Pith Number

pith:H6KWZFSQ

pith:2026:H6KWZFSQQNZWIEOI2FR6HCVEZQ

not attested not anchored not stored refs resolved

When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution

Bowen Pang, Jing Liu, Longteng Guo, Ruyi Ji, Xingjian He, Yanghong Mei, Zilin Zhu, Zongxun Zhang

HoloMind agent with DAG planner and dual memories raises long-horizon household task success while cutting dependence on model size.

arxiv:2605.14504 v1 · 2026-05-14 · cs.AI

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\usepackage{pith}
\pithnumber{H6KWZFSQQNZWIEOI2FR6HCVEZQ}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

HoloMind substantially improves long-horizon performance while reducing reliance on model scale. Even top models achieve only 59% goal completion and 16% full-task success.

C2weakest assumption

Abstracting away embodiment-specific low-level control isolates high-level cognitive capabilities such as instruction understanding, dependency management, memory maintenance, and adaptive planning without losing essential task realism.

C3one line summary

LongAct benchmark reveals top VLMs reach only 59% goal completion and 16% full success on long-horizon household tasks, while HoloMind agent improves results via DAG planner, multimodal spatial memory, episodic memory, and global critic.

References

43 extracted · 43 resolved · 12 Pith anchors

[1] Qwen3 Technical Report 2025 · arXiv:2505.09388

[2] GPT-4 Technical Report 2023 · arXiv:2303.08774

[3] H. Liu, C. Li, Q. Wu, and Y . J. Lee. Visual instruction tuning.Advances in Neural Information Processing Systems, 36:1–19, 2023 2023

[4] Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond 2023 · arXiv:2308.12966

[5] Gemini: A Family of Highly Capable Multimodal Models 2023 · arXiv:2312.11805

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:06.276874Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

3f956c965083736411c8d163e38aa4cc0ff6e4cd22a55bf56445526cbae39073

Aliases

arxiv: 2605.14504 · arxiv_version: 2605.14504v1 · doi: 10.48550/arxiv.2605.14504 · pith_short_12: H6KWZFSQQNZW · pith_short_16: H6KWZFSQQNZWIEOI · pith_short_8: H6KWZFSQ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/H6KWZFSQQNZWIEOI2FR6HCVEZQ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 3f956c965083736411c8d163e38aa4cc0ff6e4cd22a55bf56445526cbae39073

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "db8c4a3e1eb299b0b1ab53c841f726d0d7d7952d4921139f945592bfb70327db",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-14T07:47:53Z",
    "title_canon_sha256": "252a621d8f7b57ae821328d3607fa3fa1e1d377ae7f8fc272c71550255cd8e83"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14504",
    "kind": "arxiv",
    "version": 1
  }
}