Paper Citation Record · LEDGER

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency

As of 6 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2604.07286.

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

pith.paper-citation-record.v1
2604.07286 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T17:54:04.337239Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact3
verified fuzzy19
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e708998-9684-4ac0-9a75-5d5bf73cc0be · outbound

This paper cites What is the state of neural network pruning?.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency What is the state of neural network pruning?

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.751997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c7ddb3a6-6ef1-485a-8ea3-27f77892fe1d · outbound

This paper cites Distilling the Knowledge in a Neural Network.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Distilling the Knowledge in a Neural Network

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:55:56.380393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c8042ffd-e06c-45af-bf1a-b6a15b4c3929 · outbound

This paper cites QONNX: Representing Arbitrary-Precision Quantized Neural Networks.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency QONNX: Representing Arbitrary-Precision Quantized Neural Networks

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:55:56.391567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 44e38800-9041-4d6f-903d-b0a08f6748f1 · outbound

This paper cites Squeezenext: Hardware-aware neural network design.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Squeezenext: Hardware-aware neural network design

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.754934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8918f046-8061-4470-9b64-4c877e989ec5 · outbound

This paper cites Videoedge: Processing camera streams using hierarchical clusters.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Videoedge: Processing camera streams using hierarchical clusters

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.748832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9a2b5b11-4a18-462f-b21a-10bc4b7e5c54 · outbound

This paper cites An overview of adaptive dynamic deep neural networks via slimmable and gated ar- chitectures.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency An overview of adaptive dynamic deep neural networks via slimmable and gated ar- chitectures

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.713444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 786f5e6d-bce9-4466-8587-f52ba8368dc8 · outbound

This paper cites Single-image real-time rain removal based on depth-guided non-local features.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Single-image real-time rain removal based on depth-guided non-local features

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.709434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 89cd29a5-b54f-4327-ac0f-1b97a9124e06 · outbound

This paper cites Navislim: Adaptive context-aware navigation and sensing via dynamic slimmable networks.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Navislim: Adaptive context-aware navigation and sensing via dynamic slimmable networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.720933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a2fc3e1d-fb32-4bad-8478-56a67e882ffe · outbound

This paper cites Airsim: High-fidelity visual and physical simulation for autonomous vehicles.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Airsim: High-fidelity visual and physical simulation for autonomous vehicles

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.739249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 84c995a4-76c7-4b3a-8250-4730d07349b2 · outbound

This paper cites Representation learning for event-based visuomotor policies.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Representation learning for event-based visuomotor policies

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.717231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9c4c6a7b-66f5-4765-a5c4-548f25598ca3 · outbound

This paper cites Split computing and early exiting for deep learning applications: Survey and research challenges.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Split computing and early exiting for deep learning applications: Survey and research challenges

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.728779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 49ba37f6-e467-4426-ac32-1233bb365756 · outbound

This paper cites Slimmable Neural Networks.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Slimmable Neural Networks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:55:56.399467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8a43ac18-b00e-4c33-94ca-b63fba69bd1e · outbound

This paper cites Hydrafu- sion: Context-aware selective sensor fusion for robust and efficient autonomous vehicle perception.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Hydrafu- sion: Context-aware selective sensor fusion for robust and efficient autonomous vehicle perception

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.735809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0cea474b-352e-44f6-82b7-88a76d1dedf8 · outbound

This paper cites Testudo: Col- laborative intelligence for latency-critical autonomous systems.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Testudo: Col- laborative intelligence for latency-critical autonomous systems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.745392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3bc12a04-d7b7-404c-9b65-9784adc3fe64 · outbound

This paper cites Dynamic slimmable denoising network.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Dynamic slimmable denoising network

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.701517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 795774ec-d5f4-4169-b514-3f6bd6874510 · outbound

This paper cites Repmono: a lightweight self-supervised monocular depth estimation architecture for high-speed inference.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Repmono: a lightweight self-supervised monocular depth estimation architecture for high-speed inference

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.691234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1c4eef94-4fb3-4d00-b758-952e16fbad71 · outbound

This paper cites Improving accuracy and efficiency of monocular depth estimation in power grid environments using point cloud optimization and knowledge distillation.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Improving accuracy and efficiency of monocular depth estimation in power grid environments using point cloud optimization and knowledge distillation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.705113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1dbb82dc-501c-436f-9383-b89bd948484a · outbound

This paper cites Navisplit: Dynamic multi-branch split dnns for efficient distributed autonomous navigation.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Navisplit: Dynamic multi-branch split dnns for efficient distributed autonomous navigation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.742363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e4e1b278-59c4-47d1-8e40-fd4903655ddd · outbound

This paper cites Energy-quality scalable monocular depth estimation on low-power cpus.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Energy-quality scalable monocular depth estimation on low-power cpus

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.693729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 155e7b3e-3e20-43e0-aac1-989fc52b6b29 · outbound

This paper cites Human-level control through deep reinforcement learning.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Human-level control through deep reinforcement learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.697116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 04db3107-b917-4abf-b5da-f05f2f21c389 · outbound

This paper cites Deep reinforcement learning with double q-learning.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency Deep reinforcement learning with double q-learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.732062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8a388ba8-f03e-441f-be36-70649e2c66a5 · outbound

This paper cites A formal basis for the heuristic determination of minimum cost paths.

CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency A formal basis for the heuristic determination of minimum cost paths

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:21:40.725026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.