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cs.AI2026
From Search to Research: Exploring Search Scaling in Autonomous Quantitative Factor Mining
Kangcheng Deng, Hui Cai, Jiacheng Lu +6
Inference scaling has been shown to improve large language model (LLM) performance, and this principle naturally extends to autonomous LLM agents through increased search budgets,…
cs.AI2026
From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets
Taojie Zhu, Wentao Zhao, Rui Sun +7
Evaluating whether large language model (LLM) agents can profit in capital markets is increasingly framed as end-to-end trading: place an agent in a historical market, let it trade…
cs.AI2026
Trade-R1: Bridging Verifiable Rewards to Stochastic Environments via Process-Level Reasoning Verification
Rui Sun, Yifan Sun, Sheng Xu +5
Reinforcement Learning (RL) has enabled Large Language Models (LLMs) to achieve remarkable reasoning in domains like mathematics and coding, where verifiable rewards provide clear…