arXiv:2509.19586v1 Announce Type: new Abstract: We introduce FragAtlas-62M, a specialized foundation model trained on the largest fragment dataset to date. Built on the complete ZINC-22 fragment subset comprising over 62 million molecules, it achieves unprecedented coverage of fragment chemical space.…
arXiv:2305.16056v4 Announce Type: replace Abstract: Reinforcement Learning Algorithms are predominantly developed for stationary environments, and the limited literature that considers nonstationary environments often involves specific assumptions about changes that can occur in transition probability matrices and reward functions. Considering that…
arXiv:2509.19601v1 Announce Type: new Abstract: In several applications, including in synthetic biology, one often has input/output data on a system composed of many modules, and although the modules’ input/output functions and signals may be unknown, knowledge of the composition architecture…
arXiv:2412.12038v2 Announce Type: replace Abstract: Mixed integer linear programming (MILP) solvers expose hundreds of parameters that have an outsized impact on performance but are difficult to configure for all but expert users. Existing machine learning (ML) approaches require training on…
arXiv:2509.19604v1 Announce Type: new Abstract: Modern therapeutic antibody design often involves composing multi-part assemblages of individual functional domains, each of which may be derived from a different source or engineered independently. While these complex formats can expand disease applicability and…
arXiv:2503.05810v3 Announce Type: replace Abstract: The accurate prediction of chemical reaction outcomes is a major challenge in computational chemistry. Current models rely heavily on either highly specific reaction templates or template-free methods, both of which present limitations. To address these,…
arXiv:2509.19625v1 Announce Type: new Abstract: Indexing time series by creating compact binary representations is a fundamental task in time series data mining. Recently, deep learning-based hashing methods have proven effective for indexing time series based on semantic meaning rather than…
arXiv:2504.03792v2 Announce Type: replace-cross Abstract: Accurately predicting spatio-temporal network traffic is essential for dynamically managing computing resources in modern communication systems and minimizing energy consumption. Although spatio-temporal traffic prediction has received extensive research attention, further improvements in prediction accuracy and…
arXiv:2509.17318v2 Announce Type: replace Abstract: Mathematical reasoning poses significant challenges for Large Language Models (LLMs) due to its demand for multi-step reasoning and abstract conceptual integration. While recent test-time scaling techniques rely heavily on high-quality, challenging problems, the scarcity of…
New research enables computer designs to incorporate the limitations of 3D printers, to better control materials’ performance in aerospace, medical, and other applications.
