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.

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Planning with Multi-Constraints via Collaborative Language Agents (2025.coling-main)

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Challenge: Recent advances in neural language models have sparked a new surge of intelligent agent research.
Approach: They propose a method for collaborative LLM-based multi-agent systems that simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks.
Outcome: The proposed method achieves an average success rate of 42.68% on two constraint-intensive benchmarks, TravelPlanner and API-Bank.
Personal Large Language Model Agents: A Case Study on Tailored Travel Planning (2024.emnlp-industry)

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Challenge: Large Language Models (LLMs) are becoming more autonomous and capable of handling real-world tasks through their access to tools, various planning strategies, and memory, referred to as LLM agents.
Approach: They introduce a personalized version of TravelPlanner and establish baselines for personal LLM agents by comparing generic and personal plans.
Outcome: The proposed model encapsulates user-related information, preferences, and personal concepts and provides baselines for personal LLM agents.
LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models (2025.findings-acl)

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Challenge: Large Language Models excel in various natural language tasks but struggle with long-horizon planning problems requiring structured reasoning.
Approach: They propose to integrate large language models into AP and NLP planning frameworks by reviewing current research and identifying critical challenges and future directions.
Outcome: The proposed frameworks are used to support reliable off-the-shelf AP planners.
Towards large language model-based personal agents in the enterprise: Current trends and open problems (2023.findings-emnlp)

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Challenge: Existing large language models (LLMs) are brittle to input changes and can produce inconsistent results for the same inputs.
Approach: They propose to use large language models to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal.
Outcome: The proposed use cases have many open problems in an exciting area of NLP research, such as trust and explainability, consistency and reproducibility, and the need for new metrics and benchmarks.
Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)

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Challenge: Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems .
Approach: This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving .
Outcome: This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance .
Revealing the Barriers of Language Agents in Planning (2025.naacl-long)

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Challenge: Existing studies show language agents lack human-level planning abilities . limitations and mechanisms to address them remain insufficiently understood .
Approach: They apply a feature attribution study to identify key factors hindering agent planning . they identify the limited role of constraints and diminishing influence of questions .
Outcome: The proposed model achieves 15.6% on a real-world planning benchmark.
Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)

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Challenge: Recent advances in large language models have led to the development of LLM-based autonomous agents.
Approach: They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process.
Outcome: The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention.
LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences (2025.findings-emnlp)

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Challenge: a recent study shows that large language models (LLMs) are limited in understanding natural language preferences.
Approach: They propose a novel LLM-as-Parser-based route planning system that utilizes an LLM to comprehend natural language, extract user preferences and recognize task dependencies.
Outcome: The proposed system achieves superior performance with guarantees across multiple constraints.
RETAIL: Towards Real-world Travel Planning for Large Language Models (2025.emnlp-main)

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Challenge: Existing travel planning systems assume users provide explicit queries, limiting their practical utility.
Approach: They propose a dataset RETAIL which supports decision-making for implicit queries while covering explicit queries.
Outcome: The proposed model achieves a 1.0% pass rate, suggesting real-world travel planning remains challenging.
Language Model as Planner and Formalizer under Constraints (2026.acl-long)

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Challenge: Large language models (LLMs) have been widely used in planning but lack interpretability and control.
Approach: They propose to augment widely used planning benchmarks with manually annotated, fine-grained, and rich natural language constraints spanning four formally defined categories.
Outcome: The proposed model outperforms existing models in 4 state-of-the-art reasoning LLMs, 4 formal languages, and 4 datasets.

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