1 citations · 1 across the 3 of their papers we have counts for
7 papers
Combee: Scaling Prompt Learning for Self-Improving Language Model Agents
Hanchen Li, Runyuan He, Qizheng Zhang +11
Recent advances in prompt learning allow large language model agents to acquire task-relevant knowledge from inference-time context without parameter changes. For example, existing…
EvoX: Meta-Evolution for Automated Discovery
Shu Liu, Shubham Agarwal, Monishwaran Maheswaran +14
Recent work such as AlphaEvolve has shown that combining LLM-driven optimization with evolutionary search can effectively improve programs, prompts, and algorithms across domains.…
K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model
Shiyi Cao, Ziming Mao, Joseph E. Gonzalez +1
Optimizing GPU kernels is critical for efficient modern machine learning systems yet remains challenging due to the complex interplay of design factors and rapid hardware evolution…
AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization
Mert Cemri, Shubham Agrawal, Akshat Gupta +9
The paradigm of automated program generation is shifting from one-shot generation to inference-time search, where Large Language Models (LLMs) function as semantic mutation operato…
GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
Lakshya A Agrawal, Shangyin Tan, Dilara Soylu +14
Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often requir…
GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
Manish Shetty, Naman Jain, Jinjian Liu +3
Developing high-performance software is a complex task that requires specialized expertise. We introduce GSO, a benchmark for evaluating language models' capabilities in developing…