This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance for real-world deployment.
Tian-Xin Wei, Ting-Wei Li, Zhining Liu et al.· 39 citations· ⚡6
The proposed TTIQ, a test-time reinforcement learning framework that harnesses image-question dependence for better vlm adaptation, combines image and question dependence with calibrated confidence to construct a response-level reward that favors jointly grounded responses.
Xinrui He, Ting-Wei Li, Jun-Ting Wang et al.· 0 citations
This formulation enables a systematic study of key self-improvement factors through the proposed Evo-Harness, and provides a principled understanding of how LLM agents can effectively learn on the fly.
Tian-Xin Wei, Zhan Shi, Min-hua Lin et al.· 14 citations
AFANet is introduced, a lightweight graph-based framework that models interaction trajectories through step-level semantic signals and agent-level relationships and suggests that effective agent failure attribution does not require heavy LLM reasoning and a lightweight, structured approach can achieve strong performanc...
Ting-Wei Li, Yuan-Chen Bei, Xiao Lin et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
