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Paper Citation Record · LEDGER

Code-Driven Planning in Grid Worlds with Large Language Models

As of 18 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2505.10749.

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

pith.paper-citation-record.v1
2505.10749 v1

Coverage vector

measured 78 of 78 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:08:47.657693Z

measured 78 of 78 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

78 of 78 outbound references displayed

  • verified exact1
  • verified fuzzy35
  • unresolved40
  • parse uncertain2
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2e0f2e31-00c3-42a7-a262-57cd56392b43 · outbound

This paper cites Large Language Model Guided Self-Debugging Code Generation.

Code-Driven Planning in Grid Worlds with Large Language Models Large Language Model Guided Self-Debugging Code Generation

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:45.313638Z digest=sha256:998433b73b30a7a2fdbef6e6a448284ce4dc4ae445a9d85e4c00f6bcbf704a6b

Observation e8feaf36-b8cd-4315-a748-5ae0b86691cc · outbound

This paper cites Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025.

Code-Driven Planning in Grid Worlds with Large Language Models Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025

Reference 2

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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.

source=pdf_text observed=2026-08-15T21:08:45.319355Z digest=sha256:48087244536cb22348bb958d48bfb7df1cf59e504ad99d08607f532551f9d74d

Observation b4114be3-6216-4a20-874d-a1f3dad7689b · outbound

This paper cites Compositional founda- tion models for hierarchical planning.

Code-Driven Planning in Grid Worlds with Large Language Models Compositional founda- tion models for hierarchical planning

Reference 3

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

source=pdf_text observed=2026-08-15T21:08:45.420861Z digest=sha256:e1c6e801998b299fa4618272783c37c071fda50f4b992600ef766d1c4ecbc252

Observation 3c6ebae0-1490-4596-aae1-165c1f4fedd2 · outbound

This paper cites Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments.

Code-Driven Planning in Grid Worlds with Large Language Models Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments

Reference 4

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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.

source=pdf_text observed=2026-08-15T21:08:45.425737Z digest=sha256:42957db4f42ef954480b35e462b6a2db603c2c6ac7a3ab7f5d3a45ad03a44e06

Observation 55e9de06-d95e-4035-97d0-9ac4a7bfb7f9 · outbound

This paper cites Claude 3.7 sonnet, 2025.

Code-Driven Planning in Grid Worlds with Large Language Models Claude 3.7 sonnet, 2025

Reference 5

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source=pdf_text observed=2026-08-15T21:08:45.500073Z digest=sha256:cc09da70b03da45d8291f081f357d72c205a4dcaf99758ba504473ea24229b6f

Observation a7a1c508-0e61-4771-8a15-fc63bde55699 · outbound

This paper cites Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback.

Code-Driven Planning in Grid Worlds with Large Language Models Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback

Reference 6

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source=pdf_text observed=2026-08-15T21:08:45.534842Z digest=sha256:781e9109aa74b719afdc01beb0f7ab60db5c8c3c90105a4403ae7dd61de1ea5d

Observation b04e65a4-dbc3-4056-9ed0-fad029086184 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Code-Driven Planning in Grid Worlds with Large Language Models Evaluating Large Language Models Trained on Code

Reference 7

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source=pdf_text observed=2026-08-15T21:08:45.540922Z digest=sha256:c24e6b92b702a59cf5cf2f2394df990929117634508987fdac653968611182a1

Observation 2810f718-3130-409e-9cc0-879c86ec0b75 · outbound

This paper cites Teaching Large Language Models to Self-Debug.

Code-Driven Planning in Grid Worlds with Large Language Models Teaching Large Language Models to Self-Debug

Reference 8

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source=pdf_text observed=2026-08-15T21:08:45.545397Z digest=sha256:59002c5501bd883d735232fcee1b8534a1d5c5c40d0c78dbd7a0280fd6537b94

Observation 5feac328-cfef-44cd-9a0d-7f55b669f77a · outbound

This paper cites A Survey on Explainable Deep Reinforcement Learning.

Code-Driven Planning in Grid Worlds with Large Language Models A Survey on Explainable Deep Reinforcement Learning

Reference 9

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source=pdf_text observed=2026-08-15T21:08:45.596041Z digest=sha256:2ece084f7bf5655c7c241e59ea1fb02000b7fb21fb80e1d7bf1679b798a23182

Observation 1563d0e6-c3d3-4ae5-a2a1-ad66e6b3954a · outbound

This paper cites BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning.

Code-Driven Planning in Grid Worlds with Large Language Models BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning

Reference 10

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source=pdf_text observed=2026-08-15T21:08:45.633152Z digest=sha256:11e3e322b24cbeafafd55a852754090159e7e20eb0df2ea6858416e7dc93a47d

Observation c275dc88-96fa-4ac0-8c52-fa5eaa5fe5be · outbound

This paper cites Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks.

Code-Driven Planning in Grid Worlds with Large Language Models Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks

Reference 11

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source=pdf_text observed=2026-08-15T21:08:45.638344Z digest=sha256:14099ff0e68f3b6dff1ea5ad76b5a1d5a1f91652f9c8db90eeefcc872f4c561d

Observation 1c1a0bd6-f4c3-47e6-9196-8d88f9902f1f · outbound

This paper cites Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks.Advances in Neural Information Processing Systems, 36:73383–73394, 2023.

Code-Driven Planning in Grid Worlds with Large Language Models Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks.Advances in Neural Information Processing Systems, 36:73383–73394, 2023

Reference 12

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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.

source=pdf_text observed=2026-08-15T21:08:45.643722Z digest=sha256:d2d33e3bfa952e10398ff717368d2201a158c695ec78b0ab1fbd526f7dbc7e42

Observation cd39b7b8-9ebb-4f24-a5e4-f01b58c4cf9d · outbound

This paper cites Palm: Scaling language modeling with pathways.

Code-Driven Planning in Grid Worlds with Large Language Models Palm: Scaling language modeling with pathways

Reference 13

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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.

source=pdf_text observed=2026-08-15T21:08:45.648727Z digest=sha256:16e6615647676a7de7fbe0ebefd9db826c6f6c68e76383ad79a70299cc364e6f

Observation 140a82b4-36e5-456e-b302-edd8f955e8b2 · outbound

This paper cites Leveraging procedural generation to benchmark reinforcement learning.

Code-Driven Planning in Grid Worlds with Large Language Models Leveraging procedural generation to benchmark reinforcement learning

Reference 14

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source=pdf_text observed=2026-08-15T21:08:45.716107Z digest=sha256:17a74ebe39d82a0b3c6cd7aae0e775cc2d40cecca5b0a5b20dc2f67b601ed24c

Observation ece0847d-10ae-439d-9a6c-84e15066f3e8 · outbound

This paper cites Quantifying generalization in reinforcement learning.

Code-Driven Planning in Grid Worlds with Large Language Models Quantifying generalization in reinforcement learning

Reference 15

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

source=pdf_text observed=2026-08-15T21:08:45.720804Z digest=sha256:b82560bba3029e0230722b4371196f5c2f8f415607bcffb9d64587b99aea111b

Observation 8e25bdbf-5282-4270-b848-960335f0a180 · outbound

This paper cites Gemini 2.5 pro, 2025.

Code-Driven Planning in Grid Worlds with Large Language Models Gemini 2.5 pro, 2025

Reference 16

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source=pdf_text observed=2026-08-15T21:08:45.725479Z digest=sha256:14c460ab10b60f05f26473d72b00ef8a94adccac8e332dbf24e2018c79268f5b

Observation e3d4630f-8c2d-4183-a1f4-9cf32bfa0b6b · outbound

This paper cites Cycle: Learning to self-refine the code generation.

Code-Driven Planning in Grid Worlds with Large Language Models Cycle: Learning to self-refine the code generation

Reference 17

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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.

source=pdf_text observed=2026-08-15T21:08:45.730560Z digest=sha256:ab9f98ee1e3fa0804a7d725c009c413b2859938c9efd9edd7477ee42b5be6e8f

Observation fdf5e2f5-2110-4a31-a8ae-49fc4bdddb19 · outbound

This paper cites Learning of generalizable and interpretable knowledge in grid-based reinforcement learning environments.

Code-Driven Planning in Grid Worlds with Large Language Models Learning of generalizable and interpretable knowledge in grid-based reinforcement learning environments

Reference 18

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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.

source=pdf_text observed=2026-08-15T21:08:45.840739Z digest=sha256:c522c15c2b27354a6f13d91da7a913494b643b5323ac3e787bc25580b4039f7c

Observation c941f595-3fe6-4742-b7b8-b9957b7141ea · outbound

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

Code-Driven Planning in Grid Worlds with Large Language Models A formal basis for the heuristic determination of minimum cost paths

Reference 19

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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.

source=pdf_text observed=2026-08-15T21:08:45.883416Z digest=sha256:6f94a372d74c6b7d1cd8171b6c398d0bcfd5bbdc48b951ff9f6207225df09d00

Observation fd4777c4-aa1d-4934-8da0-de723f7d12f8 · outbound

This paper cites Neuro-symbolic approaches in artificial intelligence.

Code-Driven Planning in Grid Worlds with Large Language Models Neuro-symbolic approaches in artificial intelligence

Reference 20

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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.

source=pdf_text observed=2026-08-15T21:08:45.887506Z digest=sha256:3514c3a2c1845ce5e6ec45a3d1abf0f0792079ecde2b3b2721597d833f864c24

Observation 2d2ad186-2fed-480b-811b-85a87007b5a6 · outbound

This paper cites Gridtopix: Training embodied agents with minimal supervision.

Code-Driven Planning in Grid Worlds with Large Language Models Gridtopix: Training embodied agents with minimal supervision

Reference 21

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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.

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Observation 36025d3d-957c-47a6-8304-1afbc144e504 · outbound

This paper cites A Survey on Large Language Models for Code Generation.

Code-Driven Planning in Grid Worlds with Large Language Models A Survey on Large Language Models for Code Generation

Reference 22

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source=pdf_text observed=2026-08-15T21:08:46.023676Z digest=sha256:406d8a17cad69b73ae81b06681f99760b546cad29b3b8a2677feec886836f687

Observation 5cb0716c-c7b3-4640-a5cf-98f2e5c79474 · outbound

This paper cites LeDex: Training LLMs to Better Self-Debug and Explain Code.

Code-Driven Planning in Grid Worlds with Large Language Models LeDex: Training LLMs to Better Self-Debug and Explain Code

Reference 23

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source=pdf_text observed=2026-08-15T21:08:46.028087Z digest=sha256:0c26af04e7cd1316505e10a1b5ab4b9510e55e0987850176feb1998485027080

Observation 2d67b652-c060-4bf6-b1ca-172dc3cc340b · outbound

This paper cites Impact of code language models on automated program repair.

Code-Driven Planning in Grid Worlds with Large Language Models Impact of code language models on automated program repair

Reference 24

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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.

source=pdf_text observed=2026-08-15T21:08:46.032081Z digest=sha256:3ae1e16af742e1276498f3ddf53f402ebc0157e19b13383c7a85d9d7c5b8c73e

Observation b7662535-7598-4184-b832-f827ebfbcef6 · outbound

This paper cites Evaluating Open-Domain Question Answering in the Era of Large Language Models.

Code-Driven Planning in Grid Worlds with Large Language Models Evaluating Open-Domain Question Answering in the Era of Large Language Models

Reference 25

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source=pdf_text observed=2026-08-15T21:08:46.067542Z digest=sha256:8ffa8e903365f4b9fe1a06fe1ad7aa7d3984a85a7ad3a793fffbf4c7d8c9a466

Observation db8cf0cf-e028-42ab-85a1-9bca1f53f361 · outbound

This paper cites Large language models are few-shot testers: Ex- ploring llm-based general bug reproduction.

Code-Driven Planning in Grid Worlds with Large Language Models Large language models are few-shot testers: Ex- ploring llm-based general bug reproduction

Reference 26

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

source=pdf_text observed=2026-08-15T21:08:46.109204Z digest=sha256:88f215030fb3ef05c3483d9c8e17572193033d07ba0d265a9ea56d4400b14d9d

Observation 982fca07-d015-425c-a33c-c438262d197f · outbound

This paper cites A Survey Analyzing Generalization in Deep Reinforcement Learning.

Code-Driven Planning in Grid Worlds with Large Language Models A Survey Analyzing Generalization in Deep Reinforcement Learning

Reference 27

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source=pdf_text observed=2026-08-15T21:08:46.113424Z digest=sha256:659d5d8a8c718927fd9a4c397dfb46c6cfe20dbabcc081cb62e3c372100e7234

Observation 79d2073e-d2f2-4357-a899-c8eb31426ea0 · outbound

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

Code-Driven Planning in Grid Worlds with Large Language Models Code as policies: Language model programs for embodied control

Reference 28

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source=pdf_text observed=2026-08-15T21:08:46.117656Z digest=sha256:46e65e0755999e5586526c73ac3c62487a0ce598d56156fbe313a800da62d954

Observation c2e52aa7-498c-4161-b792-1dce18e37695 · outbound

This paper cites Learning to solve and verify: A self-play framework for code and test generation.

Code-Driven Planning in Grid Worlds with Large Language Models Learning to solve and verify: A self-play framework for code and test generation

Reference 29

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source=pdf_text observed=2026-08-15T21:08:46.206553Z digest=sha256:47567b72c26bee9b454adc0bf01ee14a2db25db84fe2431602dfc1c335f305c9

Observation c8cc4d0e-892c-4f94-b5c7-66df44ed342f · outbound

This paper cites Large language model-based code generation for the control of construction assembly robots: A hierarchical generation approach.

Code-Driven Planning in Grid Worlds with Large Language Models Large language model-based code generation for the control of construction assembly robots: A hierarchical generation approach

Reference 30

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raw_fallback, observed 2026-08-15T21:08:49.718471Z

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.

source=pdf_text observed=2026-08-15T21:08:46.291981Z digest=sha256:6284b04f9280fa9e2aff9b3e0896b01add6b1e1b86da519ac8f191d88e10e0e0

Observation 86c5e226-43e3-4daf-ad8a-0d12aa8ff9aa · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

Code-Driven Planning in Grid Worlds with Large Language Models Self-refine: Iterative refinement with self-feedback

Reference 31

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source=pdf_text observed=2026-08-15T21:08:46.349478Z digest=sha256:861ca081cf525c508a640262b4f1029adc79c9e061358e5156c1892758c3de9d

Observation 7b159b4c-6966-4e9c-a4d6-ded645582473 · outbound

This paper cites Audere: Automated strategy decision and realization in robot planning and control via llms.

Code-Driven Planning in Grid Worlds with Large Language Models Audere: Automated strategy decision and realization in robot planning and control via llms

Reference 32

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source=pdf_text observed=2026-08-15T21:08:46.392212Z digest=sha256:3e1f94c41ed1b438e19f912deb6830076bbdcce6922bb41fe2b4adf2e3fb84aa

Observation aecd0fd0-61e3-4b91-b472-9176f980f6fd · outbound

This paper cites The monte carlo method.

Code-Driven Planning in Grid Worlds with Large Language Models The monte carlo method

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-15T21:08:49.628650Z

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.

source=pdf_text observed=2026-08-15T21:08:46.397272Z digest=sha256:0122633ac346ecb52886123b8cca86d22522071a78adf71d4971bdc621b5183d

Observation c9ef0b98-2041-4b78-b45f-949e2137851b · outbound

This paper cites Gpt-4o, 2024.

Code-Driven Planning in Grid Worlds with Large Language Models Gpt-4o, 2024

Reference 34

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no resolver link, observed 2026-08-15T21:08:46.401662Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:46.401662Z digest=sha256:d539d45c4dcb83ad4aa002af8be707607aad5d9f46a51aaef48e6c28d9663a51

Observation aed31bd0-6a06-4fa9-8183-4b5bc405bd97 · outbound

This paper cites Gpt-o1, 2024.

Code-Driven Planning in Grid Worlds with Large Language Models Gpt-o1, 2024

Reference 35

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no resolver link, observed 2026-08-15T21:08:46.406304Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:46.406304Z digest=sha256:f1d39ebeb4358269a7f2512b80bfe90bcd2dbabb6ffea94db9ab829a9d8053b1

Observation b669f578-dd8b-4481-8d88-dd1c75565570 · outbound

This paper cites Gpt-o3-mini, 2025.

Code-Driven Planning in Grid Worlds with Large Language Models Gpt-o3-mini, 2025

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-15T21:08:49.517662Z

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.

source=pdf_text observed=2026-08-15T21:08:46.536063Z digest=sha256:b3bd2147891b3d97dce5eaaac606640b9b53d9cf19819f24095336ed277f7f79

Observation 1aea34d4-8c24-4f46-b8e8-fd5733c4a6a2 · outbound

This paper cites Openrouter.

Code-Driven Planning in Grid Worlds with Large Language Models Openrouter

Reference 37

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raw_fallback, observed 2026-08-15T21:08:49.488586Z

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.

source=pdf_text observed=2026-08-15T21:08:46.540382Z digest=sha256:d47b0c95bd029c3467cc5795b2e6f36bbb980a3ceb8925472e0aeb6c56019528

Observation 8a2a263b-5c6b-49db-9b6b-9f7df8768c5d · outbound

This paper cites Large language models as planning domain generators.

Code-Driven Planning in Grid Worlds with Large Language Models Large language models as planning domain generators

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-15T21:08:49.432366Z

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.

source=pdf_text observed=2026-08-15T21:08:46.616020Z digest=sha256:ad23c77de1b1ac4842b399e67f7b8d6d0086d0011f21ac7349f41687a9600d1f

Observation 3fc72666-beac-4f3d-8f9d-597a4136c7c4 · outbound

This paper cites Curiosity-driven exploration by self-supervised prediction.

Code-Driven Planning in Grid Worlds with Large Language Models Curiosity-driven exploration by self-supervised prediction

Reference 39

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no resolver link, observed 2026-08-15T21:08:46.621882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:46.621882Z digest=sha256:b3600365d546f1630b21f2b19449781fb49f89ab26b71373e00ae4881603c28b

Observation 1893da8b-bff3-4668-a3d0-f0fa596eb85b · outbound

This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

Code-Driven Planning in Grid Worlds with Large Language Models ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 40

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:08:46.627013Z digest=sha256:972291a3cc98923c0fc244ff4765f1352faa7245bb10373bc4c829c9f60579af

Observation 4e2970c4-7f0f-4192-bd8e-aa4583c1bf27 · outbound

This paper cites Tool learning with large language models: A survey.

Code-Driven Planning in Grid Worlds with Large Language Models Tool learning with large language models: A survey

Reference 41

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source=pdf_text observed=2026-08-15T21:08:46.631543Z digest=sha256:3eab5712534e348a58aa4972b2122f1f5855af8b90f67fe44905825a8b0966bd

Observation 69f4ba9b-878c-47ad-a7ec-893c546503c4 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Code-Driven Planning in Grid Worlds with Large Language Models Code Llama: Open Foundation Models for Code

Reference 42

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no resolver link, observed 2026-08-15T21:08:46.676488Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:08:46.676488Z digest=sha256:26216990827f02c84daaf16fd8ce5b474da1fda6f4df4c5bc67cf5613e6c1815

Observation cbea314d-be6a-4e48-8d8a-f0b8e8b16ef7 · outbound

This paper cites Generalized planning in pddl domains with pretrained large language models.

Code-Driven Planning in Grid Worlds with Large Language Models Generalized planning in pddl domains with pretrained large language models

Reference 43

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no resolver link, observed 2026-08-15T21:08:46.704150Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:08:46.704150Z digest=sha256:3391b346b3ce299b241d5667f5b81694726109868c2f8b382f6fad85347d85df

Observation bbe45836-a5ba-407a-9495-0ceb33ed7f5c · outbound

This paper cites Toward expert-level medical question answering with large language models.

Code-Driven Planning in Grid Worlds with Large Language Models Toward expert-level medical question answering with large language models

Reference 44

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no resolver link, observed 2026-08-15T21:08:46.709305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:46.709305Z digest=sha256:61b8d609e8be7233dcd12ac1cbcd1a0ff221c2b476e7bf66711b0f025b123681

Observation 8d08f125-a2d7-4c38-9311-a1205ea9102e · outbound

This paper cites Generating consistent PDDL domains with Large Language Models.

Code-Driven Planning in Grid Worlds with Large Language Models Generating consistent PDDL domains with Large Language Models

Reference 45

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no resolver link, observed 2026-08-15T21:08:46.819695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:46.819695Z digest=sha256:b694eceb3333ec8870fc9c1dfed01254b213b6ef39a8eedadeb003cfe2a4bd42

Observation 5b9c1626-0f91-4969-9bf1-648026c2f5c6 · outbound

This paper cites MazeBase: A Sandbox for Learning from Games.

Code-Driven Planning in Grid Worlds with Large Language Models MazeBase: A Sandbox for Learning from Games

Reference 46

Resolution
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no resolver link, observed 2026-08-15T21:08:46.825293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:46.825293Z digest=sha256:86d7dc55405ba002e2f2f978c37606ba0c0559362c02c81f61a53d01a8ea921e

Observation 781de912-f551-42f0-bb41-0db1ac6b4fdf · outbound

This paper cites Value iteration networks.

Code-Driven Planning in Grid Worlds with Large Language Models Value iteration networks

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:49.351208Z

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.

source=pdf_text observed=2026-08-15T21:08:46.830741Z digest=sha256:bc37c7543e90b7bf973f204a7b1ef922de8ae8034f33dbe70b5b185c7b9b5c9c

Observation 5e52666a-1d34-4b0d-87cb-2d5b55ef7b25 · outbound

This paper cites GRASP: A Grid-Based Benchmark for Evaluating Commonsense Spatial Reasoning.

Code-Driven Planning in Grid Worlds with Large Language Models GRASP: A Grid-Based Benchmark for Evaluating Commonsense Spatial Reasoning

Reference 48

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no resolver link, observed 2026-08-15T21:08:46.835228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:46.835228Z digest=sha256:32948be4eddb7fed513ed8c7e2615296a8354637241a0ec247aa917958bfccee

Observation d778ad7c-b0c2-48fe-a48f-efe8b666912f · outbound

This paper cites Grid-based mapping and tracking in dynamic environments using a uniform evidential environment representation.

Code-Driven Planning in Grid Worlds with Large Language Models Grid-based mapping and tracking in dynamic environments using a uniform evidential environment representation

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:49.257635Z

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.

source=pdf_text observed=2026-08-15T21:08:46.924666Z digest=sha256:6e2628535d7ca0024d185ca07c70af4d3ebd86f58f6a5399519392b691468c64

Observation 818ae438-4476-406f-954a-bfb9b323aecc · outbound

This paper cites CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement.

Code-Driven Planning in Grid Worlds with Large Language Models CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement

Reference 50

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no resolver link, observed 2026-08-15T21:08:46.929263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:46.929263Z digest=sha256:8ed85c2eb942225cac0774d08b81a605e0062a93b731263c89543b18320b4e80

Observation 585bd728-c31f-4982-bb63-1d9f9efd5820 · outbound

This paper cites Evaluating Large Language Models with Grid-Based Game Competitions: An Extensible LLM Benchmark and Leaderboard.

Code-Driven Planning in Grid Worlds with Large Language Models Evaluating Large Language Models with Grid-Based Game Competitions: An Extensible LLM Benchmark and Leaderboard

Reference 51

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no resolver link, observed 2026-08-15T21:08:46.999220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:46.999220Z digest=sha256:07f6a6897c800c445f22cceabb8a62ddcbd6772428e754dcdb433889e2c09a4f

Observation 5bf06e68-7dcc-4fcf-a314-d223bad67721 · outbound

This paper cites Care: A collision-aware mobile robot navigation in grid environment using improved breadth first search.

Code-Driven Planning in Grid Worlds with Large Language Models Care: A collision-aware mobile robot navigation in grid environment using improved breadth first search

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:49.215935Z

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.

source=pdf_text observed=2026-08-15T21:08:47.080036Z digest=sha256:95e1501df722b17b411bc9f3864760a79eca25d5ba19c59070168bdf25aa7b5e

Observation bb56760b-d1df-466a-8db0-70b3384453c6 · outbound

This paper cites On the planning abilities of large language models-a critical investigation.

Code-Driven Planning in Grid Worlds with Large Language Models On the planning abilities of large language models-a critical investigation

Reference 53

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no resolver link, observed 2026-08-15T21:08:47.083468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:47.083468Z digest=sha256:e19e66bf9fadf0e512ef4c447e66d18d2162e945a670aea635aa39209de38a91

Observation ba5e9918-b817-4096-95e8-bc3b8a249ef0 · outbound

This paper cites Llm^ 3: Large language model-based task and motion planning with motion failure reasoning.

Code-Driven Planning in Grid Worlds with Large Language Models Llm^ 3: Large language model-based task and motion planning with motion failure reasoning

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:49.117387Z

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.

source=pdf_text observed=2026-08-15T21:08:47.087427Z digest=sha256:c252d7334544c2290b6223aaac698af11fca2edc0aa50e172bcb4f17a0b213a7

Observation 6428cfea-5c09-4ed9-b326-324c23284d9f · outbound

This paper cites Executable code actions elicit better llm agents.

Code-Driven Planning in Grid Worlds with Large Language Models Executable code actions elicit better llm agents

Reference 55

Resolution
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no resolver link, observed 2026-08-15T21:08:47.092273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:47.092273Z digest=sha256:e89db6ff6ad9ea7671f155d7af057c94e5e6f0c0fc650d85c7626a971efe62b2

Observation 22f40d99-01e1-4246-9552-1698f3b46661 · outbound

This paper cites CodeT5+: Open Code Large Language Models for Code Understanding and Generation.

Code-Driven Planning in Grid Worlds with Large Language Models CodeT5+: Open Code Large Language Models for Code Understanding and Generation

Reference 56

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no resolver link, observed 2026-08-15T21:08:47.159543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:47.159543Z digest=sha256:c56eea0ece564bea7e8df1e2ca5775122a737dd1e6e756e33d6a7f751e096cde

Observation c976dcde-4cbc-47c5-90aa-391e710a1a13 · outbound

This paper cites Q-learning.

Code-Driven Planning in Grid Worlds with Large Language Models Q-learning

Reference 57

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no resolver link, observed 2026-08-15T21:08:47.199682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:47.199682Z digest=sha256:b5c34dd9358573394b6576fd83d8789df000dc9b95336f69e9b7e07ea6922277

Observation fa0a3c9d-6922-4f41-9dff-16c8f344f85c · outbound

This paper cites CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis.

Code-Driven Planning in Grid Worlds with Large Language Models CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis

Reference 58

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no resolver link, observed 2026-08-15T21:08:47.204289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:47.204289Z digest=sha256:23fcfd9df7c7fde709862cfb58d408fd5f7439e8e7f1bb25a887a47d568345cf

Observation 659e3387-79aa-4394-878e-060c28dad58c · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Code-Driven Planning in Grid Worlds with Large Language Models Chain-of-thought prompting elicits reasoning in large language models

Reference 59

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no resolver link, observed 2026-08-15T21:08:47.208413Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:08:47.208413Z digest=sha256:0a70029a6ab30970e7438dea144ee4854eb1eee75871316bc07388e19d5b33d9

Observation 2fe8a96c-706d-436b-8499-adc059e8294c · outbound

This paper cites Creative Robot Tool Use with Large Language Models.

Code-Driven Planning in Grid Worlds with Large Language Models Creative Robot Tool Use with Large Language Models

Reference 60

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no resolver link, observed 2026-08-15T21:08:47.213015Z

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source=pdf_text observed=2026-08-15T21:08:47.213015Z digest=sha256:d1445224f541b42793544dcece70c8c127214f866de15ad3465f0bd4631c7ba6

Observation 16877695-8b66-4745-a66c-1e0e9b246092 · outbound

This paper cites IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents.

Code-Driven Planning in Grid Worlds with Large Language Models IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents

Reference 61

Resolution
verified exact
local_arxiv, observed 2026-08-15T21:08:47.837080Z

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.

source=pdf_text observed=2026-08-15T21:08:47.306555Z digest=sha256:7a304ca0bcd973d6d0a4527888d852754634564eb73d40542cfa7ab27f4c38b6

Observation 795820fc-ec53-4f80-9b8d-58e4d5d4966f · outbound

This paper cites RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation.

Code-Driven Planning in Grid Worlds with Large Language Models RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation

Reference 62

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no resolver link, observed 2026-08-15T21:08:47.311219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:47.311219Z digest=sha256:fe819dc5ded9291a792cc7fa920fa10cde9aa3d2d2113639669fbb58b69c6736

Observation a8748221-5b38-444d-91a0-d37371c1f8dd · outbound

This paper cites There is a dedicated bullet point in the introduction for contributions, with further details throughout the paper.

Code-Driven Planning in Grid Worlds with Large Language Models There is a dedicated bullet point in the introduction for contributions, with further details throughout the paper

Reference 63

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verified fuzzy
raw_fallback, observed 2026-08-15T21:08:49.062783Z

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.

source=pdf_text observed=2026-08-15T21:08:47.317843Z digest=sha256:96fa2b90effe66614cf3095521bb6e130f908de38149d835194f4493f1cc2986

Observation 82885a52-115a-434f-b2b4-cbbd8cd1d354 · outbound

This paper cites Limitations.

Code-Driven Planning in Grid Worlds with Large Language Models Limitations

Reference 64

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verified fuzzy
raw_fallback, observed 2026-08-15T21:08:49.036445Z

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.

source=pdf_text observed=2026-08-15T21:08:47.385856Z digest=sha256:c857eb90e102be92e49961a3f36be43da3132fa01f7f13d3e43e5df6732bd745

Observation fde444ae-35ec-4c0a-91c9-515f8b10ad0b · outbound

This paper cites • All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.

Code-Driven Planning in Grid Worlds with Large Language Models • All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced

Reference 65

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verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.915950Z

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.

source=pdf_text observed=2026-08-15T21:08:47.391179Z digest=sha256:5d621e9311695110810dedfce49eb440bed2b3b80af3164c83d0887a38de0520

Observation d9f38e1e-c00c-4398-927e-b75dd8a68d3d · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Code-Driven Planning in Grid Worlds with Large Language Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 66

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verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.900211Z

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.

source=pdf_text observed=2026-08-15T21:08:47.396225Z digest=sha256:35a5f9787f38d9c597146e81663e797ea5f4723453ee32a852d252b2d1898978

Observation 82f253e8-6404-4ce7-a44b-7d35c00aaabc · outbound

This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

Code-Driven Planning in Grid Worlds with Large Language Models Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 67

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verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.843744Z

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.

source=pdf_text observed=2026-08-15T21:08:47.401067Z digest=sha256:61da705524817fc71711185e9a3b5343119e8ba26eeeba11cd4a5ad6fdb25d87

Observation a03707e5-d95f-4a31-9aa8-1a5bc0c7e9f7 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Code-Driven Planning in Grid Worlds with Large Language Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.763266Z

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.

source=pdf_text observed=2026-08-15T21:08:47.455793Z digest=sha256:482234bf00dbb76f412aeb72bcf5c22001e5400996d07ea5b9f7c8eff9fba69a

Observation 8f1e718f-b4bb-4291-8bc8-6685a39a25fe · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Code-Driven Planning in Grid Worlds with Large Language Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 69

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verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.670082Z

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.

source=pdf_text observed=2026-08-15T21:08:47.514725Z digest=sha256:a15b2d44b83a8c4792d805de81ba4ba90a4b484213a4459652b2f200a493eb87

Observation ab684b40-ea17-48c4-801a-67b32ef73937 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Code-Driven Planning in Grid Worlds with Large Language Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.655983Z

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.

source=pdf_text observed=2026-08-15T21:08:47.520963Z digest=sha256:8056edf5ae9d1551bb621a6480c9e0362d9d3cacf379d5b03b271c9a38be7357

Observation bbb8df1c-b58d-4b2d-8feb-d4edfb817e7b · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

Code-Driven Planning in Grid Worlds with Large Language Models Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.575615Z

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.

source=pdf_text observed=2026-08-15T21:08:47.526731Z digest=sha256:d11e77bd9eabdbb27e774d2f3ce63067ddb79410688585742005afd62515c64a

Observation e7f42fb2-5dd5-4037-a29b-8c0b187f3687 · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

Code-Driven Planning in Grid Worlds with Large Language Models Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.561455Z

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.

source=pdf_text observed=2026-08-15T21:08:47.569262Z digest=sha256:f3ee6fdfa565f223b9348cbb8df76420a6627779bf1454912bce13e2af52a2d6

Observation b3ea539a-18d4-418a-a2bb-4d8e1bf731e6 · outbound

This paper cites an unresolved cited work.

Code-Driven Planning in Grid Worlds with Large Language Models Unresolved cited work

Reference 73

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unresolved
no resolver link, observed 2026-08-15T21:08:47.573956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:47.573956Z digest=sha256:ccdffadb081abe31b06f24fbd7b7aaa5717e37e1c477713b8d56af8d331a01f1

Observation 3d5f98d0-07d2-4b76-bcdb-fa368a167e21 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

Code-Driven Planning in Grid Worlds with Large Language Models Guidelines: • The answer NA means that the paper does not use existing assets

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.457493Z

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.

source=pdf_text observed=2026-08-15T21:08:47.578665Z digest=sha256:fedd0910d96b712103709865de9b013e70c793e60bac25395e60548e25c02d67

Observation 9a14bdcb-f307-41df-b806-c39dde964b74 · outbound

This paper cites • Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates.

Code-Driven Planning in Grid Worlds with Large Language Models • Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.384644Z

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.

source=pdf_text observed=2026-08-15T21:08:47.635763Z digest=sha256:a28e49368b1e74da1767bd4262f52841448c937099dc48f479975eeae80d1d02

Observation 04405f3a-85b1-4620-9946-2e5745703755 · outbound

This paper cites an unresolved cited work.

Code-Driven Planning in Grid Worlds with Large Language Models Unresolved cited work

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-15T21:08:47.647428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:47.647428Z digest=sha256:1ff71fa42f1d1af14dd41e4b2858ce84a43e2f3cb91b39fddc9ed864aa8ebd44

Observation e7424c12-e195-4ed6-8fba-66dd29aba4b9 · outbound

This paper cites • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research.

Code-Driven Planning in Grid Worlds with Large Language Models • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-15T21:08:47.652611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:08:47.652611Z digest=sha256:faa9d5eb16ebaa3efa668921ce6e1b0bd158c61cea01267563211875bec87285

Observation f9ef7816-2596-44fb-9c1e-39672b16b554 · outbound

This paper cites Answer: [Yes] Justification: LLMs usage are detailed in the experimental setup section.

Code-Driven Planning in Grid Worlds with Large Language Models Answer: [Yes] Justification: LLMs usage are detailed in the experimental setup section

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:08:48.322845Z

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.

source=pdf_text observed=2026-08-15T21:08:47.657693Z digest=sha256:10a8a3026a91f997aa971faf5bcd085cb50d67058ce008335857153013174331

Pith citing papers

No inbound Pith citation observations are available.