ChatSVA achieves 96.12% functional pass rate and 82.5% coverage in SVA generation on 24 RTL designs, delivering 33 percentage point gains and 11x better coverage than prior state-of-the-art.
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PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.
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ChatSVA: Bridging SVA Generation for Hardware Verification via Task-Specific LLMs
ChatSVA achieves 96.12% functional pass rate and 82.5% coverage in SVA generation on 24 RTL designs, delivering 33 percentage point gains and 11x better coverage than prior state-of-the-art.
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Physically Interpretable World Models via Weakly Supervised Representation Learning
PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.