AlgoEvo is introduced, a unified agentic architecture that transforms automated algorithm discovery into an interactive, knowledge-accumulating process, demonstrating strong intra-task accumulation, cross-task transfer, and the ability to reproduce or exceed the strongest existing methods through flexible skill activat...
Jun-Hao Qiu, Qing-Long Hu, Ji Cheng et al.· 0 citations
Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pipelines with pre-defined control flows. This limitation restricts adaptive reasoning, blocks cross-paradigm transfer, and discards valuable execution feedback. We propos...
Jun-Hao Qiu, Qing-Long Hu, Xia-Liang Tong et al.· 0 citations
Small Language Models (SLMs) have emerged as a more efficient alternative to traditional Large Language Models (LLMs), offering promising potential in resource-constrained scenarios. Existing approaches to building SLMs typically follow two paths: training compact models from scratch, or compressing larger pre-trained...
Haokun Lin, Kaijie Zhu, Hao-Bo Xu et al.· 1 citation
A domain-specific debug agent is presented that addresses three core challenges in autonomous repair: mitigating knowledge scarcity through retrieved patterns and diagnostic instrumentation, ensuring integrity through anti-cheat detection and full-coverage evaluation, and controlling cost via convergence guards and bou...
Yansong Sun, Shenxi Wu, Siyuan Chen et al.· 0 citations
KOPE is presented, an experience-driven framework for hardware kernel optimization that records optimization trajectories with correctness and performance feedback in Experience Graph Memory, then uses Active Context Management and Injection to retrieve relevant experience under a fixed token budget.
Siyuan Chen, Runlin Hou, Shenxi Wu et al.· 0 citations
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