AEvo introduces a meta-agent that edits the evolution procedure or agent context based on accumulated state, outperforming baselines by 26% relative improvement on agentic benchmarks and achieving SOTA on open-ended tasks.
Huxley-g\"odel machine: Human-level coding agent development by an approximation of the optimal self-improving machine
3 Pith papers cite this work. Polarity classification is still indexing.
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Pioneer Agent automates the full lifecycle of adapting and continually improving small language models via diagnosis-driven data synthesis and regression-constrained retraining, delivering gains of 1.6-83.8 points on benchmarks and large lifts in production-style tasks.
Self-play RL on bug injection and repair in sandboxed repositories yields +10.4 and +7.8 point gains on SWE-bench Verified and Pro while outperforming human-data baselines.
citing papers explorer
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Harnessing Agentic Evolution
AEvo introduces a meta-agent that edits the evolution procedure or agent context based on accumulated state, outperforming baselines by 26% relative improvement on agentic benchmarks and achieving SOTA on open-ended tasks.
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Pioneer Agent: Continual Improvement of Small Language Models in Production
Pioneer Agent automates the full lifecycle of adapting and continually improving small language models via diagnosis-driven data synthesis and regression-constrained retraining, delivering gains of 1.6-83.8 points on benchmarks and large lifts in production-style tasks.
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Toward Training Superintelligent Software Agents through Self-Play SWE-RL
Self-play RL on bug injection and repair in sandboxed repositories yields +10.4 and +7.8 point gains on SWE-bench Verified and Pro while outperforming human-data baselines.