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Paper Citation Record · LEDGER

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps

As of 21 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2412.12024.

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

pith.paper-citation-record.v1
2412.12024 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:26:11.806504Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

45 of 45 outbound references displayed

  • verified exact1
  • verified fuzzy32
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a34893a-972e-49ed-9d87-7a6e1f8c3e81 · outbound

This paper cites On Evaluation of Embodied Navigation Agents.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps On Evaluation of Embodied Navigation Agents

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T14:26:11.515243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:26:11.515243Z digest=sha256:88e04ef7c6f602b9e0f5010db1a0c63c4cc58c0c48190fda268b1f9ae3928cd3

Observation 37f40904-8f92-4925-8201-2cc3589eff1f · outbound

This paper cites Hindsight experience replay.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Hindsight experience replay

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.848578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.523321Z digest=sha256:013cee9a9f90643407e93ec3dd9338da8d32c798aaaff80f058ceaea80b2a9a0

Observation 1399f2d9-bef7-421a-baa4-ef66db2627b1 · outbound

This paper cites an unresolved cited work.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:26:12.828509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.530623Z digest=sha256:882305dfdd221f3c893b1a3700125554208e45e8d0a3b2b68b4d96232322a754

Observation 2840e049-4d4c-434d-bfc4-c02632ce58bf · outbound

This paper cites DeepMind Lab.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps DeepMind Lab

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T14:26:11.538307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:26:11.538307Z digest=sha256:5993d6b1dd3e5f397ce89392b003c34bbce57879acb9b915d459bfe075fbb71b

Observation b2ede029-9672-41d0-99cd-f3a6f1f08605 · outbound

This paper cites Teaching a machine to read maps with deep reinforcement learning.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Teaching a machine to read maps with deep reinforcement learning

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.808836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.547152Z digest=sha256:2c3e570505979dba9507e9849bb9173f5ffd43fe248b972b02be1bf0da3da315

Observation 438ea220-4b1d-43d6-abd7-351a51c1d2d2 · outbound

This paper cites Learning to explore using active neural SLAM.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Learning to explore using active neural SLAM

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.789664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.553981Z digest=sha256:7bd14e407f7120c9d7ae75ec947aeb7659b77e021ea2f44ba39d0202b4ebcd87

Observation 3503bce1-4b4b-42c4-8341-7ed848cd21c7 · outbound

This paper cites Learning exploration policies for navigation.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Learning exploration policies for navigation

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.771219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.563242Z digest=sha256:5add1a19cfe838c72932c1d2f56bb49462dc50f839cbe45de5f71197c3ba846b

Observation 69226ac9-c22d-4758-8e3f-e2dddc795145 · outbound

This paper cites Deep reinforcement learning in a handful of trials using probabilistic dynamics models.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Deep reinforcement learning in a handful of trials using probabilistic dynamics models

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.749995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.570940Z digest=sha256:5d029568032bd8ecc594419e038849f86f196484f68beaa50ee1e618b0181af8

Observation 34f9c502-bbcd-4873-b498-27e743171498 · outbound

This paper cites Learning to act by predicting the future.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Learning to act by predicting the future

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.722815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.577347Z digest=sha256:b528caee814e7d1195b45c37eb20d9d41e7b467ec5e079dbf6aedff0be5c2d0a

Observation bf48cf93-f12a-4ff1-bc5b-cdad54c70a98 · outbound

This paper cites Leibo, and Charles Blundell.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Leibo, and Charles Blundell

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.704156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.583087Z digest=sha256:2b67509e4419eb18f3394d433601f6b16a4f67d63067b2f7768b93a2cc08a028

Observation 87061269-af07-475b-b1a4-abe6bcac7bdd · outbound

This paper cites Cognitive mapping and planning for visual navigation.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Cognitive mapping and planning for visual navigation

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.686417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.589351Z digest=sha256:b8d6c0af30d8b2d5a3c76bc18fe1d6429f7150e4fda3f41231c5abdd1e25b5ab

Observation ff58a015-c0be-4ffb-b8d6-7491667e408a · outbound

This paper cites Hyper N etworks.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Hyper N etworks

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.664407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.593920Z digest=sha256:50ace942c8f9ab3ab230c23a10e0e4600ad5f4807b4de7962d095a811a5acd5a

Observation 3b298aaa-6af3-4925-94df-5937f298dbe2 · outbound

This paper cites Learning latent dynamics for planning from pixels.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Learning latent dynamics for planning from pixels

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.637385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.600697Z digest=sha256:65df5db9a90b188202d08d9bae77f871ad3bfd14a841a9ce1a52d51ace04d8fe

Observation 6e0aabce-0bfa-4908-9b67-727c265b9a17 · outbound

This paper cites Distributed prioritized experience replay.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Distributed prioritized experience replay

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T14:26:11.607247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:26:11.607247Z digest=sha256:83b7f5feaa12c214f29a964eff3d9986cf97ae41ca800214b3253f17ce04c8d2

Observation 43e0d6f1-5ce1-4091-bd24-2e980e98e28f · outbound

This paper cites Equivariant single view pose prediction via induced and restriction representations.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Equivariant single view pose prediction via induced and restriction representations

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.601173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.612175Z digest=sha256:45c8be702b03ce6010ad520f9b8d35177aa99e6dc3074e866a895e49e136ce45

Observation d127af4b-3037-4681-ba80-b34c06fceff5 · outbound

This paper cites Continual model-based reinforcement learning with hypernetworks.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Continual model-based reinforcement learning with hypernetworks

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.584081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.617261Z digest=sha256:22b7130d6ff21f3b11427bdf27d52f030bfe754d4edeba12edd1d59f46875d5b

Observation 32eaeb63-09e5-4a9f-8083-ee0b67f1d731 · outbound

This paper cites Reinforcement learning with unsupervised auxiliary tasks.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Reinforcement learning with unsupervised auxiliary tasks

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.564145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.621788Z digest=sha256:b7c8f0e5eab32829d3f8b2963418719b2a3693c1e2553390be55f72c8b9c2bdd

Observation ccf61a8d-6852-422f-8722-db5b755246d9 · outbound

This paper cites Open-vocabulary pick and place via patch-level semantic maps.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Open-vocabulary pick and place via patch-level semantic maps

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.541061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.626255Z digest=sha256:77ea599587ebe8cd9a026062d2bdfdbabc54dc591cf54cf3ba94467b59f729ba

Observation 75dabcc9-0f20-4037-af67-af0a258986f6 · outbound

This paper cites Differentiable algorithm networks for composable robot learning.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Differentiable algorithm networks for composable robot learning

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.521147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.632372Z digest=sha256:1ef652dd90f34d19dbb75934c01db73313ddf727cd78dbea941a6d3812fb4c9d

Observation 9d8f071b-d038-4e8a-afc6-c68b5abfaea0 · outbound

This paper cites Practice Makes Perfect: Planning to Learn Skill Parameter Policies.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Practice Makes Perfect: Planning to Learn Skill Parameter Policies

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-11T14:26:11.638162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:26:11.638162Z digest=sha256:fd65ad2990b1188fd3c520103550cb2d2e7b85667599211e533643f4abc5f852

Observation b3626f9c-bd10-4cde-9a81-d365b9b96a9f · outbound

This paper cites Playing FPS games with deep reinforcement learning.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Playing FPS games with deep reinforcement learning

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.499038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.646160Z digest=sha256:281c337e5eed51bc3f214c15a4067f0c0a4032f9eb250c1f9bb1ef6828bcba6f

Observation c2c349a4-631d-41e8-8b32-8e19787d5ca8 · outbound

This paper cites Gated path planning networks.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Gated path planning networks

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.479120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.652109Z digest=sha256:1172c4d559d52dc8b7010e1c4df35756c78de0fe46ad6d002767a52cb8370bd1

Observation cc5755ab-9cd7-4531-afe2-d1a2e8b257d8 · outbound

This paper cites Discriminative particle filter reinforcement learning for complex partial observations.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Discriminative particle filter reinforcement learning for complex partial observations

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.459211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.661316Z digest=sha256:569e553db72e0fafc9733f466729910923058273f6cca79f9204a2e8151bca70

Observation 1a8f8d6a-f220-4ae3-8f9d-e030bb359b46 · outbound

This paper cites Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, Dharshan Kumaran, and Raia Hadsell.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, Dharshan Kumaran, and Raia Hadsell

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.439031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.668303Z digest=sha256:dec798cfd277babd1e8ed7f92cdd778e13056e43a57614edaa461f131a1cff25

Observation 3415ef71-ff1a-454a-869b-633efb9ec312 · outbound

This paper cites Learning to navigate in cities without a map.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Learning to navigate in cities without a map

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.419319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.675953Z digest=sha256:e41bb1519b630066c8a08ba4a52894ec305a4d613d04b6dca688a1f05a1a0eed

Observation be39e87a-a8d1-4ced-aa6f-c1a4c35a3dd1 · outbound

This paper cites Rusu, Joel Veness, Marc G.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Rusu, Joel Veness, Marc G

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-11T14:26:11.683495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:26:11.683495Z digest=sha256:92a3724a5753f777e68fabb32f29a7f1ae64d28eae6c11be6cb31e0e28c11a42

Observation 29ada361-5167-4f40-b9c2-1e71e65ab250 · outbound

This paper cites Goal-directed planning via hindsight experience replay.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Goal-directed planning via hindsight experience replay

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.389498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.690398Z digest=sha256:b104550210db90e7a44b267a45b945e6dc047f9abe0697f07e5671e7acbd880b

Observation 046a6596-9c02-4743-a348-87a128a9e177 · outbound

This paper cites Value prediction network.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Value prediction network

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.366329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.695985Z digest=sha256:8cdbf4db8d796fddaa727020d491002e835da361562e07a8395c705e3442293e

Observation c2004631-5258-4bd2-b671-8787b9f454de · outbound

This paper cites Neural map: Structured memory for deep reinforcement learning.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Neural map: Structured memory for deep reinforcement learning

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.350271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.703390Z digest=sha256:4bcd6b8eeb36a4957b235c6e67c15c2120429159bda038610f3823186ee65921

Observation c1d06af8-7d19-4c8c-a614-b51e67db3cbc · outbound

This paper cites Learning symmetric embeddings for equivariant world models.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Learning symmetric embeddings for equivariant world models

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.333248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.709303Z digest=sha256:076d396243e3ef4771707219ae901fab7b10b2400eaaef5fb7f03245d12d0ef6

Observation 3d15cd90-487f-47f6-9193-3a01f7d979c7 · outbound

This paper cites Temporal difference models: Model-free deep RL for model-based control.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Temporal difference models: Model-free deep RL for model-based control

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.316089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.715908Z digest=sha256:3bde6f402606bad8c43609521f50c6cd2e6678cfe8787a71413781ab762762b5

Observation c08d3712-b277-4374-a953-a9303a6868c2 · outbound

This paper cites Mastering A tari, G o, chess and shogi by planning with a learned model.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Mastering A tari, G o, chess and shogi by planning with a learned model

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.299750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.722218Z digest=sha256:1d8449612a36ee8896af4026a9245c1d1b8a071be55da8f7fbd5306e8284e628

Observation 3985cce0-a9dd-4e5d-bda9-c32aae34d5ac · outbound

This paper cites Value iteration networks.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Value iteration networks

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.281561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.727581Z digest=sha256:a76b0130ebe99cbffab1eef21575c4698da310a393b2ae316fd3fc5e02ab0120

Observation 3065d424-fd52-4466-9053-b9c7762f3c86 · outbound

This paper cites Probabilistic Robotics.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Probabilistic Robotics

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.263847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.733389Z digest=sha256:af6da297d9edab96f0b87e6f142b3321b99067e993b357a6708af4accf71f11e

Observation 699f11c6-e66c-4a78-8e7e-e7f11b107710 · outbound

This paper cites Grewe, and Jo \ a o Sacramento.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Grewe, and Jo \ a o Sacramento

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.246884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.739053Z digest=sha256:92de4d5bfb23eb132fc9201a65129d07f654bbaaeea6062dea80a11edd65ef61

Observation c9736984-7b79-4b95-b0fc-484f79e7d318 · outbound

This paper cites Unsupervised Predictive Memory in a Goal-Directed Agent.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Unsupervised Predictive Memory in a Goal-Directed Agent

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-11T14:26:11.746939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:26:11.746939Z digest=sha256:b1dba73d25aa0fa52f7f0219f7fb1f8ef474ad5fb636187764b4024ff4ada55b

Observation 744a391c-3acd-4e8d-8064-79cd98e4d209 · outbound

This paper cites Clara De Paolis Kaluza, Linfeng Zhao, Lawson Wong, and Rose Yu.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Clara De Paolis Kaluza, Linfeng Zhao, Lawson Wong, and Rose Yu

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.225587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.756605Z digest=sha256:6e5e56d287686f7335a1ed9854530a3616562f890335ca9659774cb94caaca23

Observation 7009cce6-56a4-40f3-8cfc-00c7882966a4 · outbound

This paper cites Toward compositional generalization in object-oriented world modeling.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Toward compositional generalization in object-oriented world modeling

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.205209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.762355Z digest=sha256:6c98c31d08569abc9af15ac663cae9dcb8cce7d0f15f6ec0239bdc1ecbe679d1

Observation 166b5969-b28e-4850-be21-c88d03121327 · outbound

This paper cites Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-11T14:26:11.992130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.768143Z digest=sha256:6b8e9e3daa9348ea1f5b01cd4f7156b018706080213f24487a4fad1a83da7181

Observation cd333d17-bd74-4222-9a4a-b14e4ba12d31 · outbound

This paper cites an unresolved cited work.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:26:12.186633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.774149Z digest=sha256:37ea51d1bf07362de367578702a0d4d2eee561832f5c91993704f03ab3a13629

Observation 702d6fad-72e0-4770-8997-4a681c044dd0 · outbound

This paper cites an unresolved cited work.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:26:12.170023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.779904Z digest=sha256:45808ff78974e609cc5b3ec03cd16451d74eb54fd333535a374076e61181f69d

Observation eafbd434-0d8d-4206-8a1c-6f2d1b15472e · outbound

This paper cites an unresolved cited work.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:26:12.149452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.785556Z digest=sha256:e4450c3d8d15d46a7a98d4a56d7df8eeee62ca4aa3fa9e8c9afb81637d0509ee

Observation 509e9ecc-ef8a-470c-ab6a-cb61a02f90b6 · outbound

This paper cites an unresolved cited work.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps Unresolved cited work

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-11T14:26:11.794881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:26:11.794881Z digest=sha256:31cb724b4633f27840d22ef45e687ddb36bd9107aaa0f330bff750d579217a31

Observation 327a4d24-d801-4881-b71c-128d8777ec25 · outbound

This paper cites RTFM : Generalising to new environment dynamics via reading.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps RTFM : Generalising to new environment dynamics via reading

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:26:12.129085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T14:26:11.800674Z digest=sha256:1bfb467673a90e3b514997712a06b047bae790a2b0a2aaa6cb0393f85ccc2032

Observation d1341910-5644-4c32-b259-a51f2c413708 · outbound

This paper cites write newline.

Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps write newline

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-11T14:26:11.806504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:26:11.806504Z digest=sha256:38f1c071fb9ff47b703f35c04a7a6b5a268d01fef919d1658a44a5a57f26593d

Pith citing papers

No inbound Pith citation observations are available.