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arxiv: 2605.18109 · v1 · pith:5UMND3J7new · submitted 2026-05-18 · 💻 cs.AI · cs.CV· cs.RO

TaskGround: Structured Executable Task Inference for Full-Scene Household Reasoning

ZhiYuan Feng , Yu Deng , Ruichuan An , Zhenhua Liu , Qixiu Li , Keming Wu , Zhiying Du , Weijie Wang
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Haoxiao Wang Shuang Chen Sicheng Xu Yaobo Liang Jiaolong Yang Baining Guo
This is my paper

Pith reviewed 2026-05-20 10:50 UTC · model grok-4.3

classification 💻 cs.AI cs.CVcs.RO
keywords household roboticstask inferencescene groundingexecutable planningfull-scene reasoningopen-weight modelstoken efficiency
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The pith

Grounding complete household scenes into compact task-relevant slices lets agents recover executable task structures accurately, raising success rates and slashing token costs even for small models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Household agents often receive vague requests inside cluttered full scenes rather than clean task lists, so they must spot relevant objects, recover hidden conditions, and work out ordering constraints before they can act. Direct prompting with every detail in the scene wastes tokens and introduces errors because most of the information is irrelevant to the current request. TaskGround solves this without any extra training by first extracting a small focused slice of the scene that matches the request, then using that slice to build a structured plan of what must be done and in what order, and finally turning the plan into concrete low-level actions the agent can execute. On a new benchmark of 400 human-checked household tasks, this produces large gains in success for both closed and open models and lets a 9B model match the performance of a much larger one while cutting input tokens by as much as 18 times. The results point to executable task-structure inference, rather than raw perception, as the main remaining obstacle for practical home robots.

Core claim

TaskGround is a training-free, model-agnostic Ground-Infer-Execute framework that first grounds a complete household scene plus a situated request into a compact task-relevant scene slice, then infers an executable task structure that includes ordering constraints, and finally compiles that structure into a grounded sequence of skill-level actions. Evaluated on the FullHome suite of 400 human-validated tasks across diverse home environments and both goal-oriented and process-constrained requirements, the approach delivers large improvements in task success rates for proprietary and open-weight models alike and makes Qwen3.5-9B competitive with GPT-5 under direct complete-scene prompting at a

What carries the argument

The Ground-Infer-Execute pipeline that converts a full scene and request into a compact task-relevant slice before inferring executable task structure and ordering.

If this is right

  • Compact open-weight models become viable for on-device household agents that cannot afford long-context reasoning.
  • Success improves because the agent is no longer distracted by irrelevant objects when recovering task conditions and orderings.
  • Token costs drop enough to support repeated reasoning steps in long-horizon household activities.
  • Task-structure inference emerges as the identifiable central bottleneck once grounding removes scene clutter.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same slice-first strategy could reduce context overload in other embodied settings where scenes contain far more data than any single task needs.
  • Learned grounding modules might produce even tighter slices than the current training-free method and further improve small-model performance.
  • Recovered ordering constraints could transfer directly to multi-agent coordination inside shared living spaces.

Load-bearing premise

Compact task-relevant scene slices obtained by grounding preserve enough context to recover accurate task structure and ordering constraints without losing critical information present only in the full scene.

What would settle it

A collection of tasks where essential ordering or condition information sits only in scene elements the grounding step discards; if success rates fall sharply on those tasks compared with direct full-scene prompting, the central claim does not hold.

Figures

Figures reproduced from arXiv: 2605.18109 by Baining Guo, Haoxiao Wang, Jiaolong Yang, Keming Wu, Qixiu Li, Ruichuan An, Shuang Chen, Sicheng Xu, Weijie Wang, Yaobo Liang, Yu Deng, Zhenhua Liu, Zhiying Du, Zhiyuan Feng.

Figure 1
Figure 1. Figure 1: Overview of TaskGround for full-scene household reasoning. It grounds complete scenes, infers executable task structure, and compiles grounded skill-level actions, improving task success rates on our FullHome benchmark with up to 18× lower total input-token cost. Abstract In real home deployments, household agents must often operate from a complete household scene and a situated household request, rather t… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of TaskGround. Given a complete household scene and a situated household request, TaskGround follows a Ground–Infer–Execute decomposition. The orange module grounds the input into a compact task-relevant scene slice; the green module infers and completes executable task structure; the blue module compiles the completed structure into a grounded skill-level action sequence. The bottom row shows a r… view at source ↗
Figure 3
Figure 3. Figure 3: Framework ablation on the VirtualHome split of FullHome. Bars report [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes TaskGround, a training-free and model-agnostic Ground-Infer-Execute framework for full-scene household reasoning. Given a complete household scene and a situated household request, the method first grounds the scene into a compact task-relevant slice, then infers executable task structure (including conditions and ordering constraints), and finally compiles the structure into grounded skill-level action sequences. To support evaluation, the authors introduce FullHome, a human-validated suite of 400 tasks across diverse home environments covering both goal-oriented and process-constrained requirements. On this benchmark the framework is reported to deliver large gains in task success rate for both proprietary and open-weight models, notably bringing Qwen3.5-9B to parity with GPT-5 under direct complete-scene prompting while reducing total input tokens by up to 18×.

Significance. If the empirical claims hold under rigorous verification, the work would be significant for practical household robotics: it directly targets the context-overload and long-context limitations that hinder compact local models under privacy and compute constraints. The training-free, model-agnostic design and the explicit identification of executable task-structure inference as a central bottleneck are strengths. The new FullHome benchmark also provides a concrete testbed for future work on situated, full-scene reasoning.

major comments (2)
  1. [§4 and Abstract] §4 (Experiments) and Abstract: the headline claims of large success-rate margins and up to 18× token reduction rest on an evaluation whose protocol, baseline definitions, statistical significance tests, and error analysis are not described. Without these details the central empirical claim cannot be verified from the manuscript.
  2. [§3 and §4] §3 (Grounding) and §4: the framework asserts that compact task-relevant slices preserve every entity, spatial relation, and implicit ordering constraint required for accurate executable task inference. No ablation compares inference accuracy on identical models using the full scene versus the grounded slice, and no failure-case audit attributes errors to omitted context. This leaves the performance margins vulnerable to the possibility that they are artifacts of the particular FullHome task distribution rather than a general property of the Ground-Infer-Execute pipeline.
minor comments (2)
  1. [§3] Notation for the inferred task structure (conditions, ordering constraints, and skill-level actions) is introduced without a compact formal definition or example that would allow readers to reproduce the compilation step.
  2. [§4] The FullHome benchmark description would benefit from an explicit breakdown of task categories (goal-oriented vs. process-constrained) and environment scales to clarify the diversity claimed in the abstract.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and for highlighting areas where the experimental details and supporting analyses can be strengthened. We address each major comment below and will incorporate the suggested clarifications and additional studies in a revised manuscript.

read point-by-point responses

  1. Referee: [§4 and Abstract] §4 (Experiments) and Abstract: the headline claims of large success-rate margins and up to 18× token reduction rest on an evaluation whose protocol, baseline definitions, statistical significance tests, and error analysis are not described. Without these details the central empirical claim cannot be verified from the manuscript.

    Authors: We acknowledge that the current manuscript does not provide a sufficiently explicit description of the full evaluation protocol, baseline implementations, statistical testing procedures, or a systematic error analysis. In the revised version we will expand §4 with a dedicated subsection that details the task execution protocol, precise definitions of all baselines (including direct complete-scene prompting), success criteria, and the statistical tests used to assess significance (e.g., McNemar’s test for paired success-rate comparisons and bootstrap confidence intervals). We will also add a quantitative error analysis that categorizes failure modes across models. revision: yes

  2. Referee: [§3 and §4] §3 (Grounding) and §4: the framework asserts that compact task-relevant slices preserve every entity, spatial relation, and implicit ordering constraint required for accurate executable task inference. No ablation compares inference accuracy on identical models using the full scene versus the grounded slice, and no failure-case audit attributes errors to omitted context. This leaves the performance margins vulnerable to the possibility that they are artifacts of the particular FullHome task distribution rather than a general property of the Ground-Infer-Execute pipeline.

    Authors: We agree that an explicit ablation isolating the contribution of the grounding step to task-structure inference accuracy would strengthen the claims. Although the reported gains are consistent across multiple model families and sizes, this does not directly quantify whether the grounded slices retain all necessary constraints. In the revision we will add an ablation that measures the correctness of inferred conditions and ordering constraints on the same models when given the full scene versus the grounded slice. We will also include a qualitative failure-case audit that examines whether any observed errors can be traced to omitted context, leveraging the human-validated annotations already present in FullHome. revision: yes

Circularity Check

0 steps flagged

No circularity: training-free framework with empirical results on external benchmark

full rationale

The paper presents TaskGround as a training-free, model-agnostic Ground-Infer-Execute pipeline that grounds scenes into compact slices before inferring executable task structure. No mathematical derivations, equations, fitted parameters, or first-principles predictions are described that could reduce to inputs by construction. Performance claims rest on reported success rates on the newly introduced FullHome benchmark of 400 tasks, with comparisons across models. The central claims are externally falsifiable via the benchmark and do not rely on self-citation chains or ansatzes smuggled from prior work. This matches the default case of a self-contained empirical framework with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no free parameters, axioms, or invented entities are identifiable. The central claim rests on the unstated assumption that scene grounding loses no task-critical information.

pith-pipeline@v0.9.0 · 5880 in / 1050 out tokens · 29635 ms · 2026-05-20T10:50:00.284442+00:00 · methodology

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