Brief context
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Original paper
The executive brief below is grounded in the source paper and linked back to the arXiv abstract.
In real home deployments, household agents must often operate from a complete household scene and a situated household request, rather than from a clean task specification. Such requests require agents to identify task-relevant entities, recover intended task conditions, and resolve ordering constraints from the surrounding scene context. We formalize this capability as full-scene household reasoning: given a complete household scene and a situated household request, an agent must infer executable task structure before producing a grounded skill-level action sequence. This setting is challenging because complete household scenes contain substantial task-irrelevant information, making direct complete-scene prompting inefficient and error-prone. In practical deployment, this challenge is further amplified by privacy and local compute constraints, which favor compact open-weight models with limited long-context reasoning ability. We propose TaskGround, a training-free and model-agnostic Ground-Infer-Execute framework that grounds complete scenes into compact task-relevant scene slices, infers executable task structure, and compiles it into grounded skill-level action sequences. To evaluate this setting, we introduce FullHome, a human-validated evaluation suite of 400 household tasks spanning diverse home-scale environments and both goal-oriented and process-constrained requirements. On FullHome, TaskGround improves task success rates by large margins across both proprietary and open-weight models. Notably, it makes Qwen3.5-9B competitive with GPT-5 under direct complete-scene prompting while reducing total input-token cost by up to 18x. Our results identify executable task-structure inference as a central bottleneck in full-scene household reasoning and show that structured grounding can make compact local models substantially more effective for practical household deployment.
Executive brief
A short business-reader brief that explains why the paper matters now and what to watch or do next.
Why this is worth your attention
Household robots and in-home agents do not mainly fail because the model cannot write a plan; they fail because real rooms are noisy context and user requests leave goals and ordering constraints implicit. TaskGround points to a cheaper control pattern: shrink the scene to relevant objects, infer explicit task structure, then use deterministic execution rules, letting smaller open models close much of the gap to frontier direct prompting while cutting input tokens sharply. The evidence is strong inside structured simulators and relevant for teams building embodied or spatial agents, but it is not yet proof of real-home reliability.
- If this result holds up, the practical advantage is not just better household reasoning; it is a cheaper architecture for embodied agents. Teams should evaluate whether scene filtering, explicit goal inference, and rule-based precondition handling can remove enough burden from the LLM to use smaller or more private model deployments.
- The paper directly challenges the assumption that putting the whole home scene into a large context window is the right default. In the reported setup, full-scene prompting was far more token-heavy and often less successful than first compressing the scene into a task-relevant slice.
- For robotics, smart-home, warehouse, or spatial-agent vendors, ask how their system constrains object IDs, affordances, and action preconditions before execution. A demo that merely produces plausible action text is weaker than one that proves the agent cannot call impossible actions on the wrong objects.
- The evidence is meaningful but bounded: FullHome is simulator-backed, structured, and built around predefined skills. Real deployments add perception errors, partial observability, user ambiguity, safety constraints, and recovery from failed actions, none of which are solved by these results.
- The most important next proof point is not another simulator score; it is whether this decomposition lets compact models run reliably with acceptable latency, hardware cost, and privacy controls in messy environments. The paper’s open-model results are promising, but the reported evaluation hardware is still far from a light edge deployment.
Evidence ledger
The strongest claims in the brief, along with the confidence and citation depth behind them.
TaskGround improves task success even for a frontier model, indicating that the pipeline decomposition contributes value beyond model scale alone.
The method substantially improves compact open-weight model performance and narrows the gap with frontier direct prompting in the reported benchmark.
Grounding the scene before reasoning sharply reduces input-token cost versus serializing the complete household scene.
The evaluation is not yet evidence of robust real-home deployment because it omits several real-world sources of failure.
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