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Paper Citation Record · LEDGER

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration

As of 7 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2505.23865.

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

pith.paper-citation-record.v1
2505.23865 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:51:38.604930Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

11 of 11 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9d399527-a410-4626-b54a-d43eb98ccbfd · outbound

This paper cites Coordinated multi-robot exploration.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration Coordinated multi-robot exploration

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:40.995015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:37.116211Z digest=sha256:09aa89c3e365913eecab3d64a5cc5d04828e61661eb74ac41f6c03573189327e

Observation 6e6260a7-750d-455a-8499-fd3d0ab604f3 · outbound

This paper cites Carbone, D.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration Carbone, D

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:40.840465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:37.242807Z digest=sha256:fe24eec750ff675491de929df4d4ba8883c64e2f6bab51c9813085dbf769d9b5

Observation 93c972e7-f3ea-4035-889c-7b9099c8b1ea · outbound

This paper cites Houthooft et al.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration Houthooft et al

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:40.680369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:37.403841Z digest=sha256:2768f3ddda84f4a7466df57ac9275634a4192cc2ce48cde1b477ab3061496391

Observation 9909c974-4596-4712-9ccf-c4b708163f7e · outbound

This paper cites Mutual information-based distributed sensing and control for multi-agent systems.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration Mutual information-based distributed sensing and control for multi-agent systems

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:40.544994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:37.548616Z digest=sha256:06a455552e6546230ca632243daf00740f5c078a8b277c20579376589b13fbb4

Observation 039529ad-19fa-4bd6-a133-c8dcc9c13fca · outbound

This paper cites Liu et al.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration Liu et al

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:40.338261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:37.688299Z digest=sha256:757a9fd1c9fd83637c377d1de800c5ed6774d2cc3d199a226592e54bf9060122

Observation 1ad24486-11d4-4863-94f1-940caa9d59b2 · outbound

This paper cites In search of compositional multi-task deep architectures for infor- mation theoretic field exploration.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration In search of compositional multi-task deep architectures for infor- mation theoretic field exploration

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:40.178287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:37.841878Z digest=sha256:68e725f04de8f180a29a9a062dae1c414ac9157ac2bcf9f386eb664d4076ed87

Observation 3afae836-22c6-4ea6-9439-3cafd69d7160 · outbound

This paper cites Informative path planning for active field mapping under localization uncertainty.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration Informative path planning for active field mapping under localization uncertainty

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:39.992514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:37.997656Z digest=sha256:2689882047bd78686c41b43433fb3df7fa3149684e315fb45e370bc532d4708d

Observation 14149d43-3ccf-412d-9cc9-f60761d583df · outbound

This paper cites Singh et al.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration Singh et al

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:39.787590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:38.138340Z digest=sha256:7b9e7aacd3d158e79822226926c508e99b07449a290df42c1c7d3277199ba814

Observation 08c01ba2-fb4d-48ca-896c-0f583f9f9de0 · outbound

This paper cites Sukhija et al.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration Sukhija et al

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:39.539537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:38.304766Z digest=sha256:6e5677838c66f445998248e1132d61a09a3ed91f1f38979bc547b95fc9942f2e

Observation 7478303c-0301-4827-b5e5-bc6434418ecd · outbound

This paper cites Thrun, W.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration Thrun, W

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:39.239292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:38.473384Z digest=sha256:6dfe3edaea6673536f5e11d2a62d94a75037376900174fea74619cd534372f7b

Observation 9328b9cb-2d4a-4359-93f5-38ac9731b9a7 · outbound

This paper cites A review on vision-based path planning and navigation for agricultural robots.

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration A review on vision-based path planning and navigation for agricultural robots

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:51:38.953330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:51:38.604930Z digest=sha256:b7c14ad198d8ff22264ecbf1b118e06d98c2e3460b29b9c6b6459e31be24dd29

Pith citing papers

No inbound Pith citation observations are available.