FuRA uses block tensor-train factorization with fixed pretrained SVD basis to achieve full-rank spectral preconditioning, outperforming Full FT by +1.37 on LLaMA-3-8B commonsense reasoning and surpassing QLoRA in quantized settings.
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2026 3verdicts
UNVERDICTED 3representative citing papers
SeqMem-Eval reveals that high final accuracy in sequential LLM memory tasks often coexists with substantial forgetting and negative transfer, exposing stability-adaptability trade-offs hidden by standard aggregate metrics.
MoRe identifies modular structure in representations themselves to enable principled reuse, alignment, and expansion of modules during continual adaptation on sequential data.
citing papers explorer
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FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning
FuRA uses block tensor-train factorization with fixed pretrained SVD basis to achieve full-rank spectral preconditioning, outperforming Full FT by +1.37 on LLaMA-3-8B commonsense reasoning and surpassing QLoRA in quantized settings.
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Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory
SeqMem-Eval reveals that high final accuracy in sequential LLM memory tasks often coexists with substantial forgetting and negative transfer, exposing stability-adaptability trade-offs hidden by standard aggregate metrics.
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MoRe: Modular Representations for Principled Continual Representation Learning on Sequential Data
MoRe identifies modular structure in representations themselves to enable principled reuse, alignment, and expansion of modules during continual adaptation on sequential data.