TeRA parametrizes high-rank LLM weight updates via a random Tucker-like tensor network with shared frozen factors and layer-specific scaling vectors, matching high-rank adapter performance at vector-level parameter counts.
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Case study applies verifier-guided LLM evolutionary agents to contraction-order optimization in tensor networks and concludes that human validation remains essential.
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TeRA: Vector-based Random Tensor Network for High-Rank Adaptation of Large Language Models
TeRA parametrizes high-rank LLM weight updates via a random Tucker-like tensor network with shared frozen factors and layer-specific scaling vectors, matching high-rank adapter performance at vector-level parameter counts.
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Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks
Case study applies verifier-guided LLM evolutionary agents to contraction-order optimization in tensor networks and concludes that human validation remains essential.