9 papers
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-…
UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing
Daniel Eckhoff, Hua Kang, Zhitang Chen +1
Wi-Fi sensing promises to turn the everyday wireless signals that already surround us into ubiquitous sensors for human sensing. However, a fundamental obstacle is that CSI is acqu…
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…
Faster but Different: Diagnosing and Controlling Content Drift in Accelerated Multimodal Diffusion Language Models
Yaoxuan Dou, Yang Shu
Training-free acceleration makes diffusion-based multimodal large language models (dMLLMs) more deployable, but it may silently change generated content. We study this serving-time…
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…
ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
Chenyu Wang, Yueyuan Li, Yingmin Liu +1
Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice. We present ConflictR…
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…
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…
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…