Papers with planning process
LRPLAN: A Multi-Agent Collaboration of Large Language and Reasoning Models for Planning with Implicit & Explicit Constraints (2025.findings-emnlp)
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| Challenge: | LRPlan is a language-based multi-agent system for complex planning problems . large language models are often unable to maintain consistency across the planning process . |
| Approach: | They propose a language-based multi-agent architecture where LLM and LRM agents collaborate at training time to abstract important patterns, heuristics and insights about the domain. |
| Outcome: | The proposed language-based multi-agent architecture outperforms existing models and makes it publicly available. |
Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task (2020.emnlp-main)
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| Challenge: | despite the success of contextualized language models, language models cannot capture textual coherence of a long, multi-sentence document. |
| Approach: | They propose a paragraph completion task that predicts masked sentences in a sentence . they propose SSPlanner that predict what to say first and guides the pretrained model . |
| Outcome: | The proposed model outperforms baseline generation models on the paragraph completion task in automatic and human evaluation. |
Explicit Planning Helps Language Models in Logical Reasoning (2023.emnlp-main)
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| Challenge: | Existing systems that use pre-trained large language models to perform multi-step logical reasoning have been unable to perform this task. |
| Approach: | They propose a system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure. |
| Outcome: | The proposed system outperforms other competing methods on multiple datasets and significantly outperformed chain-of-thought prompting on the PrOntoQA dataset. |
Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents (2025.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) are developing but lack external feedback . there is no clear on how to select reward models for agents . |
| Approach: | They propose a benchmark to evaluate agent reward modeling ability in MLLMs . they use multiple dimensions and real-world agent scenarios evaluation . |
| Outcome: | The proposed benchmark evaluates agent performance in multimodal large language models . it covers perception, planning, and safety with 7 scenarios and is highly difficult and high-quality . |
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)
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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang
| Challenge: | EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers. |
| Approach: | They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs. |
| Outcome: | The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets. |
PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning (2025.acl-long)
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| Challenge: | Recent studies have shown that LLMs can be significantly improved by integrating external tools. |
| Approach: | They propose a framework that integrates external tools into large language models to evaluate their ability to generate action plans. |
| Outcome: | The proposed framework evaluates the ability of large language models to generate action plans and generate action plan templates. |