pith:5WC5NBKC
Analytic Bridge Diffusions for Controlled Path Generation
Linear-quadratic-Gaussian control with Gaussian-mixture boundaries supplies closed-form scores, marginals, and protocols for bridge diffusions.
arxiv:2605.02961 v2 · 2026-05-03 · cs.LG · cond-mat.stat-mech · cs.AI · cs.SY · eess.SY · math.OC
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\pithnumber{5WC5NBKCCD66TVMEBJSMZSQAJJ}
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Claims
In LQ-GM-PID the score, intermediate marginals, and protocol gradients are available in closed form without inner stochastic simulation loops and without neural networks in the optimization loop.
The assumption that linear dynamics, Gaussian noise, quadratic costs, and Gaussian-mixture initial/terminal laws are together sufficient to keep the Riccati-based solution closed-form when the terminal target is a prescribed density rather than a point.
LQ-GM-PID recasts LQG control as a path-integral diffusion problem with Gaussian-mixture terminals to deliver closed-form bridge diffusion quantities for controlled path generation.
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| First computed | 2026-08-13T00:21:22.738784Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ed85d6854210fde9d5840a64ccca004a652adccafbaf8e38d043ed6bce9ce32e
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/5WC5NBKCCD66TVMEBJSMZSQAJJ \
| 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: ed85d6854210fde9d5840a64ccca004a652adccafbaf8e38d043ed6bce9ce32e
Canonical record JSON
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