Paper Citation Record · LEDGER

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning

As of 18 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2504.17282.

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

pith.paper-citation-record.v1
2504.17282 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:49:09.412095Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

36 of 36 outbound references displayed

verified exact0
verified fuzzy7
unresolved29
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2cd06ae8-6364-4505-887a-d360e806da26 · outbound

This paper cites Developing a computer use model.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Developing a computer use model

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:49:11.854451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6f7bf0b8-78d1-4f4d-a04a-bd3aa1cf70c7 · outbound

This paper cites Uniter: Universal image-text representation learning.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Uniter: Universal image-text representation learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:49:11.751490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2a27898f-37d1-4714-8564-c90a9f5edb2c · outbound

This paper cites SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.012769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02e34939-b0ca-4e1d-8a13-2aeae1e66b1e · outbound

This paper cites The theory of affordances.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning The theory of affordances

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:49:11.244301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d54c096e-6be0-420d-bc3d-090016d61994 · outbound

This paper cites VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.033394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b0bc8bb-8f61-49ac-9690-25ecb4844780 · outbound

This paper cites A data-driven approach for learning to control computers.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning A data-driven approach for learning to control computers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.040202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac4f2dda-a3c9-4579-8f20-8f3991945ab2 · outbound

This paper cites What can i do here? a theory of affordances in reinforcement learning.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning What can i do here? a theory of affordances in reinforcement learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.053057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93f1210f-e483-456e-91ec-ca18818408e3 · outbound

This paper cites Language Models can Solve Computer Tasks.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Language Models can Solve Computer Tasks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.065536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8aeff68b-927b-4002-8241-9c9a208b97f0 · outbound

This paper cites Affordance-Guided Reinforcement Learning via Visual Prompting.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Affordance-Guided Reinforcement Learning via Visual Prompting

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.072600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c38090ec-d281-474a-b7ab-3fb578c7307c · outbound

This paper cites Code as policies: Language model programs for embodied control, 2023.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Code as policies: Language model programs for embodied control, 2023

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.079557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2c3298b-861a-4172-9203-1f7b4946a3cb · outbound

This paper cites Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.090104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76c6d6d4-50be-4e9c-be2b-248f553a2e39 · outbound

This paper cites MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.104480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 244130b4-9150-468f-80ee-e2c4d87c3c55 · outbound

This paper cites Eureka: Human-Level Reward Design via Coding Large Language Models.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.110228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0f1b129-f584-4f5f-8f7a-431082ad5192 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.117000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 927e2feb-6dda-4ca5-9f95-4f61a0104888 · outbound

This paper cites Computer-using agent: Introducing a universal interface for ai to interact with the digital world.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Computer-using agent: Introducing a universal interface for ai to interact with the digital world

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.127020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70e6037b-9729-4242-a02d-29c23b7a073b · outbound

This paper cites Gpt-4 technical report, 2023.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Gpt-4 technical report, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:49:11.087355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ae1b10f-8704-467d-942d-e04a9a5eb038 · outbound

This paper cites Affordancellm: Grounding affordance from vision language models, 2024.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Affordancellm: Grounding affordance from vision language models, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:49:11.026745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 636e48e2-81fa-4972-8eae-72e28b6ee7cf · outbound

This paper cites Learning transferable visual models from natural language supervision, 2021.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Learning transferable visual models from natural language supervision, 2021

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.173123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f639c45-05cd-40df-9ae5-09f8000c94d8 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.189567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55d918b1-dbfd-4aa4-a20c-7c6b5381cfa5 · outbound

This paper cites Efficient reductions for imitation learning.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Efficient reductions for imitation learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:49:10.889238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7649d9fb-99fe-4be6-b8f9-260201ec37fe · outbound

This paper cites Prioritized Experience Replay.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Prioritized Experience Replay

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.218377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bda557d-3ef6-4bf2-9113-3c65213a7d44 · outbound

This paper cites From pixels to ui actions: Learning to follow instructions via graphical user interfaces.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning From pixels to ui actions: Learning to follow instructions via graphical user interfaces

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.242278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0337c792-d69f-4c7a-ad60-b1bb88b04025 · outbound

This paper cites World of bits: An open-domain platform for web-based agents.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning World of bits: An open-domain platform for web-based agents

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.253142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 843d2154-1953-40cb-b97b-fbdbb8a4819b · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Mastering the game of go with deep neural networks and tree search

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.263138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c341beff-48b7-401c-ac7b-9b53b03d654a · outbound

This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.276370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f8a2b6c-921e-4c96-9543-535c95787a39 · outbound

This paper cites Reinforcement learning: An introduction.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Reinforcement learning: An introduction

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.292550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0269a0a8-39b7-4cc9-b2a9-e5b814bd3c66 · outbound

This paper cites Deep Reinforcement Learning with Double Q-learning.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Deep Reinforcement Learning with Double Q-learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.317937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbc6079a-c8da-4494-b936-bd504146a7a9 · outbound

This paper cites Code as reward: Empowering reinforcement learning with vlms, 2024.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Code as reward: Empowering reinforcement learning with vlms, 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:49:10.707685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d3968bb-e2a2-4383-a8de-5dec01bb44d8 · outbound

This paper cites Voyager: An open-ended embodied agent with large language models, 2023.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Voyager: An open-ended embodied agent with large language models, 2023

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.350950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29bf9a64-c46f-4ffc-ad5c-7868bc37ebd0 · outbound

This paper cites Q-learning.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Q-learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.358128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae19d999-e9d8-4ad3-b2fe-c1cb23d6bfa2 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.375710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f2c93d4-a092-419f-9074-90bd50c5bf78 · outbound

This paper cites Octopus: Embodied Vision-Language Programmer from Environmental Feedback.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Octopus: Embodied Vision-Language Programmer from Environmental Feedback

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.382500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e81a7c74-74ae-4831-8df6-5ea45148d072 · outbound

This paper cites write newline.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning write newline

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.388753Z

Source-reported events for the cited work

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Observation 5acb3e07-ba17-4243-809a-9bd97665ec30 · outbound

This paper cites @esa (Ref.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning @esa (Ref

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.395648Z

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Unavailable: canonical work link unavailable.

Observation e728414d-76de-4ab0-b0e9-9bcca7b7dcbc · outbound

This paper cites an unresolved cited work.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Unresolved cited work

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.404655Z

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Unavailable: canonical work link unavailable.

Observation bdde56ee-7ede-4388-b57e-b29695062cfe · outbound

This paper cites an unresolved cited work.

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Unresolved cited work

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T10:49:09.412095Z

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Pith citing papers

No inbound Pith citation observations are available.