Metadata correction for 2026.eacl-long.387

### JSON data block

```json
{
"anthology_id": "2026.eacl-long.387",
"abstract": "Leveraging multimodal large language models (MLLMs) to develop embodied agents offers significant promise for addressing complex real-world tasks. However, current evaluation benchmarks remain predominantly language-centric or heavily reliant on simulated environments, rarely probing the nuanced, knowledge-intensive reasoning essential for practical, real-world scenarios. To bridge this critical gap, we introduce the task of Sparsely Grounded Visual Navigation, explicitly designed to evaluate the sequential decision-making abilities of MLLMs in challenging, knowledge-intensive real-world environment. We operationalize this task with CityNav, a comprehensive benchmark encompassing four diverse global cities, specifically constructed to assess raw MLLM-driven agents in city navigation. Agents are required to rely solely on visual inputs and internal multimodal reasoning to sequentially navigate 50+ decision points without additional environmental annotations or specialized architectural modifications. Crucially, agents must autonomously achieve localization through interpreting city-specific cues and recognizing landmarks, perform spatial reasoning, and strategically plan and execute routes to their destinations. Through extensive evaluations, we demonstrate that current state-of-the-art MLLMs, reasoning techniques (e.g., GEPA, chain-of-thought, reflection) and competitive baseline PReP significantly underperform in this challenging setting. To address this, we propose Verbalization of Path (VoP), which explicitly grounds the agent’s internal reasoning by probing city-scale cognitive maps (key landmarks and directions toward the destination) from the MLLM, substantially enhancing navigation success. Project Webpage: https://dwipddalal.github.io/AgentNav/"
}
```

This abstract-only correction restores the missing benchmark name **CityNav**, as printed on the first page (8279) of the [published PDF](https://aclanthology.org/2026.eacl-long.387.pdf). Please preserve the title, author names and order, DOI, Anthology ID, BibTeX key (`dalal-etal-2026-mllms`), publication details, and PDF.

Start by locating the repository record for anthology_id `2026.eacl-long.387` and edit only its `abstract` field to restore the missing `CityNav` mention. Use the linked PDF as the source of truth for exact wording and then confirm no other fields were changed. Keep title, author order, DOI, Anthology ID, BibTeX key, publication details, and PDF unchanged. A newcomer is done when the corrected abstract matches the PDF and the rest of the record is intact.

Written by the indexing model from the issue text.