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Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents introduces a new benchmark, InvestLogicBen...
QuanTiMedAI is a framework designed to predict mortality for patients who have experienced cardiac arrest in intensive care units (ICUs).
The Nimblemind Multi-Agent System (nMAS) provides a method for automating the transformation of fragmented electronic health record (EHR) data into structure...
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints addresses the tendency for synthetic healthcare datasets to pass automated u...
From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models proposes a framework for building generative simulations of economie...
Comparative Approaches to Agent Retrieval over Large Skill Libraries explores how AI agents can efficiently access specific capabilities from a large library...
When History Lies: Evaluating and Improving Tool Use under Misleading Multi-Turn Histories investigates why tool-calling language models often fail when pres...
Poli-Bias is a framework designed to measure political bias in large language models (LLMs) by analyzing how they handle legally equivalent conflict scenario...
Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors explores how to detect if automated speaking assessment systems are biased a...
HarnessOpt-Bench is a new benchmark designed to measure how well Large Language Models (LLMs) can perform "harness optimization"—the process of iteratively i...
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