The authors built HamiFormer around two interacting experts: a diffusion model that denoises a whole trajectory window, and a residual-corrected Hamiltonian model that propagates states according to structured physical dynamics.
Dual-expert diffusion improves long-horizon predictions of collisions and smooth dynamics
HamiFormer combines window-wide denoising with Hamiltonian propagation to reduce accumulated trajectory errors over 192 simulation steps.
Top University
Haoxiang Huang · Xiang Liu · Shuwei Wang · Jingheng Ma · Sen Cui
University of Science and Technology of China · Tsinghua University · Chinese Academy of Sciences
Research Digest··2 min read
Huang et al.
Why this paper
From Chinese Academy of Sciences and 2 others · Released code
In one line
HamiFormer reduces phase-space prediction error by up to 26.3% using a dual-expert diffusion field with affine symplectic maps.
What it released
Code
What we could check
- ✓Code link in the paper (github.com)
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks (2 benchmarks)
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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