{"slug": "systematic-multi-agent-vision-and-language-navigation-formulation-benchmark-and", "title": "Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method", "summary": "Researchers posted arXiv:2609.35965v1, a paper presenting what they describe as the first systematic formalization of multi-agent vision-and-language navigation (VLN) as a constrained coordination problem, with subtasks carrying dependency and resource constraints such as presence locks and holding chains. The authors introduce MAVLN, a benchmark of 11,724 episodes across 145 scenes with teams of up to four agents under three instruction regimes, plus constraint-aware metrics, and TRISS, a coordination-ready system combining an LLM-based subtask scheduler, shared topological memory, and conflict-aware execution. Experiments establish TRISS as a baseline and show substantial room for improvement across scheduling, planning, and execution.", "body_md": "arXiv:2609.35965v1 Announce Type: new \nAbstract: Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require teams of robots to tackle tasks beyond the capabilities of any individual agent. We present Systematic Multi-Agent Vision-and-Language Navigation, providing, to our knowledge, the first systematic formalization of multi-agent VLN as a constrained coordination problem: each mission consists of subtasks carrying dependency and resource constraints (presence locks and holding chains). A verified four-stage crafting pipeline instantiates the task as MAVLN, comprising 11,724 episodes across 145 scenes with teams of up to four agents under three instruction regimes, accompanied by tailored constraint-aware metrics. We further present TRISS, a coordination-ready navigation system coupling an LLM-based subtask scheduler, a shared topological memory that turns each agent's exploration into team knowledge, and a conflict-aware execution mechanism that realizes simultaneous intentions as collision-free routes. Extensive experiments establish TRISS as a comprehensive baseline and reveal substantial room for improvement across scheduling, planning, and execution, highlighting the challenges of coordinating under MAVLN task constraints. Project page: https://xyz9911.github.io/mavln.", "url": "https://wpnews.pro/news/systematic-multi-agent-vision-and-language-navigation-formulation-benchmark-and", "canonical_source": "https://arxiv.org/abs/2609.35965", "published_at": "2026-09-30 04:00:00+00:00", "updated_at": "2026-09-30 04:21:01.842088+00:00", "lang": "en", "topics": ["robotics", "large-language-models", "ai-research", "ai-agents", "machine-learning"], "entities": ["arXiv", "MAVLN", "TRISS", "Vision-and-Language Navigation"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/systematic-multi-agent-vision-and-language-navigation-formulation-benchmark-and", "markdown": "https://wpnews.pro/news/systematic-multi-agent-vision-and-language-navigation-formulation-benchmark-and.md", "text": "https://wpnews.pro/news/systematic-multi-agent-vision-and-language-navigation-formulation-benchmark-and.txt", "jsonld": "https://wpnews.pro/news/systematic-multi-agent-vision-and-language-navigation-formulation-benchmark-and.jsonld"}}