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[2505.16928] Beyond Needle(s) in the Embodied Haystack: Environment, Architecture, and Training Considerations for Long Context Reasoning
Summary
This article presents the $ ext{∞-THOR}$ framework for long-horizon embodied tasks, focusing on enhancing long-context reasoning in AI through innovative architectural adaptations and a novel dataset for benchmarking.
Why It Matters
The development of $ ext{∞-THOR}$ is significant as it addresses the challenges of long-context reasoning in embodied AI, which is crucial for advancing AI's capabilities in complex, real-world environments. This research lays the groundwork for future AI systems that can perform robust long-term reasoning and planning, making it relevant for both academic research and practical applications in robotics and AI.
Key Takeaways
- Introduction of the $ ext{∞-THOR}$ framework for long-context reasoning.
- New embodied QA task, 'Needle(s) in the Embodied Haystack', tests AI agents' reasoning capabilities.
- Benchmark suite includes complex tasks designed for long-horizon scenarios.
- Architectural adaptations enhance LLM-based agents for improved reasoning.
- Experimental results provide insights into effective training strategies.
Computer Science > Artificial Intelligence arXiv:2505.16928 (cs) [Submitted on 22 May 2025 (v1), last revised 19 Feb 2026 (this version, v3)] Title:Beyond Needle(s) in the Embodied Haystack: Environment, Architecture, and Training Considerations for Long Context Reasoning Authors:Bosung Kim, Prithviraj Ammanabrolu View a PDF of the paper titled Beyond Needle(s) in the Embodied Haystack: Environment, Architecture, and Training Considerations for Long Context Reasoning, by Bosung Kim and Prithviraj Ammanabrolu View PDF HTML (experimental) 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 in...
