TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
arXiv preprint arXiv:2402.12345 , year=
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Quantum algorithm solves regularized Wasserstein proximal operator via Schrödingerization of Cole-Hopf transformed heat equations with O(d N_x T log²(1/ε)) query complexity.
Nitsum dynamically adapts tensor parallelism and GPU splits in LLM serving to raise SLO-compliant goodput by up to 5.3 times over prior systems.
MATRAG deploys four agents (user modeling, item analysis, reasoning, explanation) plus knowledge-graph retrieval and a transparency score to raise hit rate 12.7% and NDCG 15.3% while producing explanations rated helpful by 87.4% of experts.
CODA is a new global-optimization scheduler for QEC decoders that reduces longest undecoded sequences by 74% across 19 benchmarks and scales linearly with qubit count.
AgenticRAG equips an LLM with iterative retrieval and navigation tools, delivering 49.6% recall@1 on BRIGHT, 0.96 factuality on WixQA, and 92% correctness on FinanceBench.
Monte Carlo simulations of the diphoton Higgs channel in an effective coupling model reproduce the official ATLAS and CMS constraints on the gluon coupling modifier kappa_g.
citing papers explorer
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A foundation model of vision, audition, and language for in-silico neuroscience
TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
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Schr\"odingerization based quantum algorithms for regularized Wasserstein proximal operators
Quantum algorithm solves regularized Wasserstein proximal operator via Schrödingerization of Cole-Hopf transformed heat equations with O(d N_x T log²(1/ε)) query complexity.
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Nitsum: Serving Tiered LLM Requests with Adaptive Tensor Parallelism
Nitsum dynamically adapts tensor parallelism and GPU splits in LLM serving to raise SLO-compliant goodput by up to 5.3 times over prior systems.
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MATRAG: Multi-Agent Transparent Retrieval-Augmented Generation for Explainable Recommendations
MATRAG deploys four agents (user modeling, item analysis, reasoning, explanation) plus knowledge-graph retrieval and a transparency score to raise hit rate 12.7% and NDCG 15.3% while producing explanations rated helpful by 87.4% of experts.
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Constraint-Optimal Driven Allocation for Scalable QEC Decoder Scheduling
CODA is a new global-optimization scheduler for QEC decoders that reduces longest undecoded sequences by 74% across 19 benchmarks and scales linearly with qubit count.
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AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
AgenticRAG equips an LLM with iterative retrieval and navigation tools, delivering 49.6% recall@1 on BRIGHT, 0.96 factuality on WixQA, and 92% correctness on FinanceBench.
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Constraints on the Higgs-gluon effective coupling through $h \to \gamma\gamma$ decays at the LHC
Monte Carlo simulations of the diphoton Higgs channel in an effective coupling model reproduce the official ATLAS and CMS constraints on the gluon coupling modifier kappa_g.