In the news
When Both Sides Adapt: The Challenges of Evaluating Interactive LLM Systems
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Research paper identifies how user adaptation in interactive LLM systems creates evaluation challenges, showing offline benchmarks and live A/B tests measure different quantities.
- Why it matters
- Matters for engineers deploying LLMs where users adapt their prompts based on model responses and iteratively change task difficulty.
- Watch out
- Offline benchmarks and live experiments can rank models differently, potentially leading to conflicting deployment decisions without measuring induced user behavior changes.
- llm
- prompt
- serving
- eval
The patterns behind this
- Agentic Context Engineering (Evolving Playbook)
- Eval-Driven Development (Agent CI)
- AG-UI Protocol (Agent-User Interaction)
Each one covers how the technique works, when it earns its cost, and where it breaks.
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