Pith. sign in

Paper Citation Record · LEDGER

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents

As of 19 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2505.03947.

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

pith.paper-citation-record.v1
2505.03947 v1

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:46:50.244281Z

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

9 of 9 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 265152b9-2b55-40b2-bc81-06dd70d36774 · outbound

This paper cites write newline.

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents write newline

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-15T23:46:50.201092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:46:50.201092Z digest=sha256:bb592403bd46a6e94e133ea19b2010ac668e88f6375c60859c5657a387fd4d6b

Observation 98389204-7f83-475b-a274-5b58d17cf2e7 · outbound

This paper cites OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments.

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-15T23:46:50.207958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:46:50.207958Z digest=sha256:ab09aaca4a509623cc9c6a676c5d447b1095b10ed5236290f3ef2f3be92e414c

Observation e73bcb39-850c-4999-84cb-964b3b7d320b · outbound

This paper cites Deep Q-learning from Demonstrations.

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents Deep Q-learning from Demonstrations

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-15T23:46:50.213634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:46:50.213634Z digest=sha256:317271d89ca1f1d8dffe73bd5f1dcc42fce95c2c5f69bb307de8f9a15e89ba8e

Observation 57aa62d2-2535-4d4d-9e73-1a89890a2108 · outbound

This paper cites Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents.

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-15T23:46:50.218821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:46:50.218821Z digest=sha256:4baae0568b8e39dc7a46c301227f9f418802436ca01d805a467143cdcbc9a474

Observation b8feae48-f824-47fb-8470-4738f9d4bf0c · outbound

This paper cites Playing atari with deep reinforcement learning, 2013.

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents Playing atari with deep reinforcement learning, 2013

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T23:46:50.224295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:46:50.224295Z digest=sha256:724e42196079a7816c727c28ac24cade4cbf49af4c3368f1f79c863d27450359

Observation d99f0ac2-115e-47d9-b617-63d7fabd1180 · outbound

This paper cites A., Veness, J., Bellemare, M.

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents A., Veness, J., Bellemare, M

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-15T23:46:50.229215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:46:50.229215Z digest=sha256:74f8ff307bc5492c00850b2965b1c92d8dbed2dfe90f47f78a55426e4bd39d7e

Observation a97a5ffd-180f-4870-8abb-feba799e6db8 · outbound

This paper cites Prioritized Experience Replay.

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents Prioritized Experience Replay

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-15T23:46:50.233906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:46:50.233906Z digest=sha256:7b73ae94a585cd70a3915d3b66c9468a3d05aeb4c1fd92fcd719c7d0ca1ad030

Observation 042c2837-21b8-4c07-9522-f58b1af7985e · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-15T23:46:50.239455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:46:50.239455Z digest=sha256:2d61b99ae8480bb861b3c812886cd082165725295e183ecca21add01c751127e

Observation 204020c0-9bdd-4039-acf8-87e0e137b31c · outbound

This paper cites Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games.

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-15T23:46:50.244281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:46:50.244281Z digest=sha256:05ee44f6f768931e536540760c89feb683a55d2127d8c2e4382aac7ea80f467c

Pith citing papers

No inbound Pith citation observations are available.