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Research map / Probabilistic and scientific modeling / Molecules and proteins

Flow matching generative modeling

Develops flow matching theory and variants, including equivariant and spectral versions.

97 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Molecules and proteins, keyword cluster “flow · trajectory · matching”

State History Benchmarks Tried Checklist Preprints Who Export

Where it stands

Few recent reports None of the tracked benchmarks has a report from 2025–26.

16 papers in 2023–24 and 64 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 3.3% to 6.9%.

No longer reported by papers here: Sintel Clean test (EPE, last in 2024); Sintel Final test (EPE, last in 2024).

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 2162017: 5172018: 1182019: 5192020: 2202021: 1212022: 1222023: 2232024: 14242025: 16252026: 4826

Most cited paper of each year

Earliest papers here

Benchmark records

The best number each year that beat every earlier one, among papers of this approach reporting their own method. Numbers are extracted by AI from each paper's text; check the paper before citing.

Sintel Clean test · EPE (lower is better)

4 papers, 2017–2024 · not reported since 2024

2017: 6.642019: 3.842024: 1.17
20172024

Sintel Final test · EPE (lower is better)

4 papers, 2017–2024 · not reported since 2024

2017: 8.362019: 4.582024: 2.21
20172024

What has been tried

45 papers here report 466 measured changes (a component added, removed or swapped, compared with a baseline): helped 69%, no clear effect 5%, hurt 24%, mixed 2%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Use Riemannian flow matching probability paths instead of Euclidean pathsttbar + 0j test set · NLL-30.11 ± 0.98 -> -32.65 ± 0.01HelpedLorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics (NeurIPS 2024 · 14 citations)
Add the proposed lattice base distribution and unconstrained angle transformationMP-20 · Match Rate (%)43.21 ± 0.47 -> 56.82 ± 0.42HelpedFlowMM: Generating Materials with Riemannian Flow Matching (ICML 2024 · 9 citations)
Set Gaussian radius r to 3 rather than 1, 2, or 4FCDN and VBOF (Fuji2) · EPEFCDN: 1.27, 1.17, 1.08, 1.21; VBOF(Fuji2): 14.22, 13.99, 13.94, 14.15HelpedCEDFlow: Latent Contour Enhancement for Dark Optical Flow Estimation (AAAI 2024 · 6 citations)
replace the deep decoder with an inner-product decoderBeijing · RMSE39.437 -> 45.425HurtExplainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder (AAAI 2024 · 5 citations)
Add perturbation conditionBBBC021 · FID_o45.0 -> 18.7HelpedCellFlux: Simulating Cellular Morphology Changes via Flow Matching (ICML 2025 · 2 citations)
Replace the positional interpolant with linear SDE with γMP-20 · Novelty rate (%)73.31; 66.45; 55.21; 76.25MixedOpen Materials Generation with Stochastic Interpolants (ICML 2025 · 2 citations)
replace max(0,x) resource preference loss with absolute-distance L1 lossSintel-Clean, Sintel-Final, KITTI · EPE1.56 vs 1.73; 2.74 vs 2.97; 5.52 vs 5.84HurtContext-Aware Iteration Policy Network for Efficient Optical Flow Estimation (AAAI 2024 · 2 citations)
add Gaussian mixture pathsAlanine dipeptide (exact) · MinMax Energy-14.67 -> -15.54; -15.95HelpedDoob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling (NeurIPS 2024 · 2 citations)
apply DPO to the VP pathCDR-H3 · Cα-w RMSD5.01 -> 4.42Helped3D Structure Prediction of Atomic Systems with Flow-based Direct Preference Optimization (NeurIPS 2024 · 1 citation)
Add the improved architecture to retrained GMSFF3D_o-all · EPE_3D0.039 -> 0.037HelpedDiffSF: Diffusion Models for Scene Flow Estimation (NeurIPS 2024 · 1 citation)
Add trajectory guidanceOC22 IS2RS validation · ADwT58.54 -> 59.39HelpedBridging Geometric States via Geometric Diffusion Bridge (NeurIPS 2024 · 1 citation)
Remove equivariance enforcementMolecular generation tasks · Atom and molecule stabilityHurtControlled Generation with Equivariant Variational Flow Matching (ICML 2025 · 0 citations)

What reviewers will expect

Datasets and baselines named in the 30 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • perov-5 · 2 of 30 papers
  • MP-20 · 2 of 30 papers
  • MPTS-52 · 2 of 30 papers
  • Alex-MP-20 · 2 of 30 papers

Baselines

  • FlowMM · 3 of 30 papers
  • MIOFlow · 2 of 30 papers
  • DiffCSP · 2 of 30 papers
  • CDVAE · 2 of 30 papers
  • OMatG · 2 of 30 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 80 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 37 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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