45f74741 · outbound

This paper cites Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

Reference 20

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Observation 6977d20e-b8fc-42bb-bb48-9153795bdd15 · outbound

This paper cites STACoRe: Spatio-temporal and action-based contrastive representations for reinforcement learning in Atari.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents STACoRe: Spatio-temporal and action-based contrastive representations for reinforcement learning in Atari

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.765368Z

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Observation a0ba1fe7-7537-4f8b-a7ed-c7c638533ccd · outbound

This paper cites Proximal Policy Optimization Algorithms.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Proximal Policy Optimization Algorithms

Reference 22

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Source-reported events for the cited work

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Observation c130b8f3-df1e-47bd-8367-9b1bcf7d5730 · outbound

This paper cites A reduction of imitation learning and structured prediction to no-regret online learning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents A reduction of imitation learning and structured prediction to no-regret online learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.748490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4a62f344-7986-400e-9581-a30290473804 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.731120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21453528-529c-4fbe-840b-5e13b3cf2430 · outbound

This paper cites Effective Approaches to Attention-based Neural Machine Translation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Effective Approaches to Attention-based Neural Machine Translation

Reference 25

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Source-reported events for the cited work

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Observation e5e5659e-527b-411c-86dc-851fb9d06129 · outbound

This paper cites Attention is all you need.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Attention is all you need

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.713990Z

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Observation 91ead4e6-124b-4219-a0c7-945f659a8247 · outbound

This paper cites Image Augmentation Is All You Need: Regu- larizing deep reinforcement learning from pixels.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Image Augmentation Is All You Need: Regu- larizing deep reinforcement learning from pixels

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.696440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3f021913-0690-4b51-a9b4-ffe5578a0d47 · outbound

This paper cites S4RL: Surprisingly Simple Self- Supervision for Offline Reinforcement Learning in Robotics.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents S4RL: Surprisingly Simple Self- Supervision for Offline Reinforcement Learning in Robotics

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.678455Z

Source-reported events for the cited work

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Observation 584acc29-d1c6-45e5-89e5-25a813213abc · outbound

This paper cites Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?

Reference 29

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Observation e5af7360-7e9b-4bd4-a17b-771c00aeb2ac · outbound

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

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents R3M: A Universal Visual Representation for Robot Manipulation

Reference 30

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Observation 6416f018-b2f6-4c73-b405-aaf116fc0184 · outbound

This paper cites Open-World Object Manipulation using Pre-trained Vision-Language Models.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Open-World Object Manipulation using Pre-trained Vision-Language Models

Reference 31

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Observation 3b97c071-599a-438b-b526-4d4a9bd8a2fa · outbound

This paper cites Transferring Foundation Models for Generalizable Robotic Manipulation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Transferring Foundation Models for Generalizable Robotic Manipulation

Reference 32

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Observation c0bc8d8d-3626-47e2-b781-8cc4cd039777 · outbound

This paper cites Gnm: A general navigation model to drive any robot.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Gnm: A general navigation model to drive any robot

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.661409Z

Source-reported events for the cited work

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Observation 96c5793c-7d2f-40bf-b632-8cd21a081b6c · outbound

This paper cites ExAug: Robot-conditioned navigation policies via geometric experi- ence augmentation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents ExAug: Robot-conditioned navigation policies via geometric experi- ence augmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.644588Z

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Observation c6de943a-33be-4357-8d80-b7c81a6076b0 · outbound

This paper cites Speaker-follower models for vision-and-language navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Speaker-follower models for vision-and-language navigation

Reference 35

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Observation 97062384-a622-48c3-a142-d69ec246116f · outbound

This paper cites Take the Scenic Route: Improving Generalization in Vision-and-Language Navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Take the Scenic Route: Improving Generalization in Vision-and-Language Navigation

Reference 36

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Observation 66131cd1-8cc4-4341-bfe1-8b37d3585f6e · outbound

This paper cites DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following

Reference 37

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Observation 8a7fb5a2-9d83-469b-b0ae-e733e358588a · outbound

This paper cites Vision-language navigation with random environmental mixup.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Vision-language navigation with random environmental mixup

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.610037Z

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Observation 26868ec3-0eef-4a0d-8da9-79f1f2bf6d00 · outbound

This paper cites EnvEdit: Environment Editing for Vision-and-Language Navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents EnvEdit: Environment Editing for Vision-and-Language Navigation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.592641Z

Source-reported events for the cited work

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Observation 7e2246d8-7739-4d97-ba98-6c698aba04a3 · outbound

This paper cites Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.575679Z

Source-reported events for the cited work

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Observation a12bdf73-ff22-475e-b95a-ca18ac4c472d · outbound

This paper cites Explore and explain: self-supervised navigation and recounting.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Explore and explain: self-supervised navigation and recounting

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.558578Z

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Observation e537c069-d8b7-42c8-9a8b-c5031bf89454 · outbound

This paper cites MoDA: Map style transfer for self-supervised Domain Adaptation of embodied agents.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents MoDA: Map style transfer for self-supervised Domain Adaptation of embodied agents

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.541364Z

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Observation d867d00b-c456-4ffd-8930-a36d3539fefb · outbound

This paper cites CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation

Reference 43

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Observation 254aed53-2066-49d8-af68-15e762b2df0c · outbound

This paper cites CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation

Reference 44

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Source-reported events for the cited work

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Observation 681fcd5c-6fbb-4aa1-83ed-39ca4377099e · outbound

This paper cites Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.522564Z

Source-reported events for the cited work

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Observation 003eba48-21d6-4d13-a7c6-eb1b0e7afdf3 · outbound

This paper cites DARLA: Improving zero-shot transfer in reinforcement learning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents DARLA: Improving zero-shot transfer in reinforcement learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.504530Z

Source-reported events for the cited work

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Observation 19f27a73-ed13-48b0-ada4-998d62d24407 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Exploring Visual Prompts for Adapting Large-Scale Models

Reference 47

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Source-reported events for the cited work

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Observation b77b3769-67d3-42e7-a2b7-af794c2287e3 · outbound

This paper cites MaPLe: Multi-modal prompt learning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents MaPLe: Multi-modal prompt learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.488202Z

Source-reported events for the cited work

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Observation 1d8a6df2-3b78-4d5b-b138-bac6621499c6 · outbound

This paper cites Unified Vision and Language Prompt Learning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Unified Vision and Language Prompt Learning

Reference 49

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Source-reported events for the cited work

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Observation 86fa39de-8cf0-423e-b958-e5ef5067cf41 · outbound

This paper cites Algorithm AS 136: A k-means clustering algorithm.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Algorithm AS 136: A k-means clustering algorithm

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.470627Z

Source-reported events for the cited work

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Observation e66aae98-4a8b-4394-9543-ad80fb23e654 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents A simple framework for contrastive learning of visual representations

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.450551Z

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Observation 68242b82-b8c3-4cd1-8ad4-40c8dc101103 · outbound

This paper cites Generalization in reinforcement learning by soft data augmentation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Generalization in reinforcement learning by soft data augmentation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.433907Z

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Observation 634b6505-e232-4319-a659-bbbb3d61c498 · outbound

This paper cites Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:57:26.993544Z

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Observation a4054840-dd1f-4a69-82c3-2baade7de7c5 · outbound

This paper cites Robustnav: Towards benchmarking robustness in embodied navigation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Robustnav: Towards benchmarking robustness in embodied navigation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.416196Z

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Observation 0424fe5b-730e-44cc-b99d-e0c645e418e0 · outbound

This paper cites Efficient adaptation for end-to-end vision-based robotic manipulation.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Efficient adaptation for end-to-end vision-based robotic manipulation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.398452Z

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Observation 663a073c-281e-4abc-a3cb-f3fb164f001e · outbound

This paper cites On Evaluation of Embodied Navigation Agents.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents On Evaluation of Embodied Navigation Agents

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:26.887528Z

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Observation 63ef4b26-21d4-45d6-ab52-d4128028be21 · outbound

This paper cites Attempt: Parameter-efficient multi-task tuning via attentional mixtures of soft prompts.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Attempt: Parameter-efficient multi-task tuning via attentional mixtures of soft prompts

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:27.380451Z

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Observation 656a7914-1c50-457b-8269-f90873996117 · outbound

This paper cites Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:57:26.948522Z

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Pith citing papers

Observation 47c5a0bc-2d88-4f40-9254-f6b1b78b8142 · inbound

Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation cites this paper.

Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:11:27.320347Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.