6 papers
FDE-Bench: Evaluating End-to-End Delivery from Underspecified Real-World Business Requests
Huaiming Li, Can Huang, Chufan Wu +18
Real-world business requests often leave key requirements unstated because frontline practitioners struggle to articulate rules they know from experience. Forward-deployed engineer…
RLDISCOVER: LLM-driven co-evolution of reinforcement learning algorithms
Haoran Li, Zengle Ge, Xiaomin Yuan +14
LLM-guided program evolution has enabled discoveries in mathematics and computational optimization, raising the prospect of reinforcement learning (RL) algorithms that self-evolve…
AGAR: a reinforcement learning substrate for LLM program evolution
Haoran Li, Zengle Ge, Xiaomin Yuan +11
Given a task and an evaluator, a language model can rewrite a candidate program while a search loop decides which rewrites survive, offering a practical route to algorithm discover…
A Compact Selective State-Space Model for Cross-Sectional Stock Return Ranking from Raw Intraday Bars
Mingju Chen, Enze Zhang, Annan Li +5
We present STRATA (Staggered-Timescale Residual Architecture), a 244,633-parameter sequence model that maps five trading days of raw five-minute bar and order-book data directly to…
Centering Drives Normalization Gains: Price-Offset Nuisances in Cross-Sectional Return Prediction
Mingju Chen, Qianhui Liu, Yui Lo +1
Cross-sectional return prediction from raw intraday bars is sensitive to each instrument price level, an additive nuisance under a return-ranking hypothesis. We test whether removi…
Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games
Haoran Li, Zengle Ge, Ziyang Zhang +10
Recent advances in LLM-driven code evolution have enabled automated discovery by iteratively generating and improving programs. However, applying these methods to adversarial multi…