PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments
PACE-Bench is a new benchmark designed to evaluate how self-evolving AI agents adapt to changing environments.
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PACE-Bench is a new benchmark designed to evaluate how self-evolving AI agents adapt to changing environments.
SheetCompass is an agentic framework designed to improve how large language models (LLMs) reason about and automate complex spreadsheet tasks.
Split the Labor: Separating Evidence Interpretation from Decision Aggregation proposes a design principle for decision-support systems that use language mode...
Shift Aware Transfer Learning with Adaptive Dual-Encoder Fusion for PM Forecasting in Data-Limited Environments explained Researchers Shahab Band and Hamed M...
Handover of In-Context Learning State Across Session Boundaries explores how to transfer essential information from one large language model session to anoth...
Twin: Playing an Unknown Game with a Test-Time Digital Twin introduces a system that enables AI agents to learn and solve unfamiliar, rule-hidden games by bu...
Wyvern is a multi-agent framework designed to automate the creation of technical reports that integrate text, images, and tables while ensuring the content i...
Participatory Moral AI Is Not Neutral: The Invisible Hand of Developers examines how the process of gathering public input for AI decision-making is influenc...
The Dynamics of Intelligence Explosions explained Toby Ord’s paper, The Dynamics of Intelligence Explosions, investigates the mathematical conditions require...
Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations introduces optstop, a framework designed to reduce the computational cost of evaluating l...
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