flex-continuous-agent-evolutionlisted

# Evolve Agents Through Structured Experience Accumulation Deployed language model agents are typically static—once trained, they don't improve from real-world interactions. FLEX solves this through gradient-free continuous learning: agents maintain a structured experience library recording successes, failures, and their contexts. During subsequent interactions, the agent retrieves and reflects on relevant past experiences, incorporating these lessons into prompting without retraining. The approach demonstrates substantial gains: 23% improvement on mathematical reasoning (AIME25), 10% on chemical synthesis, 14% on protein engineering—all from self-refinement during deployment, not additional training. ## Core Concept FLEX treats deployed agent improvement as a problem of structured experience management rather than parameter optimization. The system maintains three components: 1. **Experience Library**: Structured records of past interactions (state, action, outcome, reflection) 2. **Retrieval Mechanism**: Finding relevant precedents for current problems 3. **Self-Reflection**: Agents analyze successes/failures and distill lessons as prompting context This approach is particularly powerful because it requires no gradient computation, model retraining, or API calls to external LLMs during learning—only structured reflection during inference. ## Architecture Overview - **Experience Capture Module**: Records interactions (problem, solution attempt, outcome, contextual fa