Pith Number

pith:T6Q27LRI

pith:2023:T6Q27LRIBE3XCCEBEJFI74RTT4

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Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents

Anji Liu, Guanzhou Chen, Shaofei Cai, Xiaojian Ma, Yitao Liang, Zihao Wang

DEPS combines LLM-based interactive planning with a trainable goal selector to create a zero-shot multi-task agent that completes 70+ Minecraft tasks and nearly doubles prior performance.

arxiv:2302.01560 v3 · 2023-02-03 · cs.AI

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4 Citations open

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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

Our experiments mark the milestone of the first zero-shot multi-task agent that can robustly accomplish 70+ Minecraft tasks and nearly double the overall performances.

C2weakest assumption

That large language models can reliably produce accurate descriptions and self-explanations of plan failures without hallucination, and that the trained goal selector generalizes to estimate completion steps across diverse tasks.

C3one line summary

DEPS combines LLM-based interactive planning with a trainable goal selector to create a zero-shot multi-task agent that completes 70+ Minecraft tasks and nearly doubles prior performance.

References

71 extracted · 71 resolved · 18 Pith anchors

[1] Flamingo: a Visual Language Model for Few-Shot Learning 2022 · arXiv:2204.14198

[2] P.-L. Bacon, J. Harb, and D. Precup. The option-critic architecture. In Proceedings of the AAAI conference on artificial intelligence, 2017. 1 2017

[3] Video pretraining (vpt): Learning to act by watching unlabeled online videos 2022

[4] A. Brohan, Y . Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian, et al. Do as i can, not as i say: Grounding language in robotic affordances. In 6th Annual Confe 2022

[5] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. Language models are few-shot learners. Advances in neural information process 1901

Cited by

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Receipt and verification

First computed	2026-05-17T23:38:49.267167Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9fa1afae280937710881224a8ff2339f02ae0972ba6e947650ad90e9c71f5898

Aliases

arxiv: 2302.01560 · arxiv_version: 2302.01560v3 · doi: 10.48550/arxiv.2302.01560 · pith_short_12: T6Q27LRIBE3X · pith_short_16: T6Q27LRIBE3XCCEB · pith_short_8: T6Q27LRI

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4 \
  | 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: 9fa1afae280937710881224a8ff2339f02ae0972ba6e947650ad90e9c71f5898

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2a168690c78dfe07b88973493558119fd2f0865dbabc19777c7e1122cd4b6b1b",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2023-02-03T06:06:27Z",
    "title_canon_sha256": "e3fd620c9334ea0d3878e7ba3848de41d55fa299f2e33aaf50240dc0098199f3"
  },
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  "source": {
    "id": "2302.01560",
    "kind": "arxiv",
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}