Proposes the ‘Optical Chemical Structure Understanding’ (OCSU) task to translate molecular images into multi-level descriptions (motifs, IUPAC, SMILES). Introduces the Vis-CheBI20 dataset and two paradigms: DoubleCheck (OCSR-based) and Mol-VL (OCSR-free).
Tay et al. systematically compare scaling laws across ten diverse architectures (Transformers, Switch Transformers, Performers, MLP-Mixers, and others), finding that the vanilla Transformer has the best scaling coefficient and that the best-performing architecture changes across compute regions.
Fuchs et al. introduce the SE(3)-Transformer, which combines self-attention with SE(3)-equivariance for 3D point clouds and graphs. Invariant attention weights modulate equivariant value messages from tensor field networks, resolving angular filter constraints while enabling data-adaptive, anisotropic processing.
Baldi and Vershynin systematically classify the fundamental building blocks of attention (activation attention, output gating, synaptic gating) by source, target, and mechanism, then prove capacity bounds showing that gating introduces quadratic terms sparsely, gaining expressiveness without the full cost of polynomial activations.
We explore the ‘Silent Failure’ mode of LLMs in production: the limits of 99% accuracy for reliability, how confidence decays in long documents, and why standard calibration techniques struggle to fix it.
We trace the history of Page Stream Segmentation (PSS) through three eras (Heuristic, Encoder, and Decoder) and explain how privacy-preserving, localized LLMs enable true semantic processing.
ChemBERTa-3 provides a unified, scalable infrastructure for pretraining and benchmarking chemical foundation models. It addresses reproducibility gaps in previous studies like MoLFormer through standardized scaffold splitting and open-source tooling.
ChemDFM-R is a 14B-parameter chemical reasoning model that integrates a 101B-token dataset of atomized chemical knowledge. Using a mix-sourced distillation strategy and domain-specific reinforcement learning, it outperforms similarly sized models and DeepSeek-R1 on ChemEval.
This work investigates the scaling hypothesis for molecular transformers, training RoBERTa models on 77M SMILES from PubChem. It compares Masked Language Modeling (MLM) against Multi-Task Regression (MTR) pretraining, finding that MTR yields better downstream performance but is computationally heavier.
This methodological paper proposes a linear-attention transformer decoder trained on 1.1 billion molecules. It introduces pair-tuning for efficient property optimization and establishes empirical scaling laws relating inference compute to generation novelty.
This paper introduces ChemBERTa, a RoBERTa-based model pretrained on 77M SMILES strings. It systematically evaluates the impact of pretraining dataset size, tokenization strategies, and input representations (SMILES vs. SELFIES) on downstream MoleculeNet tasks, finding that performance scales positively with data size.
This paper introduces Chemformer, a BART-based sequence-to-sequence model pre-trained on 100M molecules using a ‘combined’ masking and augmentation task. It achieves top-1 accuracy on reaction prediction benchmarks while significantly reducing training time through transfer learning.