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pith:2025:Z54RYTFEABGFK6WD2F6Z7W7JC5
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From Next Token Prediction to (STRIPS) World Models

Carlos N\'u\~nez-Molina, Hector Geffner, Vicen\c{c} G\'omez

Next-token prediction on action traces yields STRIPS world models accurate enough for planning on unseen states and goals.

arxiv:2509.13389 v7 · 2025-09-16 · cs.AI

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3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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Claims

C1strongest claim

Both the STRIPS Transformer and a standard transformer with stick-breaking attention can be used to produce models that support planning with off-the-shelf STRIPS planners over exponentially many unseen initial states and goals.

C2weakest assumption

The learned next-token models are sufficiently accurate and complete to serve as drop-in STRIPS action models for arbitrary unseen states and goals, with correctness evaluated exactly in the symbolic setting (abstract, evaluation section implied by results on generalization and planning performance).

C3one line summary

Transformers trained via next-token prediction on action traces can learn STRIPS action models that support planning over exponentially many unseen initial states and goals.

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1 paper in Pith

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First computed 2026-05-26T02:05:02.079507Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

cf791c4ca4004c557ac3d17d9fdbe917643c867d8fed2eb7b2e50905b5cd78cc

Aliases

arxiv: 2509.13389 · arxiv_version: 2509.13389v7 · doi: 10.48550/arxiv.2509.13389 · pith_short_12: Z54RYTFEABGF · pith_short_16: Z54RYTFEABGFK6WD · pith_short_8: Z54RYTFE
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5 \
  | 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: cf791c4ca4004c557ac3d17d9fdbe917643c867d8fed2eb7b2e50905b5cd78cc
Canonical record JSON
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2025-09-16T14:03:58Z",
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