Benchmark RadarAI BENCHMARK PROFILE
RECAP
RECAP evaluates continual-learning phenomena in prompt-based LLM adaptation under evolving constraints, using a proactive adapt-then-test protocol with constraint-level metrics for forgetting, regression, and forward transfer.
- Released
- 2026-06-04
- Readiness
- Paper only
- Primary field
- General AI
Why it matters
Current benchmarks assume static constraints or reactive feedback, while real deployments often require proactive compliance. RECAP exposes performance gaps in existing prompt optimization methods under proactive adaptation, guiding development of more robust methods for evolving deployment needs.
Motivation
Production agentic systems routinely face evolving constraints and must comply from the very next interaction.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.