GLACIER is a single-stage transformer model treating MS/MS fragmentation as subgraph detection on molecular graphs, reporting 70.0% Top-1 accuracy on MassSpecGym and 8x speedup over prior two-stage methods.
Do transformers really perform bad for graph representation?
6 Pith papers cite this work. Polarity classification is still indexing.
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A graph transformer with RL stabilizations is the first to exceed benchmarks for dynamic RMSA, supporting up to 13% more traffic load on networks up to 143 nodes.
GCCM prevents shortcut collapse in consistency models for graph prediction by using contrastive negative pairs and input feature perturbation, leading to better performance than deterministic baselines.
Deep sequence models develop geometric memory in embeddings that encodes novel global relationships, transforming l-fold composition tasks into 1-step navigation via a natural spectral bias connected to Node2Vec.
CLIO agent applies calibrated deference in closed-loop AORFB negolyte design, achieving 90 mV redox potential gain with restored reversibility after hypothesis-driven redesign from phosphonate to sulfonate.
Quantum-oriented embeddings deliver consistent gains on structure-driven graph datasets while classical baselines perform adequately on attribute-limited social graphs, under identical training pipelines across five TU datasets and binned QM9.
citing papers explorer
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GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem
GLACIER is a single-stage transformer model treating MS/MS fragmentation as subgraph detection on molecular graphs, reporting 70.0% Top-1 accuracy on MassSpecGym and 8x speedup over prior two-stage methods.
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Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks
A graph transformer with RL stabilizations is the first to exceed benchmarks for dynamic RMSA, supporting up to 13% more traffic load on networks up to 143 nodes.
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GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model
GCCM prevents shortcut collapse in consistency models for graph prediction by using contrastive negative pairs and input feature perturbation, leading to better performance than deterministic baselines.
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Deep sequence models tend to memorize geometrically; it is unclear why
Deep sequence models develop geometric memory in embeddings that encodes novel global relationships, transforming l-fold composition tasks into 1-step navigation via a natural spectral bias connected to Node2Vec.
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Closed-Loop Molecular Design with Calibrated Deference
CLIO agent applies calibrated deference in closed-loop AORFB negolyte design, achieving 90 mV redox potential gain with restored reversibility after hypothesis-driven redesign from phosphonate to sulfonate.
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How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
Quantum-oriented embeddings deliver consistent gains on structure-driven graph datasets while classical baselines perform adequately on attribute-limited social graphs, under identical training pipelines across five TU datasets and binned QM9.