Author

Fuxin Jiang

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#artificial intelligence Feb 2026

Reasoning and Tool-use Compete in Agentic RL:From Quantifying Interference to Disentangled Tuning

This paper proposes Disentangled Action--Reasoning Tuning (DART), a simple and efficient framework that explicitly decouples parameter updates for reasoning and tool use via separate low-rank adaptation modules, further supporting the finding of capability interference under shared optimization.

Yu Li, Ming-Yang Yi, Xiu‐Qing Li et al. · arXiv.org · 15 citations · ⚡1

Preprint Aug 2026

ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models

This work presents a training-free framework that formulates SQL correction as a plan-guided, tree-structured debugging process that mitigates error accumulation during iterative refinement and demonstrates the effectiveness and stability of the approach in real-world deployments.

Xinmei Huang, Jie Song, Peng Li et al. · 0 citations