A Particle Transformer jet tagger contains a sparse six-head circuit whose source-relay-readout structure recovers most performance and whose residual stream preferentially encodes 2-prong energy correlators.
Graph neural networks in particle physics
8 Pith papers cite this work. Polarity classification is still indexing.
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2026 8representative citing papers
FeynmanBench is the first benchmark for evaluating multimodal LLMs on diagrammatic reasoning with Feynman diagrams, revealing systematic failures in enforcing physical constraints and global topology.
Chirality emerges in SMILES translation models through an abrupt encoder-centered reorganization of representations after a long plateau, identified via checkpoint analysis and ablation.
Derives layer-wise recursions for finite-width tensors under orthogonal initialization that reproduce the observed large-depth stability of nonlinear networks.
Transformers reconstruct the constituent RCFTs in tensor-product theories from low-energy spectra, reaching 98% accuracy on WZW models and generalizing to larger central charges with few out-of-domain examples.
GSC-QEMit adaptively mitigates quantum errors using hierarchical context clustering, Gaussian-process forecasting, and contextual bandits, delivering 9% higher average logical fidelity than unmitigated runs in Qiskit Aer simulations.
Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.
Hybrid FPGA-AI Engine deployment of a dynamic GNN for Belle II trigger achieves 2.94M events/s throughput at 7.15us latency with 53% better throughput and DSP usage reduced from 99% to 19%.
citing papers explorer
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Dissecting Jet-Tagger Through Mechanistic Interpretability
A Particle Transformer jet tagger contains a sparse six-head circuit whose source-relay-readout structure recovers most performance and whose residual stream preferentially encodes 2-prong energy correlators.
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FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning
FeynmanBench is the first benchmark for evaluating multimodal LLMs on diagrammatic reasoning with Feynman diagrams, revealing systematic failures in enforcing physical constraints and global topology.
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From Syntax to Semantics: Unveiling the Emergence of Chirality in SMILES Translation Models
Chirality emerges in SMILES translation models through an abrupt encoder-centered reorganization of representations after a long plateau, identified via checkpoint analysis and ablation.
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Criticality and Saturation in Orthogonal Neural Networks
Derives layer-wise recursions for finite-width tensors under orthogonal initialization that reproduce the observed large-depth stability of nonlinear networks.
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Reconstructing conformal field theoretical compositions with Transformers
Transformers reconstruct the constituent RCFTs in tensor-product theories from low-energy spectra, reaching 98% accuracy on WZW models and generalizing to larger central charges with few out-of-domain examples.
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GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation
GSC-QEMit adaptively mitigates quantum errors using hierarchical context clustering, Gaussian-process forecasting, and contextual bandits, delivering 9% higher average logical fidelity than unmitigated runs in Qiskit Aer simulations.
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Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training
Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.
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Reconfigurable Computing Challenge: Real-Time Graph Neural Networks for Online Event Selection in Big Science
Hybrid FPGA-AI Engine deployment of a dynamic GNN for Belle II trigger achieves 2.94M events/s throughput at 7.15us latency with 53% better throughput and DSP usage reduced from 99% to 19%.