10 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…
Janus: An Algorithm-Evaluator Co-Evolution Framework for LLM-Driven Discovery under Expensive Evaluation Budgets
Ximeng Liu, Qianlong Wang, Yingming Mao +6
LLM-driven program discovery relies on rapid evaluator feedback, but many scientific and engineering tasks require high-fidelity simulations, hardware execution, or physical experi…
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…
A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction
Jinhui Ren, Huaiming Li, Yabin Liu +9
High-fidelity vehicle drag evaluation is constrained less by solver runtime than by workflow friction: geometry cleanup, meshing retries, queue contention, and reproducibility fail…
QianfanHuijin Technical Report: A Novel Multi-Stage Training Paradigm for Finance Industrial LLMs
Shupeng Li, Weipeng Lu, Linyun Liu +16
Domain-specific enhancement of Large Language Models (LLMs) within the financial context has long been a focal point of industrial application. While previous models such as Bloomb…
The FM Agent
Annan Li, Chufan Wu, Zengle Ge +19
Large language models (LLMs) are catalyzing the development of autonomous AI research agents for scientific and engineering discovery. We present FM Agent, a novel and general-purp…
OctoNav: Towards Generalist Embodied Navigation
Chen Gao, Liankai Jin, Xingyu Peng +5
Embodied navigation stands as a foundation pillar within the broader pursuit of embodied AI. However, previous navigation research is divided into different tasks/capabilities, e.g…