Done, But Not Sure: Disentangling World Completion from Self-Termination in Embodied Agents
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Abstract (arXiv)
Standard embodied evaluations do not independently score whether an agent correctly commits to task completion at episode closure, a capacity we call terminal commitment. Behaviorally distinct failures--never completing the task, completing it but failing to stop, and reporting success without sufficient evidence--collapse into the same benchmark failure. We introduce VIGIL, an evaluation framework that makes terminal commitment independently measurable. Under VIGIL's default protocol, agents observe only egocentric RGB, receive no action-success signals, and must end each episode with a semantic report checked deterministically against hidden world state. This yields two separate scores: world-state completion (W) and benchmark success (B), where B additionally requires a correct terminal report. This decoupling makes four outcome categories distinguishable: missed execution, post-attainment drift, unsupported commitment, and verified success. Across 20 models on 1,000 frozen episodes, systems with comparable W differ by up to 19.7 pp in B: one model converts achieved states into correct reports, while another with near-identical execution drifts past the goal without closing. An action-feedback intervention further tests the separation: execution-oriented signals improve W broadly, yet commitment failures persist in models that do not already ground terminal reports in the achieved state. VIGIL provides a protocol that makes terminal commitment independently visible and scorable.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 37 of its 43 references that are in the map. First posted on arXiv 9 May 2026.
Builds on
37 of 43 references.
References · 37
- 2025Qwen3 Technical Reportcited by 8,195
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
- 2025Qwen3-VL Technical Reportcited by 2,534
- 2022Language Models (Mostly) Know What They Knowcited by 1,783
- 2025InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiencycited by 1,419
- 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
- 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,057
- 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 850
- 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
- 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilitiescited by 679
- 2024Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spacescited by 580
- 2025Kimi-VL Technical Reportcited by 381
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- 2022ProcTHOR: Large-Scale Embodied AI Using Procedural Generationcited by 369
- 2025Seed1.5-VL Technical Reportcited by 367
- 2023Robots That Ask For Help: Uncertainty Alignment for Large Language Model Plannerscited by 298
- 2024SpatialRGPT: Grounded Spatial Reasoning in Vision Language Modelscited by 297
- 2024RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Roboticscited by 242
- 2025EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agentscited by 213
- 2021TEACh: Task-driven Embodied Agents that Chatcited by 194
- 2025MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligencecited by 169
- 2024Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Makingcited by 142
- 2025RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concretecited by 134
- 2024RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Roboticscited by 133
- 2025Cambrian-S: Towards Spatial Supersensing in Videocited by 123
- 2025OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Modelscited by 105
- 2024GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigationcited by 93
- 2022VLMbench: A Compositional Benchmark for Vision-and-Language Manipulationcited by 68
- 2025EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agentscited by 55
- 2024LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agentscited by 51
- 2025VLM4D: Towards Spatiotemporal Awareness in Vision Language Modelscited by 43
- 2025MiMo-Embodied: X-Embodied Foundation Model Technical Reportcited by 36
- 2025Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Modelscited by 34
- 2026RynnBrain: Open Embodied Foundation Modelscited by 33
- 2026Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?cited by 18
- 2024ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic Environmentscited by 16
- 2025CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observationscited by 3
- 2026How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspectivecited by 2
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Citing papers per year: 2026: 7.
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