3 citations · 6 across the 13 of their papers we have counts for
6 papers · 1 filter
Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization
Yuhan Chen, Zhihua Tian, Mahavir Dabas +7
The performance of an LLM agent depends on the scaffold around a frozen model. A common way to improve that scaffold is to use a coding agent as an optimizer: it reads current scor…
Can Generalist Agents Automate Data Curation?
Feiyang Kang, Hanze Li, Adam Nguyen +5
Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data p…
Confidence-Aware Alignment Makes Reasoning LLMs More Reliable
Kejia Chen, Jiawen Zhang, Yihong Wu +5
Large reasoning models often reach correct answers through flawed intermediate steps, creating a gap between final accuracy and reasoning reliability. Existing alignment strategies…
Characterizing Model-Native Skills
Feiyang Kang, Mahavir Dabas, Myeongseob Ko +1
Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on human-written taxonomies, tex…
Strategize Globally, Adapt Locally: A Multi-Turn Red Teaming Agent with Dual-Level Learning
Si Chen, Xiao Yu, Ninareh Mehrabi +3
The exploitation of large language models (LLMs) for malicious purposes poses significant security risks as these models become more powerful and widespread. While most existing re…
Data Acquisition: A New Frontier in Data-centric AI
Lingjiao Chen, Bilge Acun, Newsha Ardalani +8
As Machine Learning (ML) systems continue to grow, the demand for relevant and comprehensive datasets becomes imperative. There is limited study on the challenges of data acquisiti…