{
  "id": "2505.16928",
  "version": "v2",
  "published": "2025-05-22T17:20:38.000Z",
  "updated": "2025-10-01T17:51:44.000Z",
  "title": "Beyond Needle(s) in the Embodied Haystack: Environment, Architecture, and Training Considerations for Long Context Reasoning",
  "authors": [
    "Bosung Kim",
    "Prithviraj Ammanabrolu"
  ],
  "categories": [
    "cs.AI",
    "cs.LG",
    "cs.RO"
  ],
  "abstract": "We introduce $\\infty$-THOR, a new framework for long-horizon embodied tasks that advances long-context understanding in embodied AI. $\\infty$-THOR provides: (1) a generation framework for synthesizing scalable, reproducible, and unlimited long-horizon trajectories; (2) a novel embodied QA task, Needle(s) in the Embodied Haystack, where multiple scattered clues across extended trajectories test agents' long-context reasoning ability; and (3) a long-horizon dataset and benchmark suite featuring complex tasks that span hundreds of environment steps, each paired with ground-truth action sequences. To enable this capability, we explore architectural adaptations, including interleaved Goal-State-Action modeling, context extension techniques, and Context Parallelism, to equip LLM-based agents for extreme long-context reasoning and interaction. Experimental results and analyses highlight the challenges posed by our benchmark and provide insights into training strategies and model behaviors under long-horizon conditions. Our work provides a foundation for the next generation of embodied AI systems capable of robust, long-term reasoning and planning.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2025-10-01T17:51:44.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "long context reasoning",
      "embodied haystack",
      "training considerations",
      "environment",
      "long-horizon"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}