Current structure-based drug design generative models often struggle to faithfully recapitulate genuine ligand-protein binding interactions. Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff...
Huabin Du, Mingyang Wang, M. Luo et al.· Journal of the American Chem...· 0 citations
This paper introduces Caduceus, a family of MoE-enhanced foundation models built with a hierarchical pre-training paradigm to jointly integrate biological and natural language, and incorporates a multi-task instruction tuning phase, enabling robust protein parsing and natural language question answering.
Mingze Yin, Yiheng Zhu, Jialu Wu et al.· Proceedings of the 32nd ACM...· 0 citations
Post-training alignment is often shallow, eroding under fine-tuning. It remains untested as to whether constitutional midtraining interventions can produce durable alignment when cleanly isolated from post-training. We build a 394M-token constitutional corpus from Anthropic's Constitution and apply constitutional midtr...
D. Cho, Cameron Tice, Bernie Hogan et al.· arXiv.org· 3 citations
Distilling Bayesian Layers into a pretrained 340M Gated DeltaNet improves RULER long-context retrieval over a matched-compute control, and improves robustness beyond the training regime in controlled collision studies, learned associative recall, and the Zoology MQAR benchmark.
Matthew Dowling, H. Jeon, Cristina Savin et al.· arXiv.org· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
The central observation is that the negative product-of-scores feedback employed in accelerated score-driven recursions can be read as the stochastic gradient of this predictive loss, offering a new variational perspective.
This analysis identifies a common obstruction: cheap nuisance interpolation causes the refit to underweight the truly predictive coordinate, and an exact target-mass identity and a two-sign argument turn this effect into clipped prediction loss.
A novel characteristic of dataset heterogeneity is introduced by employing the norm of the difference between the maximum and minimum points in the classical terms of functional analysis.
Maksim V.Kukushkin, M. Arbatskiy, D. Balandin et al.· 0 citations
This paper proposes debiased inference with multiple imperfect measurements (DMM), a framework that combines multiple error-prone AI measurements to enable valid downstream inference without gold-standard labels and proves that the DMM estimator is consistent and asymptotically normal.
During the School of Humanities, Arts, and Social Sciences’ 75th Anniversary Symposium, President Sally Kornbluth said “MIT has never needed your particular insight and guidance more than we do now.”
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
