FMRG reformulates guidance as deterministic optimal control, deriving a single-trajectory method using the flow map that matches or exceeds baselines on reward-guided generation and inverse problems with 3 NFEs at text-to-image scale.
Albergo, Carles Domingo-Enrich, Nicholas M
7 Pith papers cite this work. Polarity classification is still indexing.
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2026 7representative citing papers
Flow map denoisers use a lookahead parameter t to span the distortion-perception frontier, proven optimal for Gaussian targets and effective for natural images and inverse problems.
ScoRe-Flow achieves decoupled mean-variance control in stochastic flow matching by deriving a closed-form score for drift modulation plus learned variance, yielding faster RL convergence and higher success rates on locomotion and manipulation benchmarks.
DeCAF distills all-atom cofolding diffusion models into few-step flow maps, showing improved or matched accuracy on protein-ligand tasks with 5x fewer inference steps.
SURGE is an unbiased particle filter that fuses diffusion-model simulations with noisy observations via sequential Monte Carlo reweighting over diffusion trajectories.
URGE performs unbiased inference-time scaling for diffusion models by attaching multiplicative path weights from Girsanov estimation and resampling trajectories, with a proven equivalence to prior particle-wise SMC schemes.
Flow matching velocity fields are governed solely by conditional endpoint means, so changing the reference-set mean steers generation without parameter updates.
citing papers explorer
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How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance
FMRG reformulates guidance as deterministic optimal control, deriving a single-trajectory method using the flow map that matches or exceeds baselines on reward-guided generation and inverse problems with 3 NFEs at text-to-image scale.
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Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems
Flow map denoisers use a lookahead parameter t to span the distortion-perception frontier, proven optimal for Gaussian targets and effective for natural images and inverse problems.
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ScoRe-Flow: Complete Distributional Control via Score-Based Reinforcement Learning for Flow Matching
ScoRe-Flow achieves decoupled mean-variance control in stochastic flow matching by deriving a closed-form score for drift modulation plus learned variance, yielding faster RL convergence and higher success rates on locomotion and manipulation benchmarks.
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Few-step Cofolding with All-Atom Flow Maps
DeCAF distills all-atom cofolding diffusion models into few-step flow maps, showing improved or matched accuracy on protein-ligand tasks with 5x fewer inference steps.
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SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate
SURGE is an unbiased particle filter that fuses diffusion-model simulations with noisy observations via sequential Monte Carlo reweighting over diffusion trajectories.
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Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
URGE performs unbiased inference-time scaling for diffusion models by attaching multiplicative path weights from Girsanov estimation and resampling trajectories, with a proven equivalence to prior particle-wise SMC schemes.
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Follow the Mean: Reference-Guided Flow Matching
Flow matching velocity fields are governed solely by conditional endpoint means, so changing the reference-set mean steers generation without parameter updates.