From the 1 of 7 linked papers with an AI index.
7 papers
Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models
Hua-Dong Xiong, Xinyuan Yan, Ji-An Li +3
The paper investigates how large language models think under uncertainty by testing them on two-armed bandit tasks, measuring action preferences, thinking duration, and confidence,…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning
Sixing Chen, Ji-An Li, Saner Cakir +3
Large language models (LLMs), especially reasoning models, generate extended chain-of-thought (CoT) reasoning that often contains explicit deliberation over future outcomes. Yet wh…
The Position Curse: LLMs Struggle to Locate the Last Few Items in a List
Zhanqi Zhang, Hua-Dong Xiong, Robert C. Wilson +3
Modern large language models (LLMs) can find a needle in a haystack (locating a single relevant fact buried among hundreds of thousands of irrelevant tokens) with near-saturated ac…
Harnessing Pre-Resolution Signals for Future Prediction Agents
Chuyang Wei, Maohang Gao, Zhixin Han +12
Many high-stakes decisions depend on forecasts made before outcomes are known. In this future prediction setting, the central challenge is that public evidence evolves over time, w…
In-context superposition: human-like working memory interference in large language models
Hua-Dong Xiong, Li Ji-An, Jiaqi Huang +3
Intelligent systems must maintain and manipulate task-relevant information online to adapt to dynamic environments. This capacity, known as working memory, is fundamental to human…