Beyond Needle(s) in the Embodied Haystack: Environment, Architecture, and Training Considerations for Long Context Reasoning

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 QA task, Needle(s) in the Embodied Haystack, where 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 and Memory-Augmented Goal-State modeling, alongside training strategies—context extension techniques and Context Parallelism—to equip VLM-based agents for extreme long-context reasoning and interaction. Experiments highlight the challenges of our benchmark and provide insights into training strategies and model behaviors under long-horizon conditions. Furthermore, we demonstrate sim-to-real transfer: our QA dataset alone improves photorealistic benchmark performance by up to +11.2%, and we successfully integrate with low-level manipulation controls. Our work provides a foundation for the next generation of embodied AI systems capable of robust, long-term reasoning and planning.