Zero-Shot Mission-Level Evaluation for Aerial MLLM Agents

• URL: http://arxiv.org/abs/2607.22014v1
• Date: Fri, 24 Jul 2026 06:22:50 GMT
• Title: Zero-Shot Mission-Level Evaluation for Aerial MLLM Agents
• Abstract summary: We introduce MissionBench, a benchmark for mission-level evaluation of MLLMs in aerial 3D environments.<n>Across 22 open- and closed-source MLLMs, the strongest model succeeds on fewer than 35% of missions compared to 84.4% human performance.<n>Our analysis shows that mission-level competence requires coordinating multiple capabilities beyond spatial perception.
• Score: 31.4676947294786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) are emerging as core reasoning modules for embodied agents, yet it remains unclear how well general-purpose models can solve long-horizon embodied tasks from a single high-level instruction. We introduce MissionBench, a benchmark for mission-level evaluation of MLLMs in aerial 3D environments. It comprises 120 missions across five simulated 3D environments and four task families. Agents must autonomously plan, navigate, and report outcomes using only egocentric observations and its action history, without aerial-specific fine-tuning. Across 22 open- and closed-source MLLMs, the strongest model succeeds on fewer than 35% of missions compared to 84.4% human performance, highlighting the difficulty of multi-step embodied tasks. Despite large variations between model families, we observe gains from scaling, indicating that larger general-purpose models possess stronger zero-shot embodied capabilities. Our analysis shows that mission-level competence requires coordinating multiple capabilities beyond spatial perception, including multi-step planning and adaptive reasoning. This motivates closed-loop evaluation and highlights both the promise and risk of scaling-driven improvements for embodied AI.

Related papers

• The Hierarchy of Agentic Capabilities: Evaluating Frontier Models on Realistic RL Environments [0.11586753333439907]
  We present an empirical study evaluating frontier AI models on 150 workplace tasks within a realistic e-commerce RL environment from Surge.<n>Our analysis reveals an empirically-derived emphhierarchy of agentic capabilities that models must master for real-world deployment.<n>Weaker models struggle with fundamental tool use and planning, whereas stronger models primarily fail on tasks requiring contextual inference beyond explicit instructions.
  arXiv  Detail & Related papers  (2026-01-13T23:49:06Z)
• BEAR: Benchmarking and Enhancing Multimodal Language Models for Atomic Embodied Capabilities [61.173773299032746]
  Embodied capabilities refer to a suite of fundamental abilities for an agent to perceive, comprehend, and interact with the physical world.<n>We introduce BEAR, a comprehensive and fine-grained benchmark that evaluates MLLMs on atomic embodied capabilities.<n> BEAR comprises 4,469 interleaved image-video-text entries across 14 domains in 6 categories, including tasks from low-level pointing, trajectory understanding, spatial reasoning, to high-level planning.<n>We propose BEAR-Agent, a multimodal conversable agent that integrates pretrained vision models to strengthen MLLM perception, 3D understanding, and planning capabilities.
  arXiv  Detail & Related papers  (2025-10-09T19:18:36Z)
• Holistic Evaluation of Multimodal LLMs on Spatial Intelligence [81.2547965083228]
  We propose EASI for holistic Evaluation of multimodAl LLMs on Spatial Intelligence.<n>We conduct the study across eight key benchmarks, at a cost exceeding ten billion total tokens.<n>Our empirical study then reveals that GPT-5 demonstrates unprecedented strength in spatial intelligence (SI), yet (2) still falls short of human performance significantly across a broad spectrum of SI-tasks.
  arXiv  Detail & Related papers  (2025-08-18T17:55:17Z)
• NavBench: Probing Multimodal Large Language Models for Embodied Navigation [21.787627598744358]
  NavBench is a benchmark to evaluate the embodied navigation capabilities of MLLMs under zero-shot settings.<n>To support real-world deployment, we introduce a pipeline that converts MLLMs' outputs into robotic actions.
  arXiv  Detail & Related papers  (2025-06-01T14:21:02Z)
• MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence [74.51213082084428]
  MMSI-Bench is a VQA benchmark dedicated to multi-image spatial intelligence.<n>We conduct extensive experiments and thoroughly evaluate 34 open-source and proprietary MLLMs.<n>The strongest open-source model attains roughly 30% accuracy and OpenAI's o3 reasoning model reaches 40%, while humans score 97%.
  arXiv  Detail & Related papers  (2025-05-29T17:59:52Z)
• LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models [59.0256377330646]
  Lens is a benchmark with 3.4K contemporary images and 60K+ human-authored questions covering eight tasks and 12 daily scenarios.<n>This dataset intrinsically supports to evaluate MLLMs to handle image-invariable prompts, from basic perception to compositional reasoning.<n>We evaluate 15+ frontier MLLMs such as Qwen2.5-VL-72B, InternVL3-78B, GPT-4o and two reasoning models QVQ-72B-preview and Kimi-VL.
  arXiv  Detail & Related papers  (2025-05-21T15:06:59Z)
• MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models [34.138699712315]
  This paper introduces a novel vision--action (VLA) model, mixture of robotic experts (MoRE) for quadruped robots.<n>MoRE integrates multiple low-rank adaptation modules as distinct experts within a dense multi-modal large language model.<n>Experiments demonstrate that MoRE outperforms all baselines across six different skills and exhibits superior generalization capabilities in out-of-distribution scenarios.
  arXiv  Detail & Related papers  (2025-03-11T03:13:45Z)
• EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents [63.43699771428243]
  EmbodiedBench is an extensive benchmark designed to evaluate vision-driven embodied agents.<n>We evaluated 24 leading proprietary and open-source MLLMs within EmbodiedBench.<n> MLLMs excel at high-level tasks but struggle with low-level manipulation, with the best model, GPT-4o, scoring only 28.9% on average.
  arXiv  Detail & Related papers  (2025-02-13T18:11:34Z)
• EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents [57.4686961979566]
  EmbodiedEval is a comprehensive and interactive evaluation benchmark for MLLMs with embodied tasks.<n>It covers a broad spectrum of existing embodied AI tasks with significantly enhanced diversity.<n>We evaluated the state-of-the-art MLLMs on EmbodiedEval and found that they have a significant shortfall compared to human level on embodied tasks.
  arXiv  Detail & Related papers  (2025-01-21T03:22:10Z)

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.