3 accepted papers
Generating long-horizon, all-atom molecular dynamics (MD) is difficult due to error accumulation in time-domain autoregressive models (causing drift) and fixed step-size constraints on temporal resolution. We propose **BioDynaSpec**, which reformulates protein dynamics as spatio-spectral generation:…
Understanding the structural dynamics of biomolecules is crucial for uncovering biological functions. As molecular dynamics (MD) simulation data becomes more available, deep generative models have been developed to synthesize realistic MD trajectories. However, existing methods produce fixed-length
Large Language Models (LLMs) fundamentally suffer from representation collapse, a bottleneck that severely degrades performance in long contexts. We identify that existing approaches risk drifting into one of two pathological extremes: Homogenization Collapse (e.g., attention sinks causing rank defi…