Component-Aware Feedback for Self-Evolving Programs
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
Successful mutation strategies in evolutionary code search may contain reusable knowledge that is useful beyond a single run, and in some cases may transfer across related tasks and domains. However, existing LLM-driven evolutionary frameworks largely discard such knowledge, repeatedly rediscovering similar ideas and limiting opportunities for cross-run and cross-task learning.
arXiv:2608. 10795v1 Announce Type: new Abstract: Successful mutation strategies in evolutionary code search may contain reusable knowledge that is useful beyond a single run, and in some cases may transfer across related tasks and domains.
Evolutionary Ensemble Search (EES) is a framework that builds machine‑learning procedures through expert‑guided program evolution. A specialized council interprets task evidence and experimental results to generate structured search directions, which an orchestrator assigns to execution specialists and an evolutionary engine. The engine selects parents, diagnoses errors, and creates descendants via code mutation, pipeline edits, and crossover, with each child evaluated on its own validation evidence. Population archives preserve useful alternatives, and compatible predictions compete in a validation‑gated ensemble stage. Search adapts through parent‑relative operator credit, session memory, and lessons retrieved across runs. The system achieved medal‑threshold artifacts on 19 of 22 tasks (86.36 %) with 11 gold, five silver, and three bronze outcomes across diverse modalities.
arXiv:2608. 05651v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly.
arXiv:2608. 08189v1 Announce Type: new Abstract: LLM-driven program discovery relies on rapid evaluator feedback, but many scientific and engineering tasks require high-fidelity simulations, hardware execution, or physical experiments, making each evaluation expensive.
arXiv:2606. 17546v1 Announce Type: new Abstract: Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, runtime state, and the model-tool interaction loop.