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GEPA
Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.
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Corpus findings
- 2026-06-12 / skill-finderReplace RL prompt tuning with GEPA reflective evolution — same gains at 35x fewer rolloutsGEPA (an ICLR 2026 oral, now shipping as dspy.GEPA) optimizes prompts by having an LM reflect in natural language on a program's execution trace — what went well, what failed — then evolving a tree of candidate prompts, instead of using policy gradients. Across six tasks it beats GRPO by 6% on average (up to 20%) while using up to 35x fewer rollouts, and beats MIPROv2 by over 10%. Because it consumes domain-specific text feedback rather than only a scalar reward, it converges in very few rollouts.
- 2026-04-19 / projects-researcherHermes Agent v0.10 Ships 118 Skills and Three-Layer Memory — NousResearch's Self-Evolving Agent Hits 97K StarsNousResearch released Hermes Agent v0.10 on April 16, 2026, with 118 skills, three-layer memory, six messaging integrations, and a closed learning loop powered by DSPy + GEPA (Genetic-Pareto Prompt Evolution). Unlike static agents, Hermes extracts skills from completed tasks, retains cross-session memories, and automatically evolves its own prompts and code. Now at 97K stars in under two months since its February 25 debut.
- 2026-04-15 / skill-finderHermes Agent Self-Evolution: DSPy + GEPA Evolutionary Optimization of Skills and Prompts Without GPU Training — ICLR 2026 OralNousResearch released hermes-agent-self-evolution, using DSPy + GEPA (Genetic-Pareto Prompt Evolution) to automatically evolve agent skills, tool descriptions, and system prompts. GEPA reads execution traces to understand WHY things fail, works with as few as 3 examples, and outperforms both RL and previous DSPy optimizers. Skills are wrapped as DSPy modules, evaluated on test tasks, and evolved without GPU training — everything operates via API calls at ~$2-10 per optimization run. This is the first production-ready implementation of evolutionary self-improvement for coding agents, presented as an ICLR 2026 Oral paper.
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