Intelligent agents must be generalists, capable of quickly adapting to various tasks.

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks.

Compiler backends are tasked with generating executable machine code for processors. With the proliferation of diverse processors, it is imperative for programmers to tailor specific compiler backends to accommodate each one.

With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training stability.

We present an alternative approach to decompose non-negative tensors, called many-body approximation .

Reasoning over TKGs has two challenges: 1.

How do language models use information provided as context when generating a response?

Activist fund Palliser Capital has taken a stake in Japanese washlet maker Toto, and is urging the company to boost promotion of its little-known chip parts business. Activist fund Palliser Capital has taken a stake in Japanese washlet maker Toto and is pushing the firm to ramp up promotion of its little-known chip parts business in a bid to unlock value from the artificial intelligence boom. The U.K.-based fund sent a letter to Toto's board last week calling for more disclosure about its advanced ceramics segment. The segment produces electrostatic chucks used in the manufacturing of NAND memory chips, and Palliser views the toilet maker as "the most undervalued and overlooked AI memory beneficiary," the documents show. A representative for Toto declined to comment. Insatiable demand for AI infrastructure has sent memory prices skyrocketing in recent months, boosting shares of chipmakers such as Kioxia Holdings to record highs.