This flow model learns both what to generate and how large it should be
KevinKaichuang · x · 2026-07-28
Expanding Generative Flows formalize generation where output size is learnable
The paper proposes Expanding Generative Flows (EFlows) and Expanding Flow Maps (EFMs) for generating data whose dimensionality or sequence length can increase during generation.
What the method does
- Starts from an expanding interpolant that grows the state with conditional noise.
- Learns an expand operator to add coordinates or tokens.
- Learns a transport map to denoise and advance the expanded state.
- Recovers standard fixed-size flow models as a special case when expansion is the identity.
Why it matters
- Handles continuous and discrete settings.
- Extends to variable-size graph generation and variable-length sequence generation.
- Treats output size as a first-class controllable degree of freedom.
The paper positions this as a principled framework for tasks where the model should decide not just what to generate, but how big the output should be.
Related event: New Flow Map Framework Enables Dynamic Output Expansion During Inference(3 posts)→
More from Research
- Tsinghua professor's ApexIntelligence raises $50M to build self-evolving foundation models — DeryaTR_ · 2026-10-16
- Anders Sandberg on fluid computation: error budgets tighten quadratically over time — anderssandberg · 2026-10-16
- How the fluid computation works: force field advects particle to 0/1 boxes — anderssandberg · 2026-10-16
- FlashAttention creator Tri Dao joins Radical Numerics as scientific advisor — exnx · 2026-10-16
- SAIR Launches Open Math OS, Offering Free Compute and 100+ Tools for Community-Owned Math AI — Thom_Wolf · 2026-10-16
- JapanFold Claims One Tenstorrent Server Beats an Nvidia DGX B200 on Bio Models at a Third of the Price — DavidBennett__ · 2026-10-16