FLARE++: Low-rank attention with dynamic attention routing
Full self-attention is a strong token mixer for PDE surrogates on irregular domains, but its quadratic cost limits its use on large problems. Latent-space attention methods such as PerceiverIO, Transolver, and FLARE (Fast Low-rank Attention Routing Engine) avoid that cost by routing attention among $N$ tokens through $M\ll N$ learned latents. They compress by dot-product matching of the input tokens against $M$ learned query tokens or projection weights: once trained, the same learned templates serve every input. We remove this restriction with FLARE++, a low-rank attention architecture with input-conditioned routing queries. FLARE++ uses FLARE's own encoder to map the $N$ input tokens to...
| Authors | Vedant Puri, Yongjie Jessica Zhang, Levent Burak Kara |
|---|---|
| Primary category | cs.LG |
| Published | 12 August 2026 |
| Last updated at source | 26 September 2026 |
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