Molin et al.
Spectral alignment lets users specify what world models preserve
The SpecWM framework improves planning success rates on visual control tasks by up to 28 percentage points over prior JEPA methods.
Top University
Holger Molin · William Peng · Marco Bagatella · Randall Balestriero
Stanford University · ETH Zürich · Brown University
Research Digest··2 min read
Thread:World Model Alignment
The authors introduce SpecWM, a Joint Embedding Predictive Architecture that trains an encoder to match a target similarity kernel between observations.
Why this paper
From Stanford University and 2 others
In one line
Spectral alignment of embedding geometry to a target similarity kernel improves world model planning performance.
What we could check
- ·No code link found
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- ·No dataset link found
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- ✓Limitations stated by the authors (2 noted)
- ✓Reports numbers on named benchmarks (3 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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