arXiv:2608. 00697v1 Announce Type: cross Abstract: Variational autoencoders (VAEs) trained on multiple sequence alignments (MSAs) have emerged as powerful generative models for biological sequences, with applications ranging from disease variant prediction to functional RNA design.
arXiv:2607. 06583v1 Announce Type: cross Abstract: DNA methylation (DNAm) serves as one of the most robust molecular biomarkers of biological aging.
The paper introduces GenDA, a bidirectional discrete diffusion model designed for genomic sequence reconstruction, hypothesizing that entropy-guided span placement would improve variant-effect prediction and functional sequence generation. While the 202‑million‑parameter GenDA model achieves a higher ClinVar SNV AUROC (0.774) than a comparable autoregressive model, the improvement is not attributable to entropy guidance, and the model fails to outperform a shuffled‑gap baseline in zero‑shot functional inpainting across various genomic regions. The authors identify limitations such as tokenization granularity, span length caps, and the mismatch between local sequence complexity and functional importance, concluding that variant prediction, corruption priors, and functional generation are distinct tasks requiring separate validation.
arXiv:2603. 14717v2 Announce Type: replace Abstract: Generating novel protein sequences that respect a family's statistical constraints typically requires training deep generative models on thousands to millions of examples.
arXiv:2608.30946v1 Announce Type: new Abstract: Closed-loop human-AI systems generate high-dimensional behavioural trajectories whose collective dynamics remain obscure. Using 297,915 learners' adapt...
The paper introduces ORBIT, a framework for probing higher‑order epistasis in protein representations. ORBIT validates Walsh‑based diagnostics on synthetic landscapes, then applies them to the GB1 fitness landscape, comparing several models including ridge regression, MLPs, and Residual Interaction Tokenization (RIT). While no architecture differences were found in overall prediction performance, RIT notably increased pairwise token‑level accessibility, and deeper MLPs improved higher‑order functional recovery, revealing representation‑level changes hidden by conventional metrics.
arXiv:2608. 15483v1 Announce Type: new Abstract: Modern deep networks are trained through long update trajectories, yet their temporal organization remains less systematically characterized than architectures, losses, or optimizers.
arXiv:2606. 07563v1 Announce Type: cross Abstract: Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite radically different microscopic details.
arXiv:2607. 23258v1 Announce Type: new Abstract: Large-scale calcium imaging has created an opportunity to build foundation-style models for neural population dynamics, but a central question remains unresolved: \textbf{whether a model pretrained on one collection of recordings can generalize to new datasets, experimental paradigms, and even species.
arXiv:2606. 08100v1 Announce Type: new Abstract: Multimodal $\Delta\Delta G$ predictors integrating protein language models with inverse-folding representations achieve strong in-distribution accuracy on the Megascale dataset but exhibit limited robustness on out-of-distribution (OOD) proteins, persistent forward-reverse bias on paired-mutation benchmarks, and under-representation of rare stabilizing mutations.
arXiv:2608.25631v1 Announce Type: cross Abstract: Continuous-time Markov chains (CTMCs) provide the backbone for modeling discrete stochastic dynamics across applied, physical, and biological science...
arXiv:2607. 10439v2 Announce Type: replace-cross Abstract: We model human motor cortex, recorded during rest and motor-imagery BCI conditions, as a port-Hamiltonian system: a conservative interconnection (skew-symmetric coupling between band-limited neural phasors) together with a dissipative port whose state-dependent decay is set by a graph-neural-network surrogate.