Author

Junnan Liu

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#artificial intelligence May 2026

AIPO: Learning to Reason from Active Interaction

This work proposes AIPO, an enhanced reinforcement learning framework that improves LLM reasoning through active multi-agent interaction during exploration and consistently improves reasoning performance and generalizes across different policy models, collaborator backbones, and RLVR algorithms.

Junnan Liu, Linhao Luo, Thuy-Trang Vu et al. · arXiv.org · 0 citations

#natural language process... Preprint Sep 2026

Selective Knowledge Edit Reversal via Gated Singular Vector Shrinkage

Results suggest that different edits are sparsely encoded within dominant singular components and can be separable when the number of edits is moderate, making selective spectral reversal a promising direction for locating edit-specific components and repairing edited language models.

Wei-Feng Jiang, Rui-Rui Chen, Qian-Ren Mao et al. · 0 citations

Preprint Aug 2026

EASy: Towards Efficient LLM-Based Agentic System

EASy is proposed, a trainable agentic framework that jointly optimizes task performance and computational efficiency through reinforcement learning and consistently achieves stronger performance-efficiency trade-offs than strong agentic baselines.

Junnan Liu, Linhao Luo, Thuy-Trang Vu et al. · 0 citations