Code-Driven Planning in Grid Worlds with Large Language Models | Paper Citation Record

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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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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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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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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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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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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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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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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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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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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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:08:50.232825Z

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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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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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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

Resolution: unresolved; no resolver link, observed 2026-08-15T21:08:45.725479Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:08:50.098716Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:08:50.067711Z

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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:08:49.952626Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:08:49.916084Z

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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:08:49.871554Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-15T21:08:46.067542Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:08:49.824873Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-15T21:08:46.113424Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-15T21:08:46.117656Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-15T21:08:46.206553Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:08:49.718471Z

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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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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

Resolution: unresolved; no resolver link, observed 2026-08-15T21:08:46.392212Z

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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

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

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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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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

Resolution: parse uncertain; no resolver link, observed 2026-08-15T21:08:46.406304Z

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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

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

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

This paper cites Openrouter.

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

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:08:49.488586Z

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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

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

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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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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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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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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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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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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

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

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

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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

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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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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

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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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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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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

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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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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

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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

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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

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

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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

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

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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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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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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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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

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

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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

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

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

This paper cites Limitations.

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

Reference 64

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

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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

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

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

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

Reference 66

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

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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

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

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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

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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

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

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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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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

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

Reference 73

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

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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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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.

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.

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 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers