Paper Citation Record · LEDGER

Harnessing Input-Adaptive Inference for Efficient VLN

As of 8 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 1 inbound Pith citation observation for arXiv:2508.09262.

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

pith.paper-citation-record.v1
2508.09262 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:16:27.443463Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T14:50:58.269817Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T14:51:08.148643Z

Reference resolution

68 of 68 outbound references displayed

verified exact1
verified fuzzy54
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b511e49f-8a6a-4dc0-85f7-db97b60daa5d · outbound

This paper cites Vision-and-language navigation: In- terpreting visually-grounded navigation instructions in real environments.

Harnessing Input-Adaptive Inference for Efficient VLN Vision-and-language navigation: In- terpreting visually-grounded navigation instructions in real environments

Reference 1

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Observation e8a12b91-793e-4038-bf2a-e8c95e5920e1 · outbound

This paper cites Sim- to-real transfer for vision-and-language navigation.

Harnessing Input-Adaptive Inference for Efficient VLN Sim- to-real transfer for vision-and-language navigation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:38.413025Z

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Observation c9907dc5-ebdf-409d-a231-d1435efc7e03 · outbound

This paper cites Near-optimal hashing algo- rithms for approximate nearest neighbor in high dimensions.

Harnessing Input-Adaptive Inference for Efficient VLN Near-optimal hashing algo- rithms for approximate nearest neighbor in high dimensions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:38.195462Z

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Observation a99c0956-64b6-4214-9140-ade9c8338cbe · outbound

This paper cites Post train- ing 4-bit quantization of convolutional networks for rapid- deployment.

Harnessing Input-Adaptive Inference for Efficient VLN Post train- ing 4-bit quantization of convolutional networks for rapid- deployment

Reference 4

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Observation d3be4173-c310-4bb3-879d-fd256e853baf · outbound

This paper cites Surf: Speeded up robust features.

Harnessing Input-Adaptive Inference for Efficient VLN Surf: Speeded up robust features

Reference 5

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Observation 0f3d5646-8387-44d2-9346-5954df171c09 · outbound

This paper cites Lsq+: Improving low-bit quantization through learnable offsets and better initialization.

Harnessing Input-Adaptive Inference for Efficient VLN Lsq+: Improving low-bit quantization through learnable offsets and better initialization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:37.622777Z

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Observation ea44c453-daeb-430e-b8b5-3e09e1462cd4 · outbound

This paper cites Matterport3d: Learning from rgb-d data in indoor environments.

Harnessing Input-Adaptive Inference for Efficient VLN Matterport3d: Learning from rgb-d data in indoor environments

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:37.397170Z

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Observation 7e9a7242-1966-4e87-a187-3fc61aeb124c · outbound

This paper cites Similarity estimation techniques from rounding algorithms.

Harnessing Input-Adaptive Inference for Efficient VLN Similarity estimation techniques from rounding algorithms

Reference 8

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Observation ffdb1142-0cee-4ae5-adf5-5e0554e649a3 · outbound

This paper cites Robustnav: Towards benchmark- ing robustness in embodied navigation.

Harnessing Input-Adaptive Inference for Efficient VLN Robustnav: Towards benchmark- ing robustness in embodied navigation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:36.860896Z

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Observation 75b984ba-07c1-4239-bcda-ddb7979c8298 · outbound

This paper cites History aware multimodal transformer for vision- and-language navigation.

Harnessing Input-Adaptive Inference for Efficient VLN History aware multimodal transformer for vision- and-language navigation

Reference 10

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Observation 9c8e2339-e081-4280-9b26-834e1dd10beb · outbound

This paper cites Think global, act lo- cal: Dual-scale graph transformer for vision-and-language navigation.

Harnessing Input-Adaptive Inference for Efficient VLN Think global, act lo- cal: Dual-scale graph transformer for vision-and-language navigation

Reference 11

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Observation 48441fbd-4f96-444d-b89b-6f42e2e53dda · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

Harnessing Input-Adaptive Inference for Efficient VLN PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 12

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Observation cba321f5-bdf9-43dc-b5de-699e1433adcb · outbound

This paper cites Low-bit quantization of neural networks for efficient infer- ence.

Harnessing Input-Adaptive Inference for Efficient VLN Low-bit quantization of neural networks for efficient infer- ence

Reference 13

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Observation e5610eb6-8336-41fd-840f-fcbd1b775724 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Harnessing Input-Adaptive Inference for Efficient VLN Sinkhorn distances: Lightspeed computation of optimal transport

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:36.186895Z

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Observation a96da192-03e0-40eb-b9a9-24fc0183cadb · outbound

This paper cites BERT: Pre-training of deep bidirectional trans- formers for language understanding.

Harnessing Input-Adaptive Inference for Efficient VLN BERT: Pre-training of deep bidirectional trans- formers for language understanding

Reference 15

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Observation 3958f03d-ef1e-4c83-9970-d31e07f3dc44 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Harnessing Input-Adaptive Inference for Efficient VLN An image is worth 16x16 words: Transformers for image recognition at scale

Reference 16

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Observation 1beebdf1-25db-475a-9bc5-eb1303dad0e5 · outbound

This paper cites Reducing Transformer Depth on Demand with Structured Dropout.

Harnessing Input-Adaptive Inference for Efficient VLN Reducing Transformer Depth on Demand with Structured Dropout

Reference 17

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Observation d8312716-778e-4db8-9cb1-ad5c64220db3 · outbound

This paper cites Depgraph: Towards any structural pruning.

Harnessing Input-Adaptive Inference for Efficient VLN Depgraph: Towards any structural pruning

Reference 18

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Observation fe304698-9c18-4731-853e-00812b98f8bb · outbound

This paper cites Spatially adaptive computation time for residual networks.

Harnessing Input-Adaptive Inference for Efficient VLN Spatially adaptive computation time for residual networks

Reference 19

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Observation 7f36c452-49b2-4373-b403-f578e6b96620 · outbound

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

Harnessing Input-Adaptive Inference for Efficient VLN Speaker-follower models for vision-and-language navigation

Reference 20

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Observation e4d143db-f0ea-46e6-ba18-ad793050eac7 · outbound

This paper cites Airbert: In-domain pretraining for vision-and-language navigation.

Harnessing Input-Adaptive Inference for Efficient VLN Airbert: In-domain pretraining for vision-and-language navigation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:34.569487Z

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Observation bcfb70cd-cce1-4071-bd82-c7983e108c20 · outbound

This paper cites Improving robust- ness of vision transformers by reducing sensitivity to patch corruptions.

Harnessing Input-Adaptive Inference for Efficient VLN Improving robust- ness of vision transformers by reducing sensitivity to patch corruptions

Reference 22

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Observation 59cd03c8-1cca-4158-846c-742f0177a02e · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Harnessing Input-Adaptive Inference for Efficient VLN Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 23

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Observation 677d8c61-6377-4c78-8501-11cec1ad2f0c · outbound

This paper cites Learning both weights and connections for efficient neural network.

Harnessing Input-Adaptive Inference for Efficient VLN Learning both weights and connections for efficient neural network

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:34.190249Z

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Observation 4f99c410-06f1-437c-9874-4ddd69aae620 · outbound

This paper cites Towards learning a generic agent for vision- and-language navigation via pre-training.

Harnessing Input-Adaptive Inference for Efficient VLN Towards learning a generic agent for vision- and-language navigation via pre-training

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:33.814372Z

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Observation 936ac15a-e28f-4a9e-818c-1566426df53c · outbound

This paper cites Deep residual learning for image recognition.

Harnessing Input-Adaptive Inference for Efficient VLN Deep residual learning for image recognition

Reference 26

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Observation 62537790-f269-4f02-b3f9-3b7aebf0eb1d · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

Harnessing Input-Adaptive Inference for Efficient VLN Benchmarking neural network robustness to common corruptions and perturbations

Reference 27

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Observation e0d58cd7-15d9-483e-9092-105bc6a6cd2c · outbound

This paper cites Revisiting pruning at ini- tialization through the lens of ramanujan graph.

Harnessing Input-Adaptive Inference for Efficient VLN Revisiting pruning at ini- tialization through the lens of ramanujan graph

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:33.147918Z

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Observation dc8b5c34-199b-4498-bedb-fb81d4f4157e · outbound

This paper cites A recurrent vision-and-language bert for navigation.

Harnessing Input-Adaptive Inference for Efficient VLN A recurrent vision-and-language bert for navigation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:32.994494Z

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Observation 221165ec-fe3f-48f5-8d4b-8f719dafafb5 · outbound

This paper cites Bridg- ing the gap between learning in discrete and continuous envi- ronments for vision-and-language navigation.

Harnessing Input-Adaptive Inference for Efficient VLN Bridg- ing the gap between learning in discrete and continuous envi- ronments for vision-and-language navigation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:32.821399Z

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Observation 5521bd9e-00cf-4f1a-89cb-4382661c850f · outbound

This paper cites Multi-scale dense networks for resource efficient image classification.

Harnessing Input-Adaptive Inference for Efficient VLN Multi-scale dense networks for resource efficient image classification

Reference 31

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Observation 23d54271-c30b-4d3a-865f-9d7d365e4dc7 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

Harnessing Input-Adaptive Inference for Efficient VLN Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 32

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Observation 857ce5db-368b-44d2-b2e7-83289a60d172 · outbound

This paper cites A new path: Scaling vision-and-language navigation with synthetic instructions and imitation learning,.

Harnessing Input-Adaptive Inference for Efficient VLN A new path: Scaling vision-and-language navigation with synthetic instructions and imitation learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:32.402035Z

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Observation 49019d57-8ca9-464a-a812-f29eda0114d4 · outbound

This paper cites Shallow-deep networks: Understanding and mitigating net- work overthinking.

Harnessing Input-Adaptive Inference for Efficient VLN Shallow-deep networks: Understanding and mitigating net- work overthinking

Reference 34

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Observation aaa6b125-039f-4d0a-97e2-337702ce222b · outbound

This paper cites Sim-2-sim transfer for vision- and-language navigation in continuous environments.

Harnessing Input-Adaptive Inference for Efficient VLN Sim-2-sim transfer for vision- and-language navigation in continuous environments

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:32.006393Z

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Observation 856735f6-84ef-46e0-a2a5-82241a408bf9 · outbound

This paper cites Beyond the nav-graph: Vision and language navigation in continuous environments.

Harnessing Input-Adaptive Inference for Efficient VLN Beyond the nav-graph: Vision and language navigation in continuous environments

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:31.866878Z

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Observation ce4562bb-de13-424a-8a4f-35afba907193 · outbound

This paper cites Improving vision-and-language navigation by generating future-view image semantics.

Harnessing Input-Adaptive Inference for Efficient VLN Improving vision-and-language navigation by generating future-view image semantics

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:31.687460Z

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Observation 56815087-af10-4b37-a0e9-5da3aef93916 · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

Harnessing Input-Adaptive Inference for Efficient VLN BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 38

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Observation 12c67c62-fd60-4b59-b644-14fb097f9a3d · outbound

This paper cites FastBERT: a Self-distilling BERT with Adaptive Inference Time.

Harnessing Input-Adaptive Inference for Efficient VLN FastBERT: a Self-distilling BERT with Adaptive Inference Time

Reference 39

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Observation ee9dc887-5bf2-48a9-9959-b63426518154 · outbound

This paper cites Relaxed Quantization for Discretized Neural Networks.

Harnessing Input-Adaptive Inference for Efficient VLN Relaxed Quantization for Discretized Neural Networks

Reference 40

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This paper cites Distinctive image features from scale- invariant keypoints.

Harnessing Input-Adaptive Inference for Efficient VLN Distinctive image features from scale- invariant keypoints

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:31.516288Z

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Observation 89e7fdf7-3075-485d-9bd5-b0f4909225dc · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

Harnessing Input-Adaptive Inference for Efficient VLN Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 42

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Observation 6e762497-5571-46aa-8e41-9b8d4f8603ee · outbound

This paper cites Soat: A scene-and object-aware transformer for vision-and-language navigation.

Harnessing Input-Adaptive Inference for Efficient VLN Soat: A scene-and object-aware transformer for vision-and-language navigation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:31.358741Z

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Observation ee3932fc-c7b6-41bc-98d3-69b8faadde68 · outbound

This paper cites Up or down? adap- tive rounding for post-training quantization.

Harnessing Input-Adaptive Inference for Efficient VLN Up or down? adap- tive rounding for post-training quantization

Reference 44

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Observation 10e07c3a-563a-4dc2-a8ad-df76a93e3465 · outbound

This paper cites Gradient- free structured pruning with unlabeled data.

Harnessing Input-Adaptive Inference for Efficient VLN Gradient- free structured pruning with unlabeled data

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:31.182875Z

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Observation 6805fda0-d190-4980-8c4b-e74c82baa80d · outbound

This paper cites Do vi- sual imaginations improve vision-and-language navigation agents? In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 3846–3855, 2025.

Harnessing Input-Adaptive Inference for Efficient VLN Do vi- sual imaginations improve vision-and-language navigation agents? In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 3846–3855, 2025

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:31.028732Z

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Observation fc0c3a03-1271-45e6-8cab-65f71a67a71b · outbound

This paper cites Reverie: Remote embodied visual referring expression in real indoor environments.

Harnessing Input-Adaptive Inference for Efficient VLN Reverie: Remote embodied visual referring expression in real indoor environments

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:30.909191Z

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Observation 3e44a65d-fbc5-4150-9d81-cbf69d181fe6 · outbound

This paper cites Orb: An efficient alternative to sift or surf.

Harnessing Input-Adaptive Inference for Efficient VLN Orb: An efficient alternative to sift or surf

Reference 48

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Observation bc81ae3a-7b86-4100-b169-4ac9626d267f · outbound

This paper cites Habitat: A platform for embodied ai research.

Harnessing Input-Adaptive Inference for Efficient VLN Habitat: A platform for embodied ai research

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:30.777281Z

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Observation 9aca160b-6137-472f-9e4f-90bc007bd49a · outbound

This paper cites P4Q: Learning to Prompt for Quantization in Visual-language Models.

Harnessing Input-Adaptive Inference for Efficient VLN P4Q: Learning to Prompt for Quantization in Visual-language Models

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:16:27.643236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6c392954-0e24-4427-b0d4-fd4379c04705 · outbound

This paper cites an unresolved cited work.

Harnessing Input-Adaptive Inference for Efficient VLN Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-05T21:16:30.622602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e6585886-2e71-48ab-a08c-e429b6522992 · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks.

Harnessing Input-Adaptive Inference for Efficient VLN Branchynet: Fast inference via early exiting from deep neural networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:30.419219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 15b46aa2-1246-4fd4-9cfd-e73b6af9a43a · outbound

This paper cites Vision-and-dialog navigation.

Harnessing Input-Adaptive Inference for Efficient VLN Vision-and-dialog navigation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:30.241668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d9856914-0fc4-41e5-9e00-f83f46834d5d · outbound

This paper cites Mixed Precision DNNs: All you need is a good parametrization.

Harnessing Input-Adaptive Inference for Efficient VLN Mixed Precision DNNs: All you need is a good parametrization

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T21:16:26.312095Z

Source-reported events for the cited work

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Observation f383c027-162a-4247-b886-e8043189ed58 · outbound

This paper cites EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning.

Harnessing Input-Adaptive Inference for Efficient VLN EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T21:16:26.354820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6824a69f-a019-4722-857d-ea04a0903f87 · outbound

This paper cites Skipnet: Learning dynamic routing in convolu- tional networks.

Harnessing Input-Adaptive Inference for Efficient VLN Skipnet: Learning dynamic routing in convolu- tional networks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:29.969622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3295c586-93ef-4b21-bb09-52c502e15d7d · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Harnessing Input-Adaptive Inference for Efficient VLN Image quality assessment: from error visibility to structural similarity

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:29.757113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a5176f39-20a9-40b2-9d9b-f92a3fa76101 · outbound

This paper cites Scaling data generation in vision-and-language navigation.

Harnessing Input-Adaptive Inference for Efficient VLN Scaling data generation in vision-and-language navigation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:29.559449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ed2e4ac4-c37a-47cd-907e-c99340ad57cd · outbound

This paper cites Wang et al.

Harnessing Input-Adaptive Inference for Efficient VLN Wang et al

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:29.374156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 468cb6f1-8414-4714-9ecf-35df5e35247b · outbound

This paper cites Wasserman et al.

Harnessing Input-Adaptive Inference for Efficient VLN Wasserman et al

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:29.140019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ae3fcb53-9d36-4050-ba7a-e5247cbe5855 · outbound

This paper cites DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference.

Harnessing Input-Adaptive Inference for Efficient VLN DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-05T21:16:26.875019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ba175dd-72bc-4977-bbc4-5c6447629910 · outbound

This paper cites Deer-vla: Dynamic inference of multimodal large language models for efficient robot execution.

Harnessing Input-Adaptive Inference for Efficient VLN Deer-vla: Dynamic inference of multimodal large language models for efficient robot execution

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:28.973165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 53ca755a-2518-49c0-a196-71e20bd166c0 · outbound

This paper cites Fsim: A feature similarity index for image quality assessment.IEEE transactions on Image Processing, 20(8):2378–2386, 2011.

Harnessing Input-Adaptive Inference for Efficient VLN Fsim: A feature similarity index for image quality assessment.IEEE transactions on Image Processing, 20(8):2378–2386, 2011

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:28.763472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 17b60309-bd93-4e2a-bddb-2c9371f208b8 · outbound

This paper cites Zhang et al.

Harnessing Input-Adaptive Inference for Efficient VLN Zhang et al

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:28.572046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2d96d2e8-4b69-4c40-bf82-7684789e341f · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Harnessing Input-Adaptive Inference for Efficient VLN The unreasonable effectiveness of deep features as a perceptual metric

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:28.428640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9f9d8f8f-ae3c-4c86-b550-dd3d1bddd16b · outbound

This paper cites Soon: Scenario oriented object nav- igation with graph-based exploration.

Harnessing Input-Adaptive Inference for Efficient VLN Soon: Scenario oriented object nav- igation with graph-based exploration

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:28.251212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 192547e1-ffc2-4875-bd68-0c5f044612bb · outbound

This paper cites Learning unforeseen robustness from out-of-distribution data using equivariant domain translator.

Harnessing Input-Adaptive Inference for Efficient VLN Learning unforeseen robustness from out-of-distribution data using equivariant domain translator

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:28.059247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a02ee394-4508-4238-aaf4-526719577bb5 · outbound

This paper cites Zhu et al.

Harnessing Input-Adaptive Inference for Efficient VLN Zhu et al

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:16:27.898133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation d5f0dd66-a0bf-424d-abca-bb237d1a6dd0 · inbound

VLN-Cache: Enabling Token Caching for VLN Models with Visual/Semantic Dynamics Awareness cites this paper.

VLN-Cache: Enabling Token Caching for VLN Models with Visual/Semantic Dynamics Awareness Harnessing Input-Adaptive Inference for Efficient VLN

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:51:08.151302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.