Researchers have proposed a method called CASE to improve the accuracy of large language models by selecting the best answer based on hidden-state sig...
Researchers propose a framework called 'cooperative observation' to improve the performance of personal AI systems. They argue that these systems need...
Researchers have developed a new framework called KNOWSIM for evaluating the performance of Large Language Models (LLMs) in collaborating with users o...
Researchers have developed an automated approach to designing feature extractors for constraint satisfaction problems. This method uses large language...
Researchers evaluated five widely used benchmark suites on 26 open-source small language models to determine their effectiveness in assessing safety. ...
Researchers have developed a new defense mechanism called 'Fool's Gold' to protect open-weight language models from safety-removal attacks. These atta...
Researchers have developed an audit protocol to test the performance of personalized agents in decision-making tasks. They created a synthetic develop...
Researchers proposed a new method to evaluate scientific hypotheses generated by large language models (LLMs). Instead of relying on LLMs as judges or...
Researchers have introduced ASI-Bench, a new benchmark designed to evaluate the capabilities of artificial intelligence systems in exploring the unkno...
A new AI framework called DeAR (Decentralized Agentic Reasoning) has been proposed to improve the accuracy of complex reasoning tasks. Unlike traditio...
Researchers have proposed a new method for training large language models (LLMs) called PlanPO. This approach aims to improve the performance of LLMs ...
Researchers have introduced LiveHouse-TS, an open-world living benchmark for Time Series Foundation Models (TSFMs). Unlike traditional benchmarks that...