ICML 2026｜机制不是开关，而是一条轨迹：TRACE 如何捕捉系统的连续变化
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TL;DR - TRACE models evolving causal mechanisms as continuous mixtures of atomic mechanisms rather than discrete switches, enabling recovery of how a system changes over time. The ICML 2026 paper provides identifiability guarantees and strong results on synthetic, vehicle-turning, and gait-transition data.
- Uses a mixture-of-experts architecture to learn atomic mechanisms from labeled pure-domain data, then estimates continuous mixture trajectories via least-squares projection.
- Establishes latent-variable identifiability up to permutation and component-wise transformations, plus finite-sample error bounds for trajectory recovery.
- Achieves trajectory correlations of 0.94 ± 0.05 on synthetic data, 0.960 on UAVDT vehicle turns, and 0.856 ± 0.043 on walking-to-running sequences.
- Assumes known mechanism count and labeled pure-mechanism training data; recovery becomes difficult when mechanism bases are nearly collinear.
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ICML 2026｜机制不是开关，而是一条轨迹：TRACE 如何捕捉系统的连续变化
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Hugging Face upvotes 8
TL;DR - TRACE models evolving causal mechanisms as continuous mixtures of atomic mechanisms rather than discrete switches, enabling recovery of how a system changes over time. The ICML 2026 paper provides identifiability guarantees and strong results on synthetic, vehicle-turning, and gait-transition data.
- Uses a mixture-of-experts architecture to learn atomic mechanisms from labeled pure-domain data, then estimates continuous mixture trajectories via least-squares projection.
- Establishes latent-variable identifiability up to permutation and component-wise transformations, plus finite-sample error bounds for trajectory recovery.
- Achieves trajectory correlations of 0.94 ± 0.05 on synthetic data, 0.960 on UAVDT vehicle turns, and 0.856 ± 0.043 on walking-to-running sequences.
- Assumes known mechanism count and labeled pure-mechanism training data; recovery becomes difficult when mechanism bases are nearly collinear.