5 papers
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,…
One Analyst Is Not Ground Truth: Grading Agent-Built Financial Models Against Observed Professional Practice
Jiacheng Lu, Sinuo Wang, Yiming Li +15
Language model agents can now construct complete financial models, but it remains unclear whether they have learned the financial judgment that gives those models meaning. Existing…
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…
Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning
Zuoyou Jiang, Li Zhao, Rui Sun +6
Signal decay and regime shifts pose recurring challenges for data-driven investment strategies in non-stationary markets, where conventional time-series and machine learning approa…
LAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation
Yiqun Shen, Song Yuan, Zhengze Zhang +3
KV Cache is commonly used to accelerate LLM inference with long contexts, yet its high memory demand drives the need for cache compression. Existing compression methods, however, a…