Smiles
PulseAugur coverage of Smiles — every cluster mentioning Smiles across labs, papers, and developer communities, ranked by signal.
6 day(s) with sentiment data
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Rust library enhances LLM molecular reasoning with explicit graph formats
A Rust cheminformatics library called chematic has been developed to improve how Large Language Models (LLMs) process molecular data. The library addresses limitations of using simple SMILES strings by incorporating exp…
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New FisherSketch method analyzes LLM update geometry at scale
Researchers have developed FisherSketch, a novel method for analyzing the geometry of updates in large language models (LLMs) with shared vocabularies. This technique allows for training-free source selection in scienti…
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Chemical language models' internal representations analyzed with sparse autoencoders
A new research paper explores the internal workings of chemical language models (cLMs) by applying sparse autoencoders (SAEs) to MolFormer. The study reveals that early layers of the model focus on syntactic patterns an…
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BioMatrix integrates sequences, structures, and language in new multimodal foundation model
Researchers have developed BioMatrix, a novel multimodal foundation model designed to integrate biological data types like sequences, structures, and natural language within a single architecture. Unlike previous models…
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New benchmark MolGraphBench evaluates GNNs for molecular regression tasks
A new benchmark called MolGraphBench has been introduced to evaluate Graph Neural Network (GNN) architectures for molecular regression tasks. The benchmark, proposed by Ishaan Gupta, analyzes four common GNN models, fin…
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LLM molecular tasks depend on representation, study finds
A new study on arXiv benchmarks the performance of 16 large language models across nine molecular representations for eight chemical tasks. The research found that model performance is heavily dependent on the molecular…
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New GFlowNet training method improves LLM prefix balance and diversity
Researchers have introduced a new training method for Generative Flow Networks (GFlowNets) called Rooted absorbed prefix Trajectory Balance (RapTB), designed to address issues like prefix collapse and length bias in lar…
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New method steers LLM attention to correct reasoning errors
Researchers have developed Manifold-Guided Attention Steering (MAGS), a novel method to improve the reasoning capabilities of large language models. MAGS identifies deviations from a 'correctness manifold' in the model'…
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New benchmark MolRecBench-Wild challenges real-world chemical structure recognition
Researchers have introduced MolRecBench-Wild, a new benchmark designed to evaluate Optical Chemical Structure Recognition (OCSR) systems on real-world chemical diagrams from scientific literature. This benchmark address…
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MolReFlect framework aligns molecules and text for better LLM understanding
Researchers have developed MolReFlect, a novel teacher-student framework designed to improve the alignment between molecular structures and textual descriptions. This approach enables large language models to learn fine…
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COMO framework uses minimum risk training for optical molecule recognition
Researchers have introduced COMO, a novel closed-loop framework for optical chemical structure recognition. This system utilizes Minimum Risk Training (MRT) to address the exposure bias inherent in traditional teacher-f…