pith:HEKUDQN6
LASER: Learning Active Sensing for Continuum Field Reconstruction
A reinforcement learning policy trained inside a latent model of physical dynamics can adapt sensor movements to reconstruct continuum fields from sparse measurements more accurately than fixed layouts.
arxiv:2604.19355 v2 · 2026-04-21 · cs.LG · cs.AI · cs.CE
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\pithnumber{HEKUDQN65QXLT3T6GN4Y3DAYDI}
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Record completeness
Claims
LASER consistently outperforms static and offline-optimized strategies, achieving high-fidelity reconstruction under sparsity across diverse continuum fields.
That the learned continuum field latent world model provides sufficiently accurate intrinsic reward feedback and 'what-if' predictions to train a policy that transfers to real sensing scenarios.
LASER trains a reinforcement learning policy inside a latent dynamics model to choose sensor placements that improve reconstruction of continuum fields under sparsity.
Receipt and verification
| First computed | 2026-05-28T01:04:40.554379Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
391541c1beec2eb9ee7e33798d8c181a1bef8af8db475ef4ee7a45eeddf18ace
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/HEKUDQN65QXLT3T6GN4Y3DAYDI \
| 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: 391541c1beec2eb9ee7e33798d8c181a1bef8af8db475ef4ee7a45eeddf18ace
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
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"submitted_at": "2026-04-21T11:36:09Z",
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