Author

Seyyedali Hosseinalipour

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#machine learning Preprint Sep 2026

Fisher-Informed Recalibration for Feedback-Based On-Policy Self-Distillation of LLMs

Fire (Fisher-Informed REcalibration), a dual-branch framework that recalibrates the supervision applied to correct and incorrect on-policy outputs during fine-tuning, is proposed, which provides substantially more stable self-distillation while maintaining strong downstream performance, particularly in settings where s...

Seohyun Lee, Dong-Jun Han, Seyyedali Hosseinalipour et al. · 0 citations

Jul 2026

HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning

Her HermesHFL, a hierarchical federated learning framework that supports selective unlearning, dynamic client participation, and client reintegration for scalable LLM fine-tuning via parameter-efficient fine-tuning (PEFT) with LoRA, is proposed and developed.

Chenxi Sun, Minghui Liwang, Wu-Si He et al. · arXiv.org · 0 citations

#artificial intelligence Preprint Sep 2026

Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment

This work proposes a two-stage generator-in-the-loop alignment framework that consistently outperforms rank-order, random, and REPLUG-style likelihood baselines under various alignment losses and pool size settings, suggesting that answer-level generator feedback is an effective supervision signal for preference alignm...

Zhang-Yu Chang, Dong-Jun Han, Seyyedali Hosseinalipour et al. · 0 citations