15 papers
From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models
Jiaxin Zhang, Wendi Cui, Zhuohang Li +4
While Large Language Models (LLMs) show remarkable capabilities, their unreliability remains a critical barrier to deployment in high-stakes domains. This survey charts a functiona…
The Illusion of Certainty: Decoupling Capability and Calibration in On-Policy Distillation
Jiaxin Zhang, Xiangyu Peng, Qinglin Chen +3
On-policy distillation (OPD) is an increasingly important paradigm for post-training language models. However, we identify a pervasive Scaling Law of Miscalibration: while OPD effe…
Scaling Knowledge Graph Construction through Synthetic Data Generation and Distillation
Prafulla Kumar Choubey, Xin Su, Man Luo +9
Document-level knowledge graph (KG) construction faces a fundamental scaling challenge: existing methods either rely on expensive large language models (LLMs), making them economic…
Agentic Confidence Calibration
Jiaxin Zhang, Caiming Xiong, Chien-Sheng Wu
AI agents are rapidly advancing from passive language models to autonomous systems executing complex, multi-step tasks. Yet their overconfidence in failure remains a fundamental ba…
Agentic Uncertainty Quantification
Jiaxin Zhang, Prafulla Kumar Choubey, Kung-Hsiang Huang +2
Although AI agents have demonstrated impressive capabilities in long-horizon reasoning, their reliability is severely hampered by the ``Spiral of Hallucination,'' where early epist…
GUI-KV: Efficient GUI Agents via KV Cache with Spatio-Temporal Awareness
Kung-Hsiang Huang, Haoyi Qiu, Yutong Dai +2
Graphical user interface (GUI) agents built on vision-language models have emerged as a promising approach to automate human-computer workflows. However, they also face the ineffic…