Pith Number

pith:P7QGPVCK

pith:2026:P7QGPVCKM4XZEWUGSVAA73R6ND

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Action-Inspired Generative Models

Debnath Pal, Eshwar R. A.

A lightweight learned scalar potential reweights bridge samples during training to penalize uninformative paths and lift generative quality.

arxiv:2605.14631 v1 · 2026-05-14 · cs.LG · cs.AI · cs.CV

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications 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

selectively penalising uninformative transport paths through the learned potential yields consistent improvements in generation quality across fidelity and coverage metrics.

C2weakest assumption

that the learned scalar potential V_φ can reliably distinguish structurally coherent trajectories from degenerate ones online during training without introducing instability or adversarial dynamics between the networks.

C3one line summary

AGMs use a lightweight learned potential V_phi with stop-gradient to selectively weight informative bridge samples in generative model training, yielding better fidelity and coverage.

References

13 extracted · 13 resolved · 0 Pith anchors

[1] Feynman and Albert R 1965

[2] Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851 2020

[3] Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole 2021

[4] Denoising diffusion implicit models 2021

[5] Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. InInternational Conference on Learning Representations, 2023 2023

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:03.970811Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7fe067d44a672f925a8695400fee3e68d16b4dfc5fd7343b585421be5d8a4319

Aliases

arxiv: 2605.14631 · arxiv_version: 2605.14631v1 · doi: 10.48550/arxiv.2605.14631 · pith_short_12: P7QGPVCKM4XZ · pith_short_16: P7QGPVCKM4XZEWUG · pith_short_8: P7QGPVCK

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/P7QGPVCKM4XZEWUGSVAA73R6ND \
  | 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: 7fe067d44a672f925a8695400fee3e68d16b4dfc5fd7343b585421be5d8a4319

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "235b19173813cb2ea6e84cdc9f64b204838db2137c949b3c33d41902e8f99d2a",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T09:43:32Z",
    "title_canon_sha256": "e211e0398ded964dfe019f994ba2549ab933a57ad6491ec3b0dd3e2ac7a4a5f2"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14631",
    "kind": "arxiv",
    "version": 1
  }
}