QD-LLM evolves prompt embeddings via neuroevolution in a quality-diversity framework, delivering 46% higher coverage and 41% higher QD-score than prior methods on coding and writing benchmarks.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
years
2026 7verdicts
UNVERDICTED 7representative citing papers
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
WPGRec is a new sequential recommender that performs multi-scale temporal modeling via stationary wavelet packets and injects high-order collaborative information through scale-aligned graph propagation with energy-aware gated fusion.
Subword tokenization's main benefits arise from higher sample throughput and the use of subword boundaries as explicit priors or inductive biases, isolated via controlled byte-level simulations.
CUCI-Net abstracts context-utterance dependency into an interpretation cue that combines local modality signals with global context and feeds it into the final multimodal interaction for context-conditioned predictions.
A multi-agent framework decomposes multimodal empathetic response generation into structured reasoning steps and uses global reflection to reduce emotional biases, outperforming prior methods on IEMOCAP and MELD benchmarks.
The paper introduces a measure of semantic entanglement in embeddings and a pipeline that improves Top-K retrieval precision from 32% to 82% on a healthcare knowledge base.
citing papers explorer
-
Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
QD-LLM evolves prompt embeddings via neuroevolution in a quality-diversity framework, delivering 46% higher coverage and 41% higher QD-score than prior methods on coding and writing benchmarks.