TAG-DLM integrates graph message passing into masked diffusion language models via topology attention masks on linearized neighborhoods, enabling prompt-based adaptation for node classification, link prediction, and transfer on text-attributed graphs.
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RSC-ZO achieves high-probability ε-stationary points for stochastic ZO optimization under weak-L_p heavy-tailed noise with Õ(d^{p/2(p-1)} ε^{-(3p-2)/(p-1)}) function queries.
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
Adapted MelBERT MIP-only reaches 0.7281 F1 on Chinese token-level metaphor detection, outperforming RoBERTa and Qwen QLoRA, with all artifacts released for reproducibility.
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TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
TAG-DLM integrates graph message passing into masked diffusion language models via topology attention masks on linearized neighborhoods, enabling prompt-based adaptation for node classification, link prediction, and transfer on text-attributed graphs.
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Stochastic Zeroth-Order Optimization Under Heavy-Tailed Noise
RSC-ZO achieves high-probability ε-stationary points for stochastic ZO optimization under weak-L_p heavy-tailed noise with Õ(d^{p/2(p-1)} ε^{-(3p-2)/(p-1)}) function queries.
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
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A Reproducible Multi-Architecture Baseline for Token-Level Chinese Metaphor Identification under the MIPVU Framework
Adapted MelBERT MIP-only reaches 0.7281 F1 on Chinese token-level metaphor detection, outperforming RoBERTa and Qwen QLoRA, with all artifacts released for reproducibility.