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cs.AI2026

LiveMACE: Process-Aware Evaluation of LLM Agent Capabilities in Evolving Markets

Jun Zhao, Leiming Fu, Yanbo Wen +9

Evaluating agents by outcomes alone can obscure the capabilities that produce them. This problem is especially pronounced in evolving environments, where outcomes reflect a closed-…

cs.AI2026

Atria Dawn: The Dawn of Agentic Superintelligence

Honglin Guo, Tao Gui, Yicheng Chen +139

As AI agents become participants in the development of their successors, they reshape both the production of intelligence and the role of human researchers. We introduce Atria Dawn…

cs.AI2026

Numbers Beat Words: A Rigorous On-Premise Benchmark for Coupled MIMO Controller Tuning

Yang Shu, Jiaxuan Chen, Haonan Li

Tuning controllers for strongly coupled multi-input multi-output (MIMO) processes is difficult because decentralized auto-tuning ignores loop interaction and local optimization is…

cs.AI2026

CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models

Yuning Wu, Yingmin Liu, Yang Shu

Large language model (LLM) self-correction -- the ability to detect and fix errors in generated outputs -- remains largely ad hoc, relying on generic prompts such as "please recons…

cs.AI2026

MetaCogAgent: A Metacognitive Multi-Agent LLM Framework with Self-Aware Task Delegation

Chenyu Wang, Yang Shu

Multi-agent large language model (LLM) systems have shown promise for solving complex tasks through agent collaboration. However, existing frameworks assign tasks based on predefin…

cs.AI2026

Agentic Retrieval-Augmented Generation for Financial Document Question Answering

Yang Shu, Yingmin Liu, Zequn Xie

Financial document question answering (QA) demands complex multi-step numerical reasoning over heterogeneous evidence--structured tables, textual narratives, and footnotes--scatter…