Papers with path planning
Can LLMs Learn to Map the World from Local Descriptions? (2026.acl-long)
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| Challenge: | Recent advances in large language models have demonstrated strong capabilities in tasks such as code generation and mathematical reasoning. |
| Approach: | They investigate whether large language models can construct coherent global spatial cognition by integrating fragmented relational descriptions. |
| Outcome: | The proposed models can generalize to unseen spatial relationships and exhibit latent representations aligned with real-world spatial distributions. |
Do Large Language Models have Problem-Solving Capability under Incomplete Information Scenarios? (2024.findings-acl)
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| Challenge: | Existing games such as "Who is undercover" are subjective and difficult to evaluate . |
| Approach: | They propose a game called BrainKing that evaluates LLMs' problem-solving capability under incomplete information scenarios. |
| Outcome: | The proposed game requires LLMs to identify target entities with limited yes-or-no questions and potential misleading answers. |
ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback (2024.emnlp-main)
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| Challenge: | Recent studies have shown that tool-augmented large language models can interact with external tools in multiple rounds and provide a final answer. |
| Approach: | They propose a tool-augmented large language model that can interact with external tools in multiple rounds and provide a final answer to an instruction. |
| Outcome: | The proposed framework significantly improves Match Rate, Pass Rate and Win Rate by 26.8%, 20.2%, and 5.6% compared to the SOTA model. |
Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)
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| Challenge: | Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations. |
| Approach: | They propose a framework that reformulates retrieval and generation as constrained optimization and path planning. |
| Outcome: | The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations. |