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Results for “LLM” · papers 4 · wiki 3
Academic Papers · 4arXiv q-fin live 0 · desk corpus 4
arXiv · arXiv · 2026

Macro Economists in the Machine: A Multi-Agent LLM Framework for Commodity-Related ETF Portfolio Construction

We test whether large language models (LLMs) add value in commodity portfolio construction when the information set and implementation rules are held fixed across strategies. A Hawkish Agent (inflation-tightening prior), a Dovish Agent (growth-easing prior), a Debate Agent, and a deterministic z-score Rule Agent each receive identical FRED macro z-scores and route their tilt signals through the same portfolio engine.

Yiqing Wang, Dehao Dai, Ding Ma, Kerui Geng
arXiv · arXiv · 2026

Optimal Market Making in Prediction Markets

Prediction markets are attracting growing attention as trading volumes rise and their practical relevance increases. To ensure efficient price discovery, liquidity provision becomes ever more important. Due to the binary settlement structure in prediction markets, optimal market making leads to an optimization problem that is fundamentally different from the ones studied in classical settings. In this paper, we devel

Dominik Feil, Max Nendel
arXiv · arXiv · 2026

CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse

The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing resource sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrate

Amr Aboeleneen, Mohamed Abdallah, Aiman Erbad, Amr Salem
arXiv · arXiv · 2026

SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes substantial token overhead, and the model never truly acquires the knowledge it merely

Zhengxi Lu, Zhiyuan Yao, Jinyang Wu, Chengcheng Han, Qi Gu
Wiki Entities · 3
Option Blackboard · 0
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Encyclopedia · 1
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