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
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.
AI Impact Assessments
(1 model)Scientific Impact Assessment: TaskGround
1. Core Contribution
TaskGround formalizes full-scene household reasoning — the problem of producing executable skill-level action sequences from a complete household scene graph and a naturally situated household request, without being given task-relevant objects, goals, or process constraints. The paper identifies that prior work typically assumes some degree of task specification is already available (relevant objects, goals, or constraints), whereas real deployments require agents to *infer* what to do from cluttered, complete scenes.
The proposed Ground–Infer–Execute framework decomposes this problem into three stages: (1) grounding the full scene into a compact task-relevant slice, (2) inferring and completing executable task structure (goal atoms and ordering constraints), and (3) compiling the structure into skill-level actions via a deterministic executor. Critically, this is training-free and model-agnostic — it wraps around any LLM without fine-tuning.
The companion benchmark, FullHome (400 human-validated tasks across VirtualHome and BEHAVIOR environments), operationalizes the full-scene information condition and distinguishes goal-oriented from process-constrained tasks.
2. Methodological Rigor
The experimental design is reasonably thorough:
However, several methodological concerns exist:
3. Potential Impact
Practical relevance: The paper addresses a genuine deployment gap — household robots will receive ambiguous situated requests in cluttered scenes. The finding that structured decomposition enables a 9B-parameter model to match GPT-5 under naive prompting while using 18× fewer tokens has clear practical implications for edge deployment, cost reduction, and privacy preservation.
Benchmark contribution: FullHome fills a niche by testing the *full-scene information condition* rather than assuming pre-curated inputs. If adopted, it could redirect evaluation practices in embodied AI toward more realistic information conditions.
Conceptual insight: The paper's most impactful finding may be identifying executable task-structure inference as the central bottleneck — not action generation, not scene understanding per se, but the intermediate step of recovering implicit goals and ordering constraints. This reframes the problem in a way that could guide future research.
Limitations on impact: The framework is limited to text-based scene graphs (no vision), predefined skill vocabularies, and simulator environments. The gap to real-world deployment remains substantial. The training-free, model-agnostic framing is appealing but may ultimately be outperformed by end-to-end trained approaches as models improve.
4. Timeliness & Relevance
The paper is highly timely. It addresses the intersection of several active research directions: LLM-based embodied agents, efficient inference for edge deployment, and privacy-preserving AI. The emphasis on making compact open-weight models competitive with frontier models resonates with current trends toward local/on-device AI. The paper's 2026 model references (GPT-5, Qwen3.5, DeepSeek-V4) indicate it engages with the latest model landscape.
The privacy motivation (avoiding sending complete household scenes to cloud APIs) is increasingly relevant but is somewhat superficially treated — the paper doesn't formally analyze privacy properties.
5. Strengths & Limitations
Key Strengths:
Notable Limitations:
Additional Observations
The paper is well-written with clear figures and a logical flow. The qualitative failure analysis (Appendix F) is informative. The honest discussion of remaining bottlenecks (Section 7) is appreciated. The broader impacts section thoughtfully acknowledges privacy and safety concerns without overclaiming.
The work would benefit from comparison against retrieval-augmented generation (RAG) baselines beyond the simple Retrieve+Act variant, and from testing on scenes with varying complexity levels to understand scaling behavior.
Generated May 19, 2026
Comparison History (18)
Paper 2 addresses a critical bottleneck (KV cache memory) in tree-based LLM reasoning, which is highly relevant to the rapidly growing field of test-time compute and search-based inference (e.g., OpenAI's o1). Its 4x memory reduction enables deeper search and better performance across broad NLP tasks. While Paper 1 is a strong contribution to embodied AI, Paper 2's system-level optimization for LLMs has significantly broader immediate applicability and potential for widespread adoption across the AI community.
Paper 1 offers broader scientific impact because its methodology—improving LLM efficiency and multi-domain specialization via modular skillpacks—applies to the entire field of natural language processing and general AI deployment. While Paper 2 presents a strong embodied AI framework and dataset for household robotics, its impact is constrained to a specific niche. Paper 1 tackles fundamental memory and inference bottlenecks in LLMs, allowing a 9B model to outperform a 32B model, which has widespread, immediate implications for resource-constrained edge computing and scalable AI applications.
Paper 2 (ArborKV) targets a fundamental systems bottleneck for a broad class of tree-based LLM inference methods, offering a generally applicable KV-cache management strategy with sizable memory savings (~4x) and minimal accuracy loss—high leverage for scaling reasoning across models and deployments. Its impact spans LLM inference, serving infrastructure, and algorithmic search, and is timely given the shift toward ToT-like methods. Paper 1 is strong and application-relevant (household agents) with a valuable benchmark, but its scope is more domain-specific and may depend more on task/scene assumptions.
MOSS introduces a fundamentally novel paradigm—source-level self-rewriting of autonomous agent systems—that addresses a previously unrecognized gap (structural failures unreachable from text-layer evolution). This is a more general and theoretically significant contribution with broader implications across all agentic AI systems. While TaskGround is a solid engineering contribution for household robotics with strong empirical results, its scope is narrower (household task grounding) and its techniques (structured prompting pipelines) are more incremental. MOSS's Turing-complete self-evolution framework opens new research directions in self-improving AI systems with wider cross-field impact.
Paper 2 likely has higher scientific impact: it introduces a new strategic-classification setting that relaxes the core rationality assumption and offers a principled framework grounded in prospect theory, potentially reshaping theory and practice in ML, economics, and policy-facing domains (credit, hiring, admissions, compliance). Its novelty is conceptual and broadly applicable across decision-making systems subject to gaming, with clear timeliness given growing concerns about real-world behavioral validity and robustness. Paper 1 is strong and timely for embodied AI, but its contributions are more domain-specific and evaluation-centric, with impact concentrated in household/robotic planning pipelines.
Paper 2 is likely to have higher scientific impact due to its broader real-world applicability (household/robotic assistants under privacy and compute constraints), introduction of a new problem formalization (full-scene household reasoning), and release of a sizable human-validated benchmark (FullHome) that can drive follow-on research. The training-free, model-agnostic Ground-Infer-Execute framework is timely and deployable, and its gains across proprietary and open models suggest wide adoption. Paper 1 is novel in multi-objective prompt/skill optimization, but its impact is narrower to prompt/skill engineering and platform-specific constraints.
Paper 1 likely has higher scientific impact due to its timeliness (enterprise agentic AI security), demonstrated large-scale real-world deployment, and rigorous empirical validation (production metrics plus two benchmarks). Its contributions (telemetry/sensing, red-teaming workflow, scalable two-tier detection) are broadly applicable across enterprises adopting agent protocols, potentially influencing security tooling and standards. Paper 2 is novel and valuable for embodied/household agents, but its impact may be narrower (domestic robotics/assistants) and currently more evaluation-driven than deployment-proven.
Paper 2 tackles high-stakes medical diagnostics where interpretability is crucial. By introducing Structured Set Policy Optimization (SSPO) to optimize clinical reasoning without manual annotations, it offers higher methodological novelty than Paper 1's training-free pipeline. Its potential to align MLLMs with human clinical thinking provides a highly impactful framework for the broader healthcare AI field.
Paper 1 likely has higher impact due to timeliness and broader cross-field relevance: it targets LLM-driven household/robotic agents under real deployment constraints (privacy, local compute, long-context limits) and contributes a new evaluation suite (FullHome) plus a model-agnostic framework that substantially improves compact open-weight models. Its practical implications span robotics, embodied AI, HRI, and efficient LLM prompting. Paper 2 is methodologically strong and advances generalized planning with scalable IW policies, but its impact is more specialized to classical planning/IPC-style benchmarks with narrower immediate real-world adoption.
Paper 2 addresses the urgent and widespread issue of multimodal LLM safety. By formalizing 'Safety Geometry Collapse' and proposing a training-free intervention (ReGap), it provides deep theoretical insights and immediate practical solutions for AI alignment. While Paper 1 offers strong advances for embodied AI and robotics, Paper 2 has a broader, more immediate impact across the rapidly expanding field of foundation models, directly tackling critical safety vulnerabilities that affect millions of current AI deployments.