REALM-Bench: A Real-World Planning Benchmark for LLMs and Multi-Agent Systems

• URL: http://arxiv.org/abs/2502.18836v1
• Date: Wed, 26 Feb 2025 05:24:22 GMT
• Title: REALM-Bench: A Real-World Planning Benchmark for LLMs and Multi-Agent Systems
• Authors: Longling Geng, Edward Y. Chang,
• Abstract summary: The suite encompasses eleven designed problems that progress from basic to highly complex.<n>Each problem can be scaled along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions.<n>The benchmark aims to drive progress in developing more robust and adaptable AI planning systems for real-world applications.
• Score: 2.1331883629523634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This benchmark suite provides a comprehensive evaluation framework for assessing both individual LLMs and multi-agent systems in real-world planning scenarios. The suite encompasses eleven designed problems that progress from basic to highly complex, incorporating key aspects such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions. Each problem can be scaled along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation. The benchmark includes detailed specifications, evaluation metrics, and baseline implementations using contemporary frameworks like LangGraph, enabling rigorous testing of both single-agent and multi-agent planning capabilities. Through standardized evaluation criteria and scalable complexity, this benchmark aims to drive progress in developing more robust and adaptable AI planning systems for real-world applications.

Related papers

• Optimizing Sequential Multi-Step Tasks with Parallel LLM Agents [15.26802977779826]
  M1-Parallel is a framework that concurrently runs multiple multi-agent teams in parallel to uncover distinct solution paths.<n>We show that M1-Parallel with early termination achieves up to $2.2times$ speedup while preserving accuracy.<n>We further investigate strategies aimed at encouraging diverse execution plans but observe no additional performance gains over repeated sampling.
  arXiv  Detail & Related papers  (2025-07-11T18:09:22Z)
• SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling [70.01883340129204]
  Single-Pass.<n>with Reference-Guided Evaluation (SPARE)<n>Novel structured framework that enables single-pass, per-step annotation by aligning each solution step to one or multiple steps in a reference solution, accompanied by explicit reasoning for evaluation.<n>SPARE achieves competitive performance on challenging mathematical datasets while offering 2.6 times greater efficiency, requiring only 38% of the runtime.
  arXiv  Detail & Related papers  (2025-06-18T14:37:59Z)
• EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models [65.48902212293903]
  We present the Extremely Complex Instruction Following Benchmark (EIFBENCH) for evaluating large language models (LLMs)<n>EIFBENCH includes multi-task scenarios that enable comprehensive assessment across diverse task types concurrently.<n>We also propose the Segment Policy Optimization (SegPO) algorithm to enhance the LLM's ability to accurately fulfill multi-task workflow.
  arXiv  Detail & Related papers  (2025-06-10T02:39:55Z)
• PuzzleBench: A Fully Dynamic Evaluation Framework for Large Multimodal Models on Puzzle Solving [50.50405233978406]
  We propose a fully dynamic multimodal evaluation framework, named Open-ended Visual Puzzle Generation (OVPG) OVPG aims to generate fresh, diverse, and verifiable evaluation data automatically in puzzle-solving tasks. Built upon OVPG, we construct PuzzleBench, a dynamic and scalable benchmark comprising 11,840 VQA samples.
  arXiv  Detail & Related papers  (2025-04-15T05:29:31Z)
• Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification [5.727096041675994]
  Large Language Models (LLMs) have shown promise as robotic planners but often struggle with long-horizon and complex tasks. We propose a neuro-symbolic approach that enhances LLMs-based planners with Knowledge Graph-based RAG for hierarchical plan generation.
  arXiv  Detail & Related papers  (2025-04-06T18:36:30Z)
• Multi-Mission Tool Bench: Assessing the Robustness of LLM based Agents through Related and Dynamic Missions [12.218102495632937]
  Large language models (LLMs) demonstrate strong potential as agents for tool invocation due to their advanced comprehension and planning capabilities. We propose the Multi-Mission Tool Bench. In the benchmark, each test case comprises multiple interrelated missions. We also propose a novel method to evaluate the accuracy and efficiency of agent decisions with dynamic decision trees.
  arXiv  Detail & Related papers  (2025-04-03T14:21:33Z)
• Parallelized Planning-Acting for Efficient LLM-based Multi-Agent Systems [31.894636711684523]
  We propose a novel parallelized planning-acting framework for Multi-Agent Systems. The proposed framework features a dual-thread architecture with interruptible execution to enable concurrent planning and acting.
  arXiv  Detail & Related papers  (2025-03-05T13:53:10Z)
• MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents [59.825725526176655]
  Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents. Existing benchmarks either focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition. We introduce MultiAgentBench, a benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
  arXiv  Detail & Related papers  (2025-03-03T05:18:50Z)
• PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving [89.60370366013142]
  We propose PlanGEN, a model-agnostic and easily scalable agent framework with three key components: constraint, verification, and selection agents.<n>Specifically, our approach proposes constraint-guided iterative verification to enhance performance of inference-time algorithms.
  arXiv  Detail & Related papers  (2025-02-22T06:21:56Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning [51.54046200512198]
  Retrieval-augmented generation (RAG) is extensively utilized to incorporate external, current knowledge into large language models.<n>A standard RAG pipeline may comprise several components, such as query rewriting, document retrieval, document filtering, and answer generation.<n>To overcome these challenges, we propose treating the RAG pipeline as a multi-agent cooperative task, with each component regarded as an RL agent.
  arXiv  Detail & Related papers  (2025-01-25T14:24:50Z)
• EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios [53.26658545922884]
  We introduce EgoPlan-Bench2, a benchmark designed to assess the planning capabilities of MLLMs across a wide range of real-world scenarios.<n>We evaluate 21 competitive MLLMs and provide an in-depth analysis of their limitations, revealing that they face significant challenges in real-world planning.<n>Our approach enhances the performance of GPT-4V by 10.24 on EgoPlan-Bench2 without additional training.
  arXiv  Detail & Related papers  (2024-12-05T18:57:23Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems. In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy. Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models [38.89166693142495]
  ET-Plan-Bench is a benchmark for embodied task planning using Large Language Models (LLMs)<n>It features a controllable and diverse set of embodied tasks varying in different levels of difficulties and complexities.<n>Our benchmark distinguishes itself as a large-scale, quantifiable, highly automated, and fine-grained diagnostic framework.
  arXiv  Detail & Related papers  (2024-10-02T19:56:38Z)
• Multi-Agent Planning Using Visual Language Models [2.2369578015657954]
  Large Language Models (LLMs) and Visual Language Models (VLMs) are attracting increasing interest due to their improving performance and applications across various domains and tasks.<n>LLMs andVLMs can produce erroneous results, especially when a deep understanding of the problem domain is required.<n>We propose a multi-agent architecture for embodied task planning that operates without the need for specific data structures as input.
  arXiv  Detail & Related papers  (2024-08-10T08:10:17Z)
• LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations [51.76373105981212]
  Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms.<n>We introduce a comprehensive reranking framework, designed to seamlessly integrate various reranking criteria.<n>A customizable input mechanism is also integrated, enabling the tuning of the language model's focus to meet specific reranking needs.
  arXiv  Detail & Related papers  (2024-06-18T09:29:18Z)
• Planning with Multi-Constraints via Collaborative Language Agents [13.550774629515843]
  This paper introduces Planning with Multi-Constraints (PMC), a zero-shot methodology for collaborative multi-agent systems.<n>PMC simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks.<n>PMC achieved an average 42.68% success rate on TravelPlanner, significantly higher than GPT-4 (2.92%), and outperforming GPT-4 with ReAct on API-Bank by 13.64%.
  arXiv  Detail & Related papers  (2024-05-26T10:33:17Z)
• TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation [41.21899915378596]
  We propose a multi-agent framework based on dynamic Task Decomposition and Agent Generation (TDAG)<n>This framework dynamically decomposes complex tasks into smaller subtasks and assigns each to a specifically generated subagent.<n>ItineraryBench is designed to assess agents' abilities in memory, planning, and tool usage across tasks of varying complexity.
  arXiv  Detail & Related papers  (2024-02-15T18:27:37Z)
• Improving Planning with Large Language Models: A Modular Agentic Architecture [7.63815864256878]
  Large language models (LLMs) often struggle with tasks that require multi-step reasoning or goal-directed planning. We propose an agentic architecture, the Modular Agentic Planner (MAP), in which planning is accomplished via the recurrent interaction of specialized modules. We find that MAP yields significant improvements over both standard LLM methods.
  arXiv  Detail & Related papers  (2023-09-30T00:10:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.