Agent Bazaar is a multi-agent simulation framework that identifies economic failure modes in LLM agents, proposes stabilizing harnesses, and shows that targeted RL training can produce a 9B model with superior economic alignment compared to frontier models.
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QuantEvolver applies reinforcement fine-tuning to evolve an LLM policy for generating executable alpha factor expressions, yielding higher-quality and more complementary factors than prompt-based baselines on market benchmarks.
Moira parameterizes hierarchical RL policies for pair trading with LLMs and adapts them via prompt updates based on trajectory and episode feedback, outperforming baselines on real market data.
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
A survey on LLM-as-a-Judge that reviews reliability strategies, proposes evaluation methods, and introduces a novel benchmark for assessing such systems.
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
A survey categorizing LLM-powered agent systems into software-based, physical, and hybrid types, covering industrial applications and challenges such as latency and security.
citing papers explorer
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Agent Bazaar: Enabling Economic Alignment in Multi-Agent Marketplaces
Agent Bazaar is a multi-agent simulation framework that identifies economic failure modes in LLM agents, proposes stabilizing harnesses, and shows that targeted RL training can produce a 9B model with superior economic alignment compared to frontier models.
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From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery
QuantEvolver applies reinforcement fine-tuning to evolve an LLM policy for generating executable alpha factor expressions, yielding higher-quality and more complementary factors than prompt-based baselines on market benchmarks.
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Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading
Moira parameterizes hierarchical RL policies for pair trading with LLMs and adapts them via prompt updates based on trajectory and episode feedback, outperforming baselines on real market data.
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A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
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A Survey on LLM-as-a-Judge
A survey on LLM-as-a-Judge that reviews reliability strategies, proposes evaluation methods, and introduces a novel benchmark for assessing such systems.
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A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
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LLM-Powered AI Agent Systems and Their Applications in Industry
A survey categorizing LLM-powered agent systems into software-based, physical, and hybrid types, covering industrial applications and challenges such as latency and security.
- BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting