ARIA is a three-tier causal framework that conditions LLM knowledge use on mechanistic completeness for forward prediction and inverse design of 2D materials, producing auditable traces.
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4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
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C2FL proposes spatial clustering plus continual learning techniques inside federated learning to maintain performance under combined spatial heterogeneity and temporal drift.
MixT compresses Transformer LLMs by substituting targeted linear projections with tensor-operator mixtures, preserving MMLU accuracy up to model-specific boundaries where parameter count drops 47.5% and inference memory 60.4% on LLaMA2-7B.
Authors propose a four-stage framework to analyze opportunities and risks of generative AI across the health information journey from public sources to clinical care.
citing papers explorer
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ARIA: A Causal-Aware Framework for Rescuing LLM Reasoning in Trustworthy Materials Discovery
ARIA is a three-tier causal framework that conditions LLM knowledge use on mechanistic completeness for forward prediction and inverse design of 2D materials, producing auditable traces.
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C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift
C2FL proposes spatial clustering plus continual learning techniques inside federated learning to maintain performance under combined spatial heterogeneity and temporal drift.
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A general tensor-structured compression scheme for efficient large language models
MixT compresses Transformer LLMs by substituting targeted linear projections with tensor-operator mixtures, preserving MMLU accuracy up to model-specific boundaries where parameter count drops 47.5% and inference memory 60.4% on LLaMA2-7B.
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Opportunities and Risks of Generative AI through the Health Information Journey
Authors propose a four-stage framework to analyze opportunities and risks of generative AI across the health information journey from public sources to clinical care.