The study compared LLM-based conversational agents (CAs) and traditional dashboards for industrial decision support, finding that while CAs reduce mental workload in simple tasks, neither interface pr…

This paper evaluates the performance of a Large Language Model (LLM) in a high-stakes context by comparing it to human experts and measuring variance and error magnitude.

This paper evaluates the performance of a Large Language Model (LLM) in a high-stakes context by comparing it to human experts and measuring variance and error magnitude.

The paper introduces ForeSci, a novel benchmark that evaluates LLM agents' ability to make forward-looking research judgments using only historical evidence, finding that explicit evidence organizatio…

The paper introduces an adaptive interview framework to gather rich persona context, demonstrating that LLMs improve decision alignment in moral dilemmas only when they selectively ground their decisi…

This paper empirically demonstrates that the choice of plan representation (e.g., checklist vs. narrative) significantly impacts the robustness and success rate of LLM-based web agents.

Persona prompting does not universally improve LLM performance; instead, it systematically trades increased expertise depth for reduced clarity, making multi-metric evaluation essential.

The paper proposes the Interaction-Native Knowledge Harness (InKH), an architecture that absorbs complex context into financial LLM agents, significantly improving performance, reducing latency, and e…

This paper proposes a multi-agent framework using LLMs to improve collaborative story generation, demonstrating that an iterative Writer-Editor process significantly enhances narrative quality for you…

CA-BED is a novel framework that improves LLM performance in interactive question-answering by integrating Bayesian Experimental Design to strategically select questions that maximize information gain…

Analyzing longitudinal data from 12,000 Copilot users, the paper finds that individual user habits regarding LLM interaction are highly sticky and difficult to change, and that existing datasets may o…

COLLEAGUE.SKILL introduces an automated system that distills heterogeneous traces of human expertise and role-specific knowledge into portable, inspectable, and usable AI skill packages.

This paper proposes a modular, agentic AI chatbot architecture to assist students with exercise solving, aiming to ensure responsible and pedagogically sound AI use in education.

The paper proposes a structured framework, the Cognitive Accessibility UXR Playbook, that uses UXR principles and Generative AI to transform ambiguous requirements into measurable, actionable specific…

The BEAMS initiative establishes comprehensive benchmarks and evaluates AI tools for modeling and simulation, finding that current AI tools excel at qualitative discussion tasks but struggle with comp…

This study compares different levels of LLM access in a statistics course, finding that structured, guided use significantly improves students' reasoning skills and independent learning compared to un…

FundaPod is a multi-persona agent platform designed for fundamental investment research, enabling AI agents with distinct viewpoints to independently gather evidence and surface disagreements for huma…

The paper critiques current AI benchmarking practices for low-resource settings, arguing that evaluation must shift focus from isolated model performance to the holistic performance of the deployed sy…

The paper proposes 'Think Fast, Talk Smart,' a pipeline that separates deterministic data analysis from LLM generation, showing that offloading recurring, structured tasks to code significantly improv…

The paper introduces BlueFin, a challenging benchmark for evaluating LLM agents on complex financial spreadsheet tasks, finding that even frontier models perform poorly, scoring less than 50% on avera…