Dynamic In-Context Example Selection for Reliable Agentic Reasoning
A theoretically grounded method for agents to dynamically select optimal in-context examples during reasoning, boosting reliability across diverse tasks.
This paper cracks a core bottleneck in agent reliability: poor in-context examples. Instead of hand-curating prompts, the system queries a vector store of past trajectories, scores them via a calibrated meta-LLM, and injects the top-3 into the planner’s context. Results show consistent gains across planning, tool-use, and multi-step QA.
What changed. Dynamic example selection turns brittle prompting into a self-improving mechanism.
Why it matters. Reliability jumps from 60% to 90%+ on real benchmarks, closing the gap to classical systems.
Builder takeaway. Fork their GitHub repo and plug into your ReAct/VoI loop today. Paper