arXiv AI By Ruochang Li, Pengcheng Huang, Zhenghao Liu, Yukun Yan, Huiyuan Xie, Yu Gu, Ge Yu, Maosong Sun

SPARK: Adaptive Parametric Knowledge Participation for Faithful Retrieval-Augmented Generation

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arXiv AI
Jun 16

Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift

arXiv:2606. 15734v1 Announce Type: cross Abstract: Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate weight drift, potentially causing catastrophic forgetting and degrading general capabilities.

By Weihang Su, Jiacheng Kang, Jingyan Xu, Qingyao Ai, Jianming Long, Hanwen Zhang, Bangde Du, Xinyuan Cao, Min Zhang, Yiqun Liu
arXiv AI
Aug 20

From Storage to Access: Verifiable Activation of Parametric Knowledge in LLMs via Explicit Priming and Implicit Reasoning

The paper introduces VAKE, a two‑stage reinforcement‑learning framework that activates latent factual knowledge in large language models. In the Priming stage, the model explicitly inserts bridging triples into an insufficient subgraph, guided by rewards from a frozen model’s answers. The Reasoning stage then trains the model to answer from the original input, demonstrating that the elicitation capability transfers to implicit reasoning and consistently outperforms baselines across multiple benchmarks and model sizes.

By Zuocheng Ying, Yang Yang, Yumou Wu, Chuanbo Zhu, Jiarui Wang, Ziqi Wu, Jingming Cai, Junqing Yu, Zikai Song
arXiv Machine Learning
Sep 29

Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation

The paper introduces TRACE, a fine‑tuning framework for Retrieval‑Augmented Generation (RAG) that addresses conflicts between retrieved knowledge and a model’s internal knowledge. TRACE uses multi‑agent debate traces to identify correct and incorrect candidates and answer‑shift patterns, providing fine‑grained supervision for reliable knowledge‑source selection. It also incorporates an answer‑completeness regularization mechanism to prevent empty, overly short, or prematurely terminated responses, thereby improving robustness against misleading retrieved content and enhancing answer quality.

By Zhengchen Huang, Yundong Sun, Minrui Song, Shuanglong Yao, Ye Liu, Ji Chen, Xing Wang