Pith Number

pith:C7BAFTVD

pith:2026:C7BAFTVDEVQCA3MT62FQPTP5IU

not attested not anchored not stored refs pending

Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs

Huang Huang, Jiajun Wu, Leonidas Guibas, Li Fei-Fei, Manling Li, Yejin Choi, Yining Hong

Embodied LLMs improve long-horizon task performance by reflecting on failures before and after each execution at test time.

arxiv:2602.21198 v3 · 2026-02-24 · cs.LG · cs.AI · cs.CL · cs.CV · cs.RO

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\pithnumber{C7BAFTVDEVQCA3MT62FQPTP5IU}

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2 Internet Archive

3 Author claim open · sign in to claim

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

Experiments on our newly-designed Long-Horizon Household benchmark and MuJoCo Cupboard Fitting benchmark show significant gains over baseline models, with zero-shot generalization to photorealistic HM3D environments and real-robot experiments on a Franka Panda arm. Ablations confirm that reflection-in-action and reflection-on-action are mutually dependent, and that retrospective reflection achieves better credit assignment than step-wise external feedback at lower computational overhead.

C2weakest assumption

That internal model reflections and external feedback after execution can reliably identify the causes of failures and that test-time training updates can improve the policy without instability or loss of prior capabilities.

C3one line summary

Reflective Test-Time Planning combines pre-execution internal reflection with post-execution model updates to improve embodied LLMs on household and manipulation tasks with better long-horizon credit assignment.

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures

Receipt and verification

First computed	2026-05-26T02:04:07.073593Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

17c202cea32560206d93f68b07cdfd451b4587b7d8a907dad826b94c387dbf52

Aliases

arxiv: 2602.21198 · arxiv_version: 2602.21198v3 · doi: 10.48550/arxiv.2602.21198 · pith_short_12: C7BAFTVDEVQC · pith_short_16: C7BAFTVDEVQCA3MT · pith_short_8: C7BAFTVD

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/C7BAFTVDEVQCA3MT62FQPTP5IU \
  | 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: 17c202cea32560206d93f68b07cdfd451b4587b7d8a907dad826b94c387dbf52

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "25f2e722250a220f2d4d04f790431684a0c0f84d49d3875744f254df9b6129d6",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CL",
      "cs.CV",
      "cs.RO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-02-24T18:55:18Z",
    "title_canon_sha256": "5c27eaf0d8482cbb5b9a33d01ba2228571b72860dcd92b60b31d246ffc847fdb"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.21198",
    "kind": "arxiv",
    "version": 3
  }
}